diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index 0d876e6afc79f09ef50a008750787aaa4a5cdf07..72edbcbb8d45ac079391155f8fa0a6de04e1704d 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -25,6 +25,11 @@ from ResultAnalysis import resultAnalysis
from Versions import testVersions
import MonoviewClassifiers
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from numpy.random import randint
+import random
+
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
@@ -104,7 +109,7 @@ def initBenchmark(args):
allMumboAlgos = [name for _, name, isPackage in
pkgutil.iter_modules(['Multiview/Mumbo/Classifiers'])
if not isPackage and not name in ["SubSampling", "ModifiedMulticlass", "Kover"]]
- allMultiviewAlgos = {"Fusion": allFusionAlgos, "Mumbo": allMumboAlgos}
+ allMultiviewAlgos = {"Fusion": allFusionAlgos}#, "Mumbo": allMumboAlgos}
benchmark = {"Monoview": allMonoviewAlgos, "Multiview": allMultiviewAlgos}
if "Multiview" in args.CL_type.strip(":"):
@@ -290,6 +295,24 @@ def initMultiviewArguments(args, benchmark, views, viewsIndices, accuracies, cla
pass
return argumentDictionaries
+
+def analyzeLabels(labelsArrays, realLabels, classifiersNames):
+ nbClassifiers = len(classifiersNames)
+ nbExamples = realLabels.shape[0]
+ nbIter = nbExamples/nbClassifiers
+ data = np.zeros((nbExamples, nbClassifiers*nbIter))
+ tempData = np.array([labelsArray == realLabels for labelsArray in labelsArrays]).astype(int)
+ for classifierIndex in range(nbClassifiers):
+ for iterIndex in range(nbIter):
+ data[:,classifierIndex*nbIter+iterIndex] = tempData[:,classifierIndex]
+ fig, ax = plt.subplots()
+ cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
+ ax.set_title('Gaussian noise with vertical colorbar')
+ cbar = fig.colorbar(cax, ticks=[0, 1])
+ cbar.ax.set_yticklabels(['Wrong', ' Right'])
+ fig.savefig("test.png")
+
+
parser = argparse.ArgumentParser(
description='This file is used to benchmark the accuracies fo multiple classification algorithm on multiview data.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -480,6 +503,7 @@ if nbCores>1:
accuracies = [[result[1][1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
classifiersNames = [[result[1][0] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
classifiersConfigs = [[result[1][1][:-1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
+
else:
resultsMonoview+=([ExecMonoview(DATASET.get("View"+str(arguments["viewIndex"])),
DATASET.get("Labels").value, args.name, labelsNames,
@@ -513,7 +537,9 @@ if nbCores>1:
logging.debug("Start:\t Deleting "+str(nbCores)+" temporary datasets for multiprocessing")
datasetFiles = DB.deleteHDF5(args.pathF, args.name, nbCores)
logging.debug("Start:\t Deleting datasets for multiprocessing")
-
+labels = np.array([resultMonoview[1][3] for resultMonoview in resultsMonoview]+[resultMultiview[3] for resultMultiview in resultsMultiview]).transpose()
+trueLabels = DATASET.get("Labels").value
+analyzeLabels(labels, trueLabels, ["" in range(labels.shape[1])])
times = [dataBaseTime, monoviewTime, multiviewTime]
# times=[]
results = (resultsMonoview, resultsMultiview)
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index 37619e1f6502a5ca0410a741e1ec4e86b2402198..5c9c9ece7e677b5b0943572a2c87fe0c18f8a76c 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -103,6 +103,7 @@ def ExecMonoview(X, Y, name, labelsNames, learningRate, nbFolds, nbCores, databa
y_train_preds.append(y_train_pred)
y_tests.append(y_test)
y_test_preds.append(y_test_pred)
+ full_labels = cl_res.predict(X)
logging.debug("Done:\t Predicting")
t_end = time.time() - t_start
logging.debug("Info:\t Time for training and predicting: " + str(t_end) + "[s]")
@@ -138,7 +139,7 @@ def ExecMonoview(X, Y, name, labelsNames, learningRate, nbFolds, nbCores, databa
logging.info("Done:\t Result Analysis")
viewIndex = args["viewIndex"]
- return viewIndex, [CL_type, cl_desc+[feat], metricsScores]
+ return viewIndex, [CL_type, cl_desc+[feat], metricsScores, full_labels]
# # Classification Report with Precision, Recall, F1 , Support
# logging.debug("Info:\t Classification report:")
# filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-Report"
diff --git a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
index 434f8091938c47ef621455d3305c70b7507832d1..37e02981a723e109dd5bc9b42e919e502247bf20 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
@@ -95,6 +95,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
classifier.fit_hdf5(DATASET, trainIndices=learningIndices, viewsIndices=viewsIndices)
trainLabels = classifier.predict_hdf5(DATASET, usedIndices=learningIndices, viewsIndices=viewsIndices)
testLabels = classifier.predict_hdf5(DATASET, usedIndices=validationIndices, viewsIndices=viewsIndices)
+ fullLabels = classifier.predict_hdf5(DATASET, viewsIndices=viewsIndices)
trainLabelsIterations.append(trainLabels)
testLabelsIterations.append(testLabels)
ivalidationIndices.append(validationIndices)
@@ -144,7 +145,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
imagesAnalysis[imageName].savefig(outputFileName + imageName + '.png')
logging.info("Done:\t Result Analysis")
- return CL_type, classificationKWARGS, metricsScores
+ return CL_type, classificationKWARGS, metricsScores, fullLabels
if __name__=='__main__':
diff --git a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
index 50755fc711ccedf1461d05d2c3437a86146aa3bc..874452b2a8d54aca9d0a0cf468c58b33ac097dec 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
@@ -10,6 +10,7 @@ import Methods.LateFusion
import Metrics
+
# Author-Info
__author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
@@ -20,6 +21,7 @@ def error(testLabels, computedLabels):
return float(error) * 100 / len(computedLabels)
+
def printMetricScore(metricScores, metrics):
metricScoreString = "\n\n"
for metric in metrics:
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index 99f4b271e235c37866da26648cb020b0f3fd83fd..22fee825bea948ce345d30ef58d2038d94d123a0 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -8,9 +8,11 @@ import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
+from matplotlib import cm
#Import own Modules
import Metrics
+from utils.Transformations import signLabels
# Author-Info
__author__ = "Baptiste Bauvin"
@@ -26,8 +28,10 @@ def autolabel(rects, ax):
def resultAnalysis(benchmark, results, name, times, metrics):
+ mono, multi = results
+ labelsByView = np.array([res[0][3] for res in mono]+[res[3] for res in multi])
+ makeColorMap(labelsByView, name)
for metric in metrics:
- mono, multi = results
names = [res[1][0]+"-"+res[1][1][-1] for res in mono]
names+=[type_ for type_, a, b in multi if type_ != "Fusion"]
names+=[ "Late-"+str(a["fusionMethod"]) for type_, a, b in multi if type_ == "Fusion" and a["fusionType"] != "EarlyFusion"]
@@ -63,3 +67,21 @@ def resultAnalysis(benchmark, results, name, times, metrics):
logging.info("Extraction time : "+str(times[0])+"s, Monoview time : "+str(times[1])+"s, Multiview Time : "+str(times[2])+"s")
+def makeColorMap(labelsByView, name):
+ nb_view = labelsByView.shape[1]
+ nbExamples = labelsByView.shape[0]
+ # Make plot with vertical (default) colorbar
+ fig, ax = plt.subplots()
+ data = np.zeros((nbExamples,nbExamples), dtype=int)
+ datap = np.array([signLabels(labels) for labels in labelsByView])
+ nbRepet = nbExamples/nb_view
+ for j in range(nb_view):
+ for i in range(nbRepet):
+ data[:, j*50+i] = datap[:, j]
+
+ cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
+ ax.set_title('Labels per view')
+ cbar = fig.colorbar(cax, ticks=[0, 1])
+ cbar.ax.set_yticklabels(['-1', ' 1']) # vertically oriented colorbar
+ plt.show()
+ fig.savefig("Results/"+time.strftime("%Y%m%d-%H%M%S")+"-"+name+"-labels.png")
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-075939-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-075939-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-075959-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-075959-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080453-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-080453-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 1c28900d9c19d9f4a5e59bb3cae87d3894d038a3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080453-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1472 +0,0 @@
-2016-09-06 08:04:53,806 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:04:53,807 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000152125 Gbytes /!\
-2016-09-06 08:04:58,817 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:04:58,819 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:04:58,908 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:04:58,909 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:04:58,909 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:04:58,911 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:04:58,912 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:04:58,912 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:04:59,094 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:04:59,094 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:04:59,094 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:04:59,094 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:04:59,094 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:04:59,094 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:04:59,095 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:04:59,095 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:04:59,204 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:04:59,205 DEBUG: Start: Training
-2016-09-06 08:04:59,209 DEBUG: Info: Time for Training: 0.297763824463[s]
-2016-09-06 08:04:59,209 DEBUG: Done: Training
-2016-09-06 08:04:59,209 DEBUG: Start: Predicting
-2016-09-06 08:04:59,246 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:04:59,246 DEBUG: Start: Training
-2016-09-06 08:04:59,253 DEBUG: Info: Time for Training: 0.345137834549[s]
-2016-09-06 08:04:59,253 DEBUG: Done: Training
-2016-09-06 08:04:59,253 DEBUG: Start: Predicting
-2016-09-06 08:04:59,427 DEBUG: Done: Predicting
-2016-09-06 08:04:59,427 DEBUG: Done: Predicting
-2016-09-06 08:04:59,428 DEBUG: Start: Getting Results
-2016-09-06 08:04:59,428 DEBUG: Start: Getting Results
-2016-09-06 08:04:59,429 DEBUG: Done: Getting Results
-2016-09-06 08:04:59,429 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:04:59,430 INFO: Done: Result Analysis
-2016-09-06 08:04:59,430 DEBUG: Done: Getting Results
-2016-09-06 08:04:59,431 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:04:59,431 INFO: Done: Result Analysis
-2016-09-06 08:04:59,576 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:04:59,577 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:04:59,577 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:04:59,578 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:04:59,578 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:04:59,578 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:04:59,578 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:04:59,581 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:04:59,581 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:04:59,581 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:04:59,582 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:04:59,582 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:04:59,582 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:04:59,582 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:04:59,670 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:04:59,670 DEBUG: Start: Training
-2016-09-06 08:04:59,671 DEBUG: Info: Time for Training: 0.0952470302582[s]
-2016-09-06 08:04:59,671 DEBUG: Done: Training
-2016-09-06 08:04:59,671 DEBUG: Start: Predicting
-2016-09-06 08:04:59,682 DEBUG: Done: Predicting
-2016-09-06 08:04:59,683 DEBUG: Start: Getting Results
-2016-09-06 08:04:59,684 DEBUG: Done: Getting Results
-2016-09-06 08:04:59,684 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:04:59,685 INFO: Done: Result Analysis
-2016-09-06 08:05:00,340 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:00,340 DEBUG: Start: Training
-2016-09-06 08:05:00,410 DEBUG: Info: Time for Training: 0.829602956772[s]
-2016-09-06 08:05:00,410 DEBUG: Done: Training
-2016-09-06 08:05:00,410 DEBUG: Start: Predicting
-2016-09-06 08:05:00,418 DEBUG: Done: Predicting
-2016-09-06 08:05:00,418 DEBUG: Start: Getting Results
-2016-09-06 08:05:00,419 DEBUG: Done: Getting Results
-2016-09-06 08:05:00,419 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:05:00,419 INFO: Done: Result Analysis
-2016-09-06 08:05:00,524 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:00,524 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:05:00,524 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:00,524 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:00,524 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:05:00,524 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:00,525 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:05:00,525 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:05:00,525 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:05:00,525 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:05:00,525 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:00,525 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:00,525 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:00,525 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:00,733 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:00,733 DEBUG: Start: Training
-2016-09-06 08:05:00,759 DEBUG: Info: Time for Training: 0.235524892807[s]
-2016-09-06 08:05:00,759 DEBUG: Done: Training
-2016-09-06 08:05:00,759 DEBUG: Start: Predicting
-2016-09-06 08:05:00,766 DEBUG: Done: Predicting
-2016-09-06 08:05:00,766 DEBUG: Start: Getting Results
-2016-09-06 08:05:00,768 DEBUG: Done: Getting Results
-2016-09-06 08:05:00,768 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.428571428571
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.428571428571
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:05:00,768 INFO: Done: Result Analysis
-2016-09-06 08:05:01,477 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:01,477 DEBUG: Start: Training
-2016-09-06 08:05:01,478 DEBUG: Info: Time for Training: 0.954334020615[s]
-2016-09-06 08:05:01,478 DEBUG: Done: Training
-2016-09-06 08:05:01,478 DEBUG: Start: Predicting
-2016-09-06 08:05:01,564 DEBUG: Done: Predicting
-2016-09-06 08:05:01,564 DEBUG: Start: Getting Results
-2016-09-06 08:05:01,565 DEBUG: Done: Getting Results
-2016-09-06 08:05:01,566 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:05:01,566 INFO: Done: Result Analysis
-2016-09-06 08:05:01,694 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:01,694 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:01,694 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:05:01,694 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:05:01,694 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:01,694 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:01,695 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:05:01,695 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:05:01,695 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:05:01,696 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:01,696 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:01,695 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:05:01,698 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:01,698 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:01,818 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:01,818 DEBUG: Start: Training
-2016-09-06 08:05:01,845 DEBUG: Info: Time for Training: 0.151879787445[s]
-2016-09-06 08:05:01,845 DEBUG: Done: Training
-2016-09-06 08:05:01,845 DEBUG: Start: Predicting
-2016-09-06 08:05:01,855 DEBUG: Done: Predicting
-2016-09-06 08:05:01,855 DEBUG: Start: Getting Results
-2016-09-06 08:05:01,857 DEBUG: Done: Getting Results
-2016-09-06 08:05:01,857 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:05:01,857 INFO: Done: Result Analysis
-2016-09-06 08:05:01,862 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:01,862 DEBUG: Start: Training
-2016-09-06 08:05:01,887 DEBUG: Info: Time for Training: 0.19349193573[s]
-2016-09-06 08:05:01,887 DEBUG: Done: Training
-2016-09-06 08:05:01,887 DEBUG: Start: Predicting
-2016-09-06 08:05:01,891 DEBUG: Done: Predicting
-2016-09-06 08:05:01,892 DEBUG: Start: Getting Results
-2016-09-06 08:05:01,893 DEBUG: Done: Getting Results
-2016-09-06 08:05:01,893 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6464
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:05:01,893 INFO: Done: Result Analysis
-2016-09-06 08:05:02,058 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:02,059 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:05:02,059 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:02,059 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:02,060 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:05:02,060 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:02,060 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:02,060 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:02,060 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:02,060 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:02,060 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:02,060 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:02,061 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:02,061 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:02,164 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:02,165 DEBUG: Start: Training
-2016-09-06 08:05:02,168 DEBUG: Info: Time for Training: 0.10915017128[s]
-2016-09-06 08:05:02,168 DEBUG: Done: Training
-2016-09-06 08:05:02,168 DEBUG: Start: Predicting
-2016-09-06 08:05:02,172 DEBUG: Done: Predicting
-2016-09-06 08:05:02,172 DEBUG: Start: Getting Results
-2016-09-06 08:05:02,173 DEBUG: Done: Getting Results
-2016-09-06 08:05:02,173 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:05:02,173 INFO: Done: Result Analysis
-2016-09-06 08:05:02,219 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:02,220 DEBUG: Start: Training
-2016-09-06 08:05:02,229 DEBUG: Info: Time for Training: 0.171218156815[s]
-2016-09-06 08:05:02,229 DEBUG: Done: Training
-2016-09-06 08:05:02,229 DEBUG: Start: Predicting
-2016-09-06 08:05:02,234 DEBUG: Done: Predicting
-2016-09-06 08:05:02,234 DEBUG: Start: Getting Results
-2016-09-06 08:05:02,237 DEBUG: Done: Getting Results
-2016-09-06 08:05:02,237 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:05:02,238 INFO: Done: Result Analysis
-2016-09-06 08:05:02,318 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:02,318 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:02,318 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:05:02,318 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:05:02,319 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:02,319 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:02,319 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:02,319 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:02,320 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:02,320 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:02,320 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:02,320 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:02,320 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:02,320 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:02,415 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:02,415 DEBUG: Start: Training
-2016-09-06 08:05:02,416 DEBUG: Info: Time for Training: 0.0986800193787[s]
-2016-09-06 08:05:02,416 DEBUG: Done: Training
-2016-09-06 08:05:02,416 DEBUG: Start: Predicting
-2016-09-06 08:05:02,427 DEBUG: Done: Predicting
-2016-09-06 08:05:02,427 DEBUG: Start: Getting Results
-2016-09-06 08:05:02,429 DEBUG: Done: Getting Results
-2016-09-06 08:05:02,429 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:05:02,429 INFO: Done: Result Analysis
-2016-09-06 08:05:03,195 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:03,196 DEBUG: Start: Training
-2016-09-06 08:05:03,273 DEBUG: Info: Time for Training: 0.955595016479[s]
-2016-09-06 08:05:03,273 DEBUG: Done: Training
-2016-09-06 08:05:03,273 DEBUG: Start: Predicting
-2016-09-06 08:05:03,282 DEBUG: Done: Predicting
-2016-09-06 08:05:03,282 DEBUG: Start: Getting Results
-2016-09-06 08:05:03,283 DEBUG: Done: Getting Results
-2016-09-06 08:05:03,283 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,283 INFO: Done: Result Analysis
-2016-09-06 08:05:03,393 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,393 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,393 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:05:03,393 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:05:03,393 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:03,393 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:03,394 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:03,394 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:03,394 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:03,394 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,395 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:03,395 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:03,395 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:03,395 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,506 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:03,507 DEBUG: Start: Training
-2016-09-06 08:05:03,508 DEBUG: Info: Time for Training: 0.115790128708[s]
-2016-09-06 08:05:03,508 DEBUG: Done: Training
-2016-09-06 08:05:03,508 DEBUG: Start: Predicting
-2016-09-06 08:05:03,538 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:03,538 DEBUG: Start: Training
-2016-09-06 08:05:03,549 DEBUG: Done: Predicting
-2016-09-06 08:05:03,549 DEBUG: Start: Getting Results
-2016-09-06 08:05:03,551 DEBUG: Done: Getting Results
-2016-09-06 08:05:03,551 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,551 INFO: Done: Result Analysis
-2016-09-06 08:05:03,570 DEBUG: Info: Time for Training: 0.178539991379[s]
-2016-09-06 08:05:03,571 DEBUG: Done: Training
-2016-09-06 08:05:03,571 DEBUG: Start: Predicting
-2016-09-06 08:05:03,577 DEBUG: Done: Predicting
-2016-09-06 08:05:03,577 DEBUG: Start: Getting Results
-2016-09-06 08:05:03,578 DEBUG: Done: Getting Results
-2016-09-06 08:05:03,579 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,579 INFO: Done: Result Analysis
-2016-09-06 08:05:03,664 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,664 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,664 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:05:03,664 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:05:03,664 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:03,664 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:03,665 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:03,665 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:05:03,665 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:03,665 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:05:03,665 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:03,665 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:03,666 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,666 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,764 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:03,764 DEBUG: Start: Training
-2016-09-06 08:05:03,783 DEBUG: Info: Time for Training: 0.12015581131[s]
-2016-09-06 08:05:03,784 DEBUG: Done: Training
-2016-09-06 08:05:03,784 DEBUG: Start: Predicting
-2016-09-06 08:05:03,790 DEBUG: Done: Predicting
-2016-09-06 08:05:03,790 DEBUG: Start: Getting Results
-2016-09-06 08:05:03,791 DEBUG: Done: Getting Results
-2016-09-06 08:05:03,791 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,792 INFO: Done: Result Analysis
-2016-09-06 08:05:03,818 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:03,818 DEBUG: Start: Training
-2016-09-06 08:05:03,846 DEBUG: Info: Time for Training: 0.183187961578[s]
-2016-09-06 08:05:03,847 DEBUG: Done: Training
-2016-09-06 08:05:03,847 DEBUG: Start: Predicting
-2016-09-06 08:05:03,851 DEBUG: Done: Predicting
-2016-09-06 08:05:03,851 DEBUG: Start: Getting Results
-2016-09-06 08:05:03,852 DEBUG: Done: Getting Results
-2016-09-06 08:05:03,853 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6464
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,853 INFO: Done: Result Analysis
-2016-09-06 08:05:03,911 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,911 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:05:03,911 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:03,912 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:03,912 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:03,912 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:03,912 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,913 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:03,913 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:05:03,913 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:03,914 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:03,914 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:03,914 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:03,914 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:03,985 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:03,985 DEBUG: Start: Training
-2016-09-06 08:05:03,987 DEBUG: Info: Time for Training: 0.0755009651184[s]
-2016-09-06 08:05:03,988 DEBUG: Done: Training
-2016-09-06 08:05:03,988 DEBUG: Start: Predicting
-2016-09-06 08:05:03,990 DEBUG: Done: Predicting
-2016-09-06 08:05:03,990 DEBUG: Start: Getting Results
-2016-09-06 08:05:03,991 DEBUG: Done: Getting Results
-2016-09-06 08:05:03,991 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:05:03,992 INFO: Done: Result Analysis
-2016-09-06 08:05:04,007 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:04,007 DEBUG: Start: Training
-2016-09-06 08:05:04,011 DEBUG: Info: Time for Training: 0.100708961487[s]
-2016-09-06 08:05:04,011 DEBUG: Done: Training
-2016-09-06 08:05:04,011 DEBUG: Start: Predicting
-2016-09-06 08:05:04,014 DEBUG: Done: Predicting
-2016-09-06 08:05:04,015 DEBUG: Start: Getting Results
-2016-09-06 08:05:04,016 DEBUG: Done: Getting Results
-2016-09-06 08:05:04,016 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:05:04,017 INFO: Done: Result Analysis
-2016-09-06 08:05:04,155 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:04,155 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:04,155 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:05:04,155 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:05:04,155 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:04,155 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:04,156 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:04,156 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:04,156 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:04,156 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:04,156 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:04,156 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:04,156 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:04,156 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:04,209 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:04,209 DEBUG: Start: Training
-2016-09-06 08:05:04,210 DEBUG: Info: Time for Training: 0.0557448863983[s]
-2016-09-06 08:05:04,210 DEBUG: Done: Training
-2016-09-06 08:05:04,210 DEBUG: Start: Predicting
-2016-09-06 08:05:04,216 DEBUG: Done: Predicting
-2016-09-06 08:05:04,217 DEBUG: Start: Getting Results
-2016-09-06 08:05:04,217 DEBUG: Done: Getting Results
-2016-09-06 08:05:04,218 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:05:04,218 INFO: Done: Result Analysis
-2016-09-06 08:05:04,803 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:04,803 DEBUG: Start: Training
-2016-09-06 08:05:04,871 DEBUG: Info: Time for Training: 0.716722011566[s]
-2016-09-06 08:05:04,871 DEBUG: Done: Training
-2016-09-06 08:05:04,871 DEBUG: Start: Predicting
-2016-09-06 08:05:04,879 DEBUG: Done: Predicting
-2016-09-06 08:05:04,879 DEBUG: Start: Getting Results
-2016-09-06 08:05:04,880 DEBUG: Done: Getting Results
-2016-09-06 08:05:04,880 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:05:04,880 INFO: Done: Result Analysis
-2016-09-06 08:05:05,007 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,007 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,007 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:05:05,007 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:05:05,007 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:05,007 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:05,008 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:05,008 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:05,008 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:05,008 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:05,009 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:05,009 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:05,009 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,009 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,082 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:05,082 DEBUG: Start: Training
-2016-09-06 08:05:05,083 DEBUG: Info: Time for Training: 0.0776901245117[s]
-2016-09-06 08:05:05,083 DEBUG: Done: Training
-2016-09-06 08:05:05,083 DEBUG: Start: Predicting
-2016-09-06 08:05:05,094 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:05,094 DEBUG: Start: Training
-2016-09-06 08:05:05,098 DEBUG: Done: Predicting
-2016-09-06 08:05:05,098 DEBUG: Start: Getting Results
-2016-09-06 08:05:05,100 DEBUG: Done: Getting Results
-2016-09-06 08:05:05,100 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,100 INFO: Done: Result Analysis
-2016-09-06 08:05:05,119 DEBUG: Info: Time for Training: 0.113911151886[s]
-2016-09-06 08:05:05,120 DEBUG: Done: Training
-2016-09-06 08:05:05,120 DEBUG: Start: Predicting
-2016-09-06 08:05:05,123 DEBUG: Done: Predicting
-2016-09-06 08:05:05,123 DEBUG: Start: Getting Results
-2016-09-06 08:05:05,124 DEBUG: Done: Getting Results
-2016-09-06 08:05:05,124 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,124 INFO: Done: Result Analysis
-2016-09-06 08:05:05,259 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,259 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,260 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:05:05,260 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:05:05,260 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:05,260 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:05,261 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:05,261 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:05:05,261 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:05,261 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:05:05,261 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:05,261 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:05,262 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,262 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,378 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:05,378 DEBUG: Start: Training
-2016-09-06 08:05:05,392 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:05,392 DEBUG: Start: Training
-2016-09-06 08:05:05,401 DEBUG: Info: Time for Training: 0.142508983612[s]
-2016-09-06 08:05:05,401 DEBUG: Done: Training
-2016-09-06 08:05:05,401 DEBUG: Start: Predicting
-2016-09-06 08:05:05,408 DEBUG: Done: Predicting
-2016-09-06 08:05:05,408 DEBUG: Start: Getting Results
-2016-09-06 08:05:05,410 DEBUG: Done: Getting Results
-2016-09-06 08:05:05,410 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,410 INFO: Done: Result Analysis
-2016-09-06 08:05:05,417 DEBUG: Info: Time for Training: 0.158962965012[s]
-2016-09-06 08:05:05,417 DEBUG: Done: Training
-2016-09-06 08:05:05,418 DEBUG: Start: Predicting
-2016-09-06 08:05:05,421 DEBUG: Done: Predicting
-2016-09-06 08:05:05,421 DEBUG: Start: Getting Results
-2016-09-06 08:05:05,422 DEBUG: Done: Getting Results
-2016-09-06 08:05:05,422 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,423 INFO: Done: Result Analysis
-2016-09-06 08:05:05,499 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,500 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:05:05,500 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:05,500 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,500 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:05,500 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:05,500 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:05:05,500 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:05,500 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:05,500 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,501 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:05,501 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:05,501 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:05,501 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,564 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:05,564 DEBUG: Start: Training
-2016-09-06 08:05:05,566 DEBUG: Info: Time for Training: 0.0668630599976[s]
-2016-09-06 08:05:05,566 DEBUG: Done: Training
-2016-09-06 08:05:05,566 DEBUG: Start: Predicting
-2016-09-06 08:05:05,568 DEBUG: Done: Predicting
-2016-09-06 08:05:05,568 DEBUG: Start: Getting Results
-2016-09-06 08:05:05,569 DEBUG: Done: Getting Results
-2016-09-06 08:05:05,569 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,570 INFO: Done: Result Analysis
-2016-09-06 08:05:05,593 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:05,593 DEBUG: Start: Training
-2016-09-06 08:05:05,597 DEBUG: Info: Time for Training: 0.0977339744568[s]
-2016-09-06 08:05:05,597 DEBUG: Done: Training
-2016-09-06 08:05:05,597 DEBUG: Start: Predicting
-2016-09-06 08:05:05,600 DEBUG: Done: Predicting
-2016-09-06 08:05:05,600 DEBUG: Start: Getting Results
-2016-09-06 08:05:05,602 DEBUG: Done: Getting Results
-2016-09-06 08:05:05,602 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,603 INFO: Done: Result Analysis
-2016-09-06 08:05:05,747 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,747 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:05,747 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:05:05,747 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:05:05,747 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:05,747 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:05,748 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:05,748 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:05,748 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:05,748 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:05,748 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:05,748 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:05,748 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,748 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:05,831 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:05,831 DEBUG: Start: Training
-2016-09-06 08:05:05,832 DEBUG: Info: Time for Training: 0.0860028266907[s]
-2016-09-06 08:05:05,832 DEBUG: Done: Training
-2016-09-06 08:05:05,832 DEBUG: Start: Predicting
-2016-09-06 08:05:05,842 DEBUG: Done: Predicting
-2016-09-06 08:05:05,842 DEBUG: Start: Getting Results
-2016-09-06 08:05:05,843 DEBUG: Done: Getting Results
-2016-09-06 08:05:05,843 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:05:05,844 INFO: Done: Result Analysis
-2016-09-06 08:05:06,432 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:06,432 DEBUG: Start: Training
-2016-09-06 08:05:06,500 DEBUG: Info: Time for Training: 0.753455877304[s]
-2016-09-06 08:05:06,500 DEBUG: Done: Training
-2016-09-06 08:05:06,500 DEBUG: Start: Predicting
-2016-09-06 08:05:06,507 DEBUG: Done: Predicting
-2016-09-06 08:05:06,508 DEBUG: Start: Getting Results
-2016-09-06 08:05:06,508 DEBUG: Done: Getting Results
-2016-09-06 08:05:06,509 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:05:06,509 INFO: Done: Result Analysis
-2016-09-06 08:05:06,597 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:06,597 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:05:06,597 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:05:06,597 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:05:06,597 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:06,597 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:05:06,598 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:06,598 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:05:06,598 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:06,598 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:05:06,598 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:06,598 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:05:06,598 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:06,598 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:05:06,693 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:06,693 DEBUG: Start: Training
-2016-09-06 08:05:06,694 DEBUG: Info: Time for Training: 0.0977959632874[s]
-2016-09-06 08:05:06,694 DEBUG: Done: Training
-2016-09-06 08:05:06,694 DEBUG: Start: Predicting
-2016-09-06 08:05:06,704 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:05:06,704 DEBUG: Start: Training
-2016-09-06 08:05:06,718 DEBUG: Done: Predicting
-2016-09-06 08:05:06,718 DEBUG: Start: Getting Results
-2016-09-06 08:05:06,719 DEBUG: Done: Getting Results
-2016-09-06 08:05:06,719 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:05:06,719 INFO: Done: Result Analysis
-2016-09-06 08:05:06,724 DEBUG: Info: Time for Training: 0.127656936646[s]
-2016-09-06 08:05:06,724 DEBUG: Done: Training
-2016-09-06 08:05:06,724 DEBUG: Start: Predicting
-2016-09-06 08:05:06,727 DEBUG: Done: Predicting
-2016-09-06 08:05:06,727 DEBUG: Start: Getting Results
-2016-09-06 08:05:06,728 DEBUG: Done: Getting Results
-2016-09-06 08:05:06,728 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:05:06,728 INFO: Done: Result Analysis
-2016-09-06 08:05:06,989 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:05:06,989 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:05:06,990 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:05:06,990 INFO: Info: Shape of View0 :(300, 17)
-2016-09-06 08:05:06,990 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:05:06,990 INFO: Info: Shape of View1 :(300, 14)
-2016-09-06 08:05:06,990 INFO: Info: Shape of View0 :(300, 17)
-2016-09-06 08:05:06,991 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 08:05:06,991 INFO: Info: Shape of View1 :(300, 14)
-2016-09-06 08:05:06,991 INFO: Info: Shape of View3 :(300, 15)
-2016-09-06 08:05:06,991 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 08:05:06,991 INFO: Done: Read Database Files
-2016-09-06 08:05:06,991 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:05:06,991 INFO: Info: Shape of View3 :(300, 15)
-2016-09-06 08:05:06,992 INFO: Done: Read Database Files
-2016-09-06 08:05:06,992 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:05:06,995 INFO: Done: Determine validation split
-2016-09-06 08:05:06,995 INFO: Start: Determine 5 folds
-2016-09-06 08:05:06,995 INFO: Done: Determine validation split
-2016-09-06 08:05:06,995 INFO: Start: Determine 5 folds
-2016-09-06 08:05:07,002 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:05:07,002 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:05:07,002 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:05:07,002 INFO: Done: Determine folds
-2016-09-06 08:05:07,002 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:05:07,003 INFO: Start: Classification
-2016-09-06 08:05:07,003 INFO: Start: Fold number 1
-2016-09-06 08:05:07,004 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:05:07,004 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:05:07,004 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:05:07,004 INFO: Done: Determine folds
-2016-09-06 08:05:07,004 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:05:07,005 INFO: Start: Classification
-2016-09-06 08:05:07,005 INFO: Start: Fold number 1
-2016-09-06 08:05:07,104 INFO: Start: Classification
-2016-09-06 08:05:07,188 INFO: Done: Fold number 1
-2016-09-06 08:05:07,188 INFO: Start: Fold number 2
-2016-09-06 08:05:07,286 INFO: Start: Classification
-2016-09-06 08:05:07,370 INFO: Done: Fold number 2
-2016-09-06 08:05:07,371 INFO: Start: Fold number 3
-2016-09-06 08:05:07,470 INFO: Start: Classification
-2016-09-06 08:05:07,555 INFO: Done: Fold number 3
-2016-09-06 08:05:07,556 INFO: Start: Fold number 4
-2016-09-06 08:05:07,655 INFO: Start: Classification
-2016-09-06 08:05:07,739 INFO: Done: Fold number 4
-2016-09-06 08:05:07,739 INFO: Start: Fold number 5
-2016-09-06 08:05:07,838 INFO: Start: Classification
-2016-09-06 08:05:07,923 INFO: Done: Fold number 5
-2016-09-06 08:05:07,924 INFO: Done: Classification
-2016-09-06 08:05:07,924 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:05:07,924 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:05:07,929 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 45.2380952381
- -On Validation : 83.8202247191
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 31
- - Random Forest with num_esimators : 26, max_depth : 23
- - K nearest Neighbors with n_neighbors: 31
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:05:07,929 INFO: Done: Result Analysis
-2016-09-06 08:05:08,912 DEBUG: Start: Iteration 1
-2016-09-06 08:05:08,920 DEBUG: View 0 : 0.513812154696
-2016-09-06 08:05:08,927 DEBUG: View 1 : 0.569060773481
-2016-09-06 08:05:08,934 DEBUG: View 2 : 0.497237569061
-2016-09-06 08:05:08,941 DEBUG: View 3 : 0.524861878453
-2016-09-06 08:05:08,984 DEBUG: Best view : View3
-2016-09-06 08:05:09,067 DEBUG: Start: Iteration 2
-2016-09-06 08:05:09,074 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:09,081 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:09,089 DEBUG: View 2 : 0.674033149171
-2016-09-06 08:05:09,096 DEBUG: View 3 : 0.690607734807
-2016-09-06 08:05:09,145 DEBUG: Best view : View0
-2016-09-06 08:05:09,295 DEBUG: Start: Iteration 3
-2016-09-06 08:05:09,303 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:09,310 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:09,317 DEBUG: View 2 : 0.674033149171
-2016-09-06 08:05:09,325 DEBUG: View 3 : 0.690607734807
-2016-09-06 08:05:09,382 DEBUG: Best view : View0
-2016-09-06 08:05:09,602 DEBUG: Start: Iteration 4
-2016-09-06 08:05:09,609 DEBUG: View 0 : 0.674033149171
-2016-09-06 08:05:09,617 DEBUG: View 1 : 0.602209944751
-2016-09-06 08:05:09,624 DEBUG: View 2 : 0.668508287293
-2016-09-06 08:05:09,632 DEBUG: View 3 : 0.718232044199
-2016-09-06 08:05:09,690 DEBUG: Best view : View3
-2016-09-06 08:05:09,980 DEBUG: Start: Iteration 5
-2016-09-06 08:05:09,987 DEBUG: View 0 : 0.674033149171
-2016-09-06 08:05:09,995 DEBUG: View 1 : 0.602209944751
-2016-09-06 08:05:10,002 DEBUG: View 2 : 0.668508287293
-2016-09-06 08:05:10,009 DEBUG: View 3 : 0.718232044199
-2016-09-06 08:05:10,070 DEBUG: Best view : View3
-2016-09-06 08:05:10,426 DEBUG: Start: Iteration 6
-2016-09-06 08:05:10,434 DEBUG: View 0 : 0.701657458564
-2016-09-06 08:05:10,441 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:10,448 DEBUG: View 2 : 0.67955801105
-2016-09-06 08:05:10,456 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:10,520 DEBUG: Best view : View0
-2016-09-06 08:05:10,946 DEBUG: Start: Iteration 7
-2016-09-06 08:05:10,953 DEBUG: View 0 : 0.701657458564
-2016-09-06 08:05:10,961 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:10,968 DEBUG: View 2 : 0.71270718232
-2016-09-06 08:05:10,975 DEBUG: View 3 : 0.662983425414
-2016-09-06 08:05:11,043 DEBUG: Best view : View2
-2016-09-06 08:05:11,537 DEBUG: Start: Iteration 8
-2016-09-06 08:05:11,545 DEBUG: View 0 : 0.701657458564
-2016-09-06 08:05:11,552 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:11,560 DEBUG: View 2 : 0.668508287293
-2016-09-06 08:05:11,567 DEBUG: View 3 : 0.662983425414
-2016-09-06 08:05:11,637 DEBUG: Best view : View0
-2016-09-06 08:05:12,202 DEBUG: Start: Iteration 9
-2016-09-06 08:05:12,210 DEBUG: View 0 : 0.701657458564
-2016-09-06 08:05:12,217 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:12,225 DEBUG: View 2 : 0.629834254144
-2016-09-06 08:05:12,232 DEBUG: View 3 : 0.662983425414
-2016-09-06 08:05:12,304 DEBUG: Best view : View0
-2016-09-06 08:05:12,937 DEBUG: Start: Iteration 10
-2016-09-06 08:05:12,944 DEBUG: View 0 : 0.67955801105
-2016-09-06 08:05:12,952 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:12,959 DEBUG: View 2 : 0.629834254144
-2016-09-06 08:05:12,966 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:13,042 DEBUG: Best view : View0
-2016-09-06 08:05:13,745 DEBUG: Start: Iteration 11
-2016-09-06 08:05:13,753 DEBUG: View 0 : 0.67955801105
-2016-09-06 08:05:13,760 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:13,768 DEBUG: View 2 : 0.640883977901
-2016-09-06 08:05:13,775 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:13,853 DEBUG: Best view : View0
-2016-09-06 08:05:14,626 DEBUG: Start: Iteration 12
-2016-09-06 08:05:14,634 DEBUG: View 0 : 0.67955801105
-2016-09-06 08:05:14,641 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:14,648 DEBUG: View 2 : 0.640883977901
-2016-09-06 08:05:14,656 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:14,737 DEBUG: Best view : View0
-2016-09-06 08:05:15,578 DEBUG: Start: Iteration 13
-2016-09-06 08:05:15,586 DEBUG: View 0 : 0.651933701657
-2016-09-06 08:05:15,593 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:15,601 DEBUG: View 2 : 0.640883977901
-2016-09-06 08:05:15,608 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:15,692 DEBUG: Best view : View0
-2016-09-06 08:05:16,601 DEBUG: Start: Iteration 14
-2016-09-06 08:05:16,609 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:16,616 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:16,623 DEBUG: View 2 : 0.640883977901
-2016-09-06 08:05:16,631 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:16,717 DEBUG: Best view : View0
-2016-09-06 08:05:17,696 DEBUG: Start: Iteration 15
-2016-09-06 08:05:17,704 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:17,711 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:17,718 DEBUG: View 2 : 0.651933701657
-2016-09-06 08:05:17,726 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:17,815 DEBUG: Best view : View0
-2016-09-06 08:05:18,860 DEBUG: Start: Iteration 16
-2016-09-06 08:05:18,867 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:18,874 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:18,881 DEBUG: View 2 : 0.651933701657
-2016-09-06 08:05:18,889 DEBUG: View 3 : 0.646408839779
-2016-09-06 08:05:18,979 DEBUG: Best view : View0
-2016-09-06 08:05:20,107 DEBUG: Start: Iteration 17
-2016-09-06 08:05:20,116 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:20,125 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:20,134 DEBUG: View 2 : 0.651933701657
-2016-09-06 08:05:20,143 DEBUG: View 3 : 0.662983425414
-2016-09-06 08:05:20,262 DEBUG: Best view : View0
-2016-09-06 08:05:21,920 DEBUG: Start: Iteration 18
-2016-09-06 08:05:21,936 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:21,951 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:21,965 DEBUG: View 2 : 0.640883977901
-2016-09-06 08:05:21,980 DEBUG: View 3 : 0.662983425414
-2016-09-06 08:05:22,140 DEBUG: Best view : View0
-2016-09-06 08:05:23,627 DEBUG: Start: Iteration 19
-2016-09-06 08:05:23,634 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:23,642 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:23,649 DEBUG: View 2 : 0.640883977901
-2016-09-06 08:05:23,657 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:23,756 DEBUG: Best view : View0
-2016-09-06 08:05:25,105 DEBUG: Start: Iteration 20
-2016-09-06 08:05:25,113 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:25,121 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:25,128 DEBUG: View 2 : 0.530386740331
-2016-09-06 08:05:25,136 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:25,240 DEBUG: Best view : View1
-2016-09-06 08:05:26,677 DEBUG: Start: Iteration 21
-2016-09-06 08:05:26,684 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:26,691 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:26,698 DEBUG: View 2 : 0.629834254144
-2016-09-06 08:05:26,705 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:26,808 DEBUG: Best view : View0
-2016-09-06 08:05:28,293 DEBUG: Start: Iteration 22
-2016-09-06 08:05:28,301 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:28,309 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:28,316 DEBUG: View 2 : 0.530386740331
-2016-09-06 08:05:28,324 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:28,431 DEBUG: Best view : View0
-2016-09-06 08:05:29,978 DEBUG: Start: Iteration 23
-2016-09-06 08:05:29,986 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:29,993 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:30,000 DEBUG: View 2 : 0.530386740331
-2016-09-06 08:05:30,008 DEBUG: View 3 : 0.662983425414
-2016-09-06 08:05:30,116 DEBUG: Best view : View0
-2016-09-06 08:05:32,159 DEBUG: Start: Iteration 24
-2016-09-06 08:05:32,169 DEBUG: View 0 : 0.696132596685
-2016-09-06 08:05:32,178 DEBUG: View 1 : 0.635359116022
-2016-09-06 08:05:32,187 DEBUG: View 2 : 0.563535911602
-2016-09-06 08:05:32,196 DEBUG: View 3 : 0.662983425414
-2016-09-06 08:05:32,326 DEBUG: Best view : View0
-2016-09-06 08:05:34,196 DEBUG: Start: Iteration 25
-2016-09-06 08:05:34,203 DEBUG: View 0 : 0.701657458564
-2016-09-06 08:05:34,211 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:34,218 DEBUG: View 2 : 0.563535911602
-2016-09-06 08:05:34,225 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:34,373 DEBUG: Best view : View0
-2016-09-06 08:05:36,562 DEBUG: Start: Iteration 26
-2016-09-06 08:05:36,571 DEBUG: View 0 : 0.690607734807
-2016-09-06 08:05:36,579 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:36,587 DEBUG: View 2 : 0.563535911602
-2016-09-06 08:05:36,594 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:36,737 DEBUG: Best view : View0
-2016-09-06 08:05:38,930 DEBUG: Start: Iteration 27
-2016-09-06 08:05:38,944 DEBUG: View 0 : 0.71270718232
-2016-09-06 08:05:38,956 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:38,966 DEBUG: View 2 : 0.640883977901
-2016-09-06 08:05:38,981 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:39,216 DEBUG: Best view : View0
-2016-09-06 08:05:41,369 DEBUG: Start: Iteration 28
-2016-09-06 08:05:41,379 DEBUG: View 0 : 0.71270718232
-2016-09-06 08:05:41,388 DEBUG: View 1 : 0.662983425414
-2016-09-06 08:05:41,395 DEBUG: View 2 : 0.640883977901
-2016-09-06 08:05:41,403 DEBUG: View 3 : 0.602209944751
-2016-09-06 08:05:41,538 DEBUG: Best view : View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f30c8b56c3c7d92e5a6b8b32f973c67b3bb016a4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9a76fd81ded61ed1bfa5c87cffc49dcf1d68d05d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0e3255b0b65a7807081f9afd6ea5565992ba2c09..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080459Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 28e5d9514f9cfd43156ff4bf799476d76d88f0df..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5e4e696a96c9a128c1564e6adcfcf9b33c772157..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080500Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.428571428571
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.428571428571
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e413d0c9db2ab230dbca7ca918a0b44ba0573b9c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 41d1525baca3cd97802c1fb04dde55fdc88ba86c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6464
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9bf62b3edb976c3548c13e252a457cd289349665..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080501Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4ec46b3986e4cde5ca6c50b38c0ed949d345037a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6d7aad0d07f8e781dec33bc658d6850bb98079b9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f0877a51b9e1500e0a3deb9b7cea007ad84c8fff..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080502Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 40bdc1795c9833245180f6a1d8693822294bc364..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 47b35c557bb07be61a67051f01f79a3d1425fbc2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b12bd9f225ab525851eda732fe3ab1df3f5ad6b0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ce93eca4d52381d6f14828acadd16e0d31f13933..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 35982db367f97f20e5a9b4632b4699536c2ab479..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6464
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 565ed5611b54183af8ab5c1b20fc356d2cd466cf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080503Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5f12573287b5581fc9117effaa533c47e98b275b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 128ee537810dbd793ec86ce451056827e21035ec..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 40c443971ed09faf713d471d0c6a2db6378ba1ba..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080504Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 26b43d37fe1460c6d256bc4c688210fc1f8d4d71..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 226def6e284330f5d176bca8aae8762d907a5458..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 350738bca4c927a3bf21fb4282a712ba1360bfcf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2cbc0923b4227e4e6992df0f6d048a71ca905312..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 730d5642ca863f913e6b06f75e575d5c38d4fc85..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 154fff3f7b8768d9321d3de73481bf328d045bab..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a00ae59d431a52ca5ba446e88bde2176c16c5d05..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080505Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ab0b361cd20e9d50f0d3c6864fed19ca46762d3c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2b7800f3a8fdfac73ce0c5bc65715628ceb1646c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 821287cab53f241324444d7869c1690704bf92dd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080506Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6231
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080507Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-KNN-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080507Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-KNN-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index c500f78a28b8fecfe37f9a7de37d16984cfc6bad..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080507Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-KNN-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 45.2380952381
- -On Validation : 83.8202247191
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 31
- - Random Forest with num_esimators : 26, max_depth : 23
- - K nearest Neighbors with n_neighbors: 31
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080555-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-080555-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 32d8fc693c4dc2b4f2554c5783958153090dd08f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080555-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1550 +0,0 @@
-2016-09-06 08:05:55,156 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:05:55,156 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 9.821875e-05 Gbytes /!\
-2016-09-06 08:06:00,166 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:06:00,167 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:06:00,212 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:00,212 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:00,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:06:00,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:06:00,213 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:00,213 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:00,214 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:00,214 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:00,214 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:00,214 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:00,214 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:00,214 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:00,214 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:00,214 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:00,274 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:00,275 DEBUG: Start: Training
-2016-09-06 08:06:00,277 DEBUG: Info: Time for Training: 0.0646359920502[s]
-2016-09-06 08:06:00,277 DEBUG: Done: Training
-2016-09-06 08:06:00,277 DEBUG: Start: Predicting
-2016-09-06 08:06:00,279 DEBUG: Done: Predicting
-2016-09-06 08:06:00,280 DEBUG: Start: Getting Results
-2016-09-06 08:06:00,281 DEBUG: Done: Getting Results
-2016-09-06 08:06:00,281 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:06:00,281 INFO: Done: Result Analysis
-2016-09-06 08:06:00,302 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:00,302 DEBUG: Start: Training
-2016-09-06 08:06:00,306 DEBUG: Info: Time for Training: 0.0938808917999[s]
-2016-09-06 08:06:00,306 DEBUG: Done: Training
-2016-09-06 08:06:00,306 DEBUG: Start: Predicting
-2016-09-06 08:06:00,309 DEBUG: Done: Predicting
-2016-09-06 08:06:00,309 DEBUG: Start: Getting Results
-2016-09-06 08:06:00,310 DEBUG: Done: Getting Results
-2016-09-06 08:06:00,311 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:00,311 INFO: Done: Result Analysis
-2016-09-06 08:06:00,462 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:00,462 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:00,462 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:06:00,462 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:06:00,463 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:00,463 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:00,463 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:00,463 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:00,463 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:00,463 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:00,464 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:00,464 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:00,464 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:00,464 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:00,516 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:00,517 DEBUG: Start: Training
-2016-09-06 08:06:00,517 DEBUG: Info: Time for Training: 0.0554449558258[s]
-2016-09-06 08:06:00,517 DEBUG: Done: Training
-2016-09-06 08:06:00,517 DEBUG: Start: Predicting
-2016-09-06 08:06:00,523 DEBUG: Done: Predicting
-2016-09-06 08:06:00,524 DEBUG: Start: Getting Results
-2016-09-06 08:06:00,525 DEBUG: Done: Getting Results
-2016-09-06 08:06:00,525 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 24
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:06:00,525 INFO: Done: Result Analysis
-2016-09-06 08:06:00,980 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:00,980 DEBUG: Start: Training
-2016-09-06 08:06:01,004 DEBUG: Info: Time for Training: 0.541980981827[s]
-2016-09-06 08:06:01,004 DEBUG: Done: Training
-2016-09-06 08:06:01,004 DEBUG: Start: Predicting
-2016-09-06 08:06:01,008 DEBUG: Done: Predicting
-2016-09-06 08:06:01,008 DEBUG: Start: Getting Results
-2016-09-06 08:06:01,009 DEBUG: Done: Getting Results
-2016-09-06 08:06:01,009 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 9, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,010 INFO: Done: Result Analysis
-2016-09-06 08:06:01,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,112 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:06:01,112 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:01,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,113 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:06:01,113 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:01,113 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:01,113 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:01,113 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:01,114 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:01,114 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:01,114 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,114 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:01,114 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,192 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:01,192 DEBUG: Start: Training
-2016-09-06 08:06:01,193 DEBUG: Info: Time for Training: 0.0819571018219[s]
-2016-09-06 08:06:01,193 DEBUG: Done: Training
-2016-09-06 08:06:01,193 DEBUG: Start: Predicting
-2016-09-06 08:06:01,203 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:01,203 DEBUG: Start: Training
-2016-09-06 08:06:01,210 DEBUG: Done: Predicting
-2016-09-06 08:06:01,210 DEBUG: Start: Getting Results
-2016-09-06 08:06:01,211 DEBUG: Done: Getting Results
-2016-09-06 08:06:01,212 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,212 INFO: Done: Result Analysis
-2016-09-06 08:06:01,225 DEBUG: Info: Time for Training: 0.113878011703[s]
-2016-09-06 08:06:01,226 DEBUG: Done: Training
-2016-09-06 08:06:01,226 DEBUG: Start: Predicting
-2016-09-06 08:06:01,229 DEBUG: Done: Predicting
-2016-09-06 08:06:01,229 DEBUG: Start: Getting Results
-2016-09-06 08:06:01,231 DEBUG: Done: Getting Results
-2016-09-06 08:06:01,231 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,231 INFO: Done: Result Analysis
-2016-09-06 08:06:01,351 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,351 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,351 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:06:01,351 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:06:01,351 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:01,352 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:01,352 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:01,352 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:06:01,352 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:01,352 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:06:01,352 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:01,352 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:01,352 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,352 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,445 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:01,445 DEBUG: Start: Training
-2016-09-06 08:06:01,454 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:01,454 DEBUG: Start: Training
-2016-09-06 08:06:01,464 DEBUG: Info: Time for Training: 0.113697052002[s]
-2016-09-06 08:06:01,465 DEBUG: Done: Training
-2016-09-06 08:06:01,465 DEBUG: Start: Predicting
-2016-09-06 08:06:01,471 DEBUG: Done: Predicting
-2016-09-06 08:06:01,471 DEBUG: Start: Getting Results
-2016-09-06 08:06:01,472 DEBUG: Done: Getting Results
-2016-09-06 08:06:01,473 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,473 INFO: Done: Result Analysis
-2016-09-06 08:06:01,474 DEBUG: Info: Time for Training: 0.123625040054[s]
-2016-09-06 08:06:01,474 DEBUG: Done: Training
-2016-09-06 08:06:01,474 DEBUG: Start: Predicting
-2016-09-06 08:06:01,479 DEBUG: Done: Predicting
-2016-09-06 08:06:01,479 DEBUG: Start: Getting Results
-2016-09-06 08:06:01,480 DEBUG: Done: Getting Results
-2016-09-06 08:06:01,480 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4367
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,480 INFO: Done: Result Analysis
-2016-09-06 08:06:01,598 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,598 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,598 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:06:01,598 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:06:01,599 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:01,599 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:01,599 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:01,599 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:01,599 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:01,599 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:01,599 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:01,599 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:01,599 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,599 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,653 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:01,653 DEBUG: Start: Training
-2016-09-06 08:06:01,655 DEBUG: Info: Time for Training: 0.0572459697723[s]
-2016-09-06 08:06:01,655 DEBUG: Done: Training
-2016-09-06 08:06:01,655 DEBUG: Start: Predicting
-2016-09-06 08:06:01,658 DEBUG: Done: Predicting
-2016-09-06 08:06:01,658 DEBUG: Start: Getting Results
-2016-09-06 08:06:01,659 DEBUG: Done: Getting Results
-2016-09-06 08:06:01,659 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,659 INFO: Done: Result Analysis
-2016-09-06 08:06:01,679 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:01,679 DEBUG: Start: Training
-2016-09-06 08:06:01,682 DEBUG: Info: Time for Training: 0.084260225296[s]
-2016-09-06 08:06:01,682 DEBUG: Done: Training
-2016-09-06 08:06:01,682 DEBUG: Start: Predicting
-2016-09-06 08:06:01,685 DEBUG: Done: Predicting
-2016-09-06 08:06:01,685 DEBUG: Start: Getting Results
-2016-09-06 08:06:01,687 DEBUG: Done: Getting Results
-2016-09-06 08:06:01,687 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,687 INFO: Done: Result Analysis
-2016-09-06 08:06:01,749 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,749 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:01,749 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:06:01,749 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:06:01,749 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:01,749 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:01,750 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:01,750 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:01,750 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:01,750 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:01,750 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:01,750 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:01,750 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,750 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:01,825 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:01,825 DEBUG: Start: Training
-2016-09-06 08:06:01,826 DEBUG: Info: Time for Training: 0.0780990123749[s]
-2016-09-06 08:06:01,826 DEBUG: Done: Training
-2016-09-06 08:06:01,826 DEBUG: Start: Predicting
-2016-09-06 08:06:01,832 DEBUG: Done: Predicting
-2016-09-06 08:06:01,832 DEBUG: Start: Getting Results
-2016-09-06 08:06:01,833 DEBUG: Done: Getting Results
-2016-09-06 08:06:01,833 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 24
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:01,833 INFO: Done: Result Analysis
-2016-09-06 08:06:02,371 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:02,371 DEBUG: Start: Training
-2016-09-06 08:06:02,435 DEBUG: Info: Time for Training: 0.687300920486[s]
-2016-09-06 08:06:02,435 DEBUG: Done: Training
-2016-09-06 08:06:02,435 DEBUG: Start: Predicting
-2016-09-06 08:06:02,443 DEBUG: Done: Predicting
-2016-09-06 08:06:02,443 DEBUG: Start: Getting Results
-2016-09-06 08:06:02,444 DEBUG: Done: Getting Results
-2016-09-06 08:06:02,444 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 24, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:06:02,444 INFO: Done: Result Analysis
-2016-09-06 08:06:02,609 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:02,610 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:06:02,610 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:02,610 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:02,611 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:02,611 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:02,611 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:02,617 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:02,617 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:06:02,617 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:02,618 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:02,618 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:02,618 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:02,618 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:02,739 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:02,739 DEBUG: Start: Training
-2016-09-06 08:06:02,740 DEBUG: Info: Time for Training: 0.131755828857[s]
-2016-09-06 08:06:02,741 DEBUG: Done: Training
-2016-09-06 08:06:02,741 DEBUG: Start: Predicting
-2016-09-06 08:06:02,794 DEBUG: Done: Predicting
-2016-09-06 08:06:02,794 DEBUG: Start: Getting Results
-2016-09-06 08:06:02,796 DEBUG: Done: Getting Results
-2016-09-06 08:06:02,796 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:06:02,796 INFO: Done: Result Analysis
-2016-09-06 08:06:02,802 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:02,803 DEBUG: Start: Training
-2016-09-06 08:06:02,833 DEBUG: Info: Time for Training: 0.216584920883[s]
-2016-09-06 08:06:02,833 DEBUG: Done: Training
-2016-09-06 08:06:02,833 DEBUG: Start: Predicting
-2016-09-06 08:06:02,838 DEBUG: Done: Predicting
-2016-09-06 08:06:02,838 DEBUG: Start: Getting Results
-2016-09-06 08:06:02,840 DEBUG: Done: Getting Results
-2016-09-06 08:06:02,840 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:06:02,841 INFO: Done: Result Analysis
-2016-09-06 08:06:02,958 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:02,958 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:06:02,958 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:02,958 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:02,958 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:06:02,958 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:02,959 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:02,959 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:02,959 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:02,959 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:02,959 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:02,959 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:02,959 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:02,959 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:03,046 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:03,046 DEBUG: Start: Training
-2016-09-06 08:06:03,064 DEBUG: Info: Time for Training: 0.106878042221[s]
-2016-09-06 08:06:03,064 DEBUG: Done: Training
-2016-09-06 08:06:03,065 DEBUG: Start: Predicting
-2016-09-06 08:06:03,071 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:03,071 DEBUG: Start: Training
-2016-09-06 08:06:03,071 DEBUG: Done: Predicting
-2016-09-06 08:06:03,071 DEBUG: Start: Getting Results
-2016-09-06 08:06:03,073 DEBUG: Done: Getting Results
-2016-09-06 08:06:03,073 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:06:03,073 INFO: Done: Result Analysis
-2016-09-06 08:06:03,092 DEBUG: Info: Time for Training: 0.135026931763[s]
-2016-09-06 08:06:03,093 DEBUG: Done: Training
-2016-09-06 08:06:03,093 DEBUG: Start: Predicting
-2016-09-06 08:06:03,096 DEBUG: Done: Predicting
-2016-09-06 08:06:03,097 DEBUG: Start: Getting Results
-2016-09-06 08:06:03,098 DEBUG: Done: Getting Results
-2016-09-06 08:06:03,098 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:06:03,098 INFO: Done: Result Analysis
-2016-09-06 08:06:03,202 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:03,202 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:03,202 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:06:03,203 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:06:03,203 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:03,203 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:03,203 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:03,203 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:03,203 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:03,203 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:03,203 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:03,204 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:03,204 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:03,204 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:03,262 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:03,262 DEBUG: Start: Training
-2016-09-06 08:06:03,263 DEBUG: Info: Time for Training: 0.0617170333862[s]
-2016-09-06 08:06:03,264 DEBUG: Done: Training
-2016-09-06 08:06:03,264 DEBUG: Start: Predicting
-2016-09-06 08:06:03,267 DEBUG: Done: Predicting
-2016-09-06 08:06:03,267 DEBUG: Start: Getting Results
-2016-09-06 08:06:03,268 DEBUG: Done: Getting Results
-2016-09-06 08:06:03,268 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:06:03,268 INFO: Done: Result Analysis
-2016-09-06 08:06:03,306 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:03,306 DEBUG: Start: Training
-2016-09-06 08:06:03,310 DEBUG: Info: Time for Training: 0.108617067337[s]
-2016-09-06 08:06:03,311 DEBUG: Done: Training
-2016-09-06 08:06:03,311 DEBUG: Start: Predicting
-2016-09-06 08:06:03,314 DEBUG: Done: Predicting
-2016-09-06 08:06:03,314 DEBUG: Start: Getting Results
-2016-09-06 08:06:03,316 DEBUG: Done: Getting Results
-2016-09-06 08:06:03,316 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:06:03,316 INFO: Done: Result Analysis
-2016-09-06 08:06:03,447 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:03,447 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:03,447 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:06:03,447 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:06:03,447 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:03,447 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:03,448 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:03,448 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:03,448 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:03,448 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:03,448 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:03,448 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:03,448 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:03,448 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:03,502 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:03,502 DEBUG: Start: Training
-2016-09-06 08:06:03,502 DEBUG: Info: Time for Training: 0.0562369823456[s]
-2016-09-06 08:06:03,502 DEBUG: Done: Training
-2016-09-06 08:06:03,503 DEBUG: Start: Predicting
-2016-09-06 08:06:03,508 DEBUG: Done: Predicting
-2016-09-06 08:06:03,509 DEBUG: Start: Getting Results
-2016-09-06 08:06:03,510 DEBUG: Done: Getting Results
-2016-09-06 08:06:03,510 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 24
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:06:03,510 INFO: Done: Result Analysis
-2016-09-06 08:06:03,974 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:03,974 DEBUG: Start: Training
-2016-09-06 08:06:03,999 DEBUG: Info: Time for Training: 0.552613019943[s]
-2016-09-06 08:06:03,999 DEBUG: Done: Training
-2016-09-06 08:06:03,999 DEBUG: Start: Predicting
-2016-09-06 08:06:04,003 DEBUG: Done: Predicting
-2016-09-06 08:06:04,004 DEBUG: Start: Getting Results
-2016-09-06 08:06:04,005 DEBUG: Done: Getting Results
-2016-09-06 08:06:04,005 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 9, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,005 INFO: Done: Result Analysis
-2016-09-06 08:06:04,092 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,092 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,092 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:06:04,092 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:06:04,092 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:04,092 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:04,093 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:04,093 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:04,093 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:04,093 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:04,093 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:04,093 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:04,093 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,093 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,213 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:04,213 DEBUG: Start: Training
-2016-09-06 08:06:04,215 DEBUG: Info: Time for Training: 0.123574018478[s]
-2016-09-06 08:06:04,215 DEBUG: Done: Training
-2016-09-06 08:06:04,215 DEBUG: Start: Predicting
-2016-09-06 08:06:04,223 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:04,223 DEBUG: Start: Training
-2016-09-06 08:06:04,238 DEBUG: Done: Predicting
-2016-09-06 08:06:04,238 DEBUG: Start: Getting Results
-2016-09-06 08:06:04,240 DEBUG: Done: Getting Results
-2016-09-06 08:06:04,240 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,240 INFO: Done: Result Analysis
-2016-09-06 08:06:04,241 DEBUG: Info: Time for Training: 0.150360107422[s]
-2016-09-06 08:06:04,242 DEBUG: Done: Training
-2016-09-06 08:06:04,242 DEBUG: Start: Predicting
-2016-09-06 08:06:04,245 DEBUG: Done: Predicting
-2016-09-06 08:06:04,245 DEBUG: Start: Getting Results
-2016-09-06 08:06:04,246 DEBUG: Done: Getting Results
-2016-09-06 08:06:04,246 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,246 INFO: Done: Result Analysis
-2016-09-06 08:06:04,334 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,334 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,334 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:06:04,334 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:06:04,334 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:04,334 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:04,335 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:04,335 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:06:04,335 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:04,335 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:06:04,335 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:04,335 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:04,335 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,335 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,422 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:04,422 DEBUG: Start: Training
-2016-09-06 08:06:04,428 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:04,428 DEBUG: Start: Training
-2016-09-06 08:06:04,441 DEBUG: Info: Time for Training: 0.107517957687[s]
-2016-09-06 08:06:04,441 DEBUG: Done: Training
-2016-09-06 08:06:04,441 DEBUG: Start: Predicting
-2016-09-06 08:06:04,447 DEBUG: Done: Predicting
-2016-09-06 08:06:04,448 DEBUG: Start: Getting Results
-2016-09-06 08:06:04,449 DEBUG: Done: Getting Results
-2016-09-06 08:06:04,449 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,449 DEBUG: Info: Time for Training: 0.115609884262[s]
-2016-09-06 08:06:04,449 INFO: Done: Result Analysis
-2016-09-06 08:06:04,449 DEBUG: Done: Training
-2016-09-06 08:06:04,449 DEBUG: Start: Predicting
-2016-09-06 08:06:04,453 DEBUG: Done: Predicting
-2016-09-06 08:06:04,453 DEBUG: Start: Getting Results
-2016-09-06 08:06:04,454 DEBUG: Done: Getting Results
-2016-09-06 08:06:04,454 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.980952380952
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,454 INFO: Done: Result Analysis
-2016-09-06 08:06:04,581 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,581 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,581 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:06:04,581 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:06:04,581 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:04,581 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:04,582 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:04,582 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:04,582 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:04,582 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:04,582 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:04,582 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:04,582 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,582 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,665 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:04,665 DEBUG: Start: Training
-2016-09-06 08:06:04,667 DEBUG: Info: Time for Training: 0.0870370864868[s]
-2016-09-06 08:06:04,667 DEBUG: Done: Training
-2016-09-06 08:06:04,667 DEBUG: Start: Predicting
-2016-09-06 08:06:04,671 DEBUG: Done: Predicting
-2016-09-06 08:06:04,671 DEBUG: Start: Getting Results
-2016-09-06 08:06:04,673 DEBUG: Done: Getting Results
-2016-09-06 08:06:04,673 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,673 INFO: Done: Result Analysis
-2016-09-06 08:06:04,696 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:04,696 DEBUG: Start: Training
-2016-09-06 08:06:04,700 DEBUG: Info: Time for Training: 0.119526147842[s]
-2016-09-06 08:06:04,700 DEBUG: Done: Training
-2016-09-06 08:06:04,700 DEBUG: Start: Predicting
-2016-09-06 08:06:04,703 DEBUG: Done: Predicting
-2016-09-06 08:06:04,703 DEBUG: Start: Getting Results
-2016-09-06 08:06:04,705 DEBUG: Done: Getting Results
-2016-09-06 08:06:04,705 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,705 INFO: Done: Result Analysis
-2016-09-06 08:06:04,825 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,825 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:04,825 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:06:04,825 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:06:04,826 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:04,826 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:04,826 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:04,826 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:04,827 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:04,827 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:04,827 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:04,827 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:04,827 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,827 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:04,910 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:04,910 DEBUG: Start: Training
-2016-09-06 08:06:04,911 DEBUG: Info: Time for Training: 0.0860750675201[s]
-2016-09-06 08:06:04,911 DEBUG: Done: Training
-2016-09-06 08:06:04,911 DEBUG: Start: Predicting
-2016-09-06 08:06:04,920 DEBUG: Done: Predicting
-2016-09-06 08:06:04,920 DEBUG: Start: Getting Results
-2016-09-06 08:06:04,921 DEBUG: Done: Getting Results
-2016-09-06 08:06:04,922 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:06:04,922 INFO: Done: Result Analysis
-2016-09-06 08:06:05,414 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:05,414 DEBUG: Start: Training
-2016-09-06 08:06:05,476 DEBUG: Info: Time for Training: 0.651627063751[s]
-2016-09-06 08:06:05,476 DEBUG: Done: Training
-2016-09-06 08:06:05,476 DEBUG: Start: Predicting
-2016-09-06 08:06:05,484 DEBUG: Done: Predicting
-2016-09-06 08:06:05,484 DEBUG: Start: Getting Results
-2016-09-06 08:06:05,485 DEBUG: Done: Getting Results
-2016-09-06 08:06:05,485 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 24, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 08:06:05,485 INFO: Done: Result Analysis
-2016-09-06 08:06:05,580 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:05,580 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:06:05,581 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:06:05,581 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:06:05,581 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:05,581 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:06:05,582 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:05,582 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:06:05,582 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:05,582 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:06:05,583 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:05,583 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:06:05,583 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:05,583 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:06:05,697 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:05,697 DEBUG: Start: Training
-2016-09-06 08:06:05,698 DEBUG: Info: Time for Training: 0.118808031082[s]
-2016-09-06 08:06:05,698 DEBUG: Done: Training
-2016-09-06 08:06:05,698 DEBUG: Start: Predicting
-2016-09-06 08:06:05,706 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:06:05,706 DEBUG: Start: Training
-2016-09-06 08:06:05,725 DEBUG: Done: Predicting
-2016-09-06 08:06:05,725 DEBUG: Start: Getting Results
-2016-09-06 08:06:05,726 DEBUG: Info: Time for Training: 0.146739006042[s]
-2016-09-06 08:06:05,726 DEBUG: Done: Training
-2016-09-06 08:06:05,726 DEBUG: Start: Predicting
-2016-09-06 08:06:05,727 DEBUG: Done: Getting Results
-2016-09-06 08:06:05,727 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:06:05,727 INFO: Done: Result Analysis
-2016-09-06 08:06:05,729 DEBUG: Done: Predicting
-2016-09-06 08:06:05,729 DEBUG: Start: Getting Results
-2016-09-06 08:06:05,730 DEBUG: Done: Getting Results
-2016-09-06 08:06:05,731 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:06:05,731 INFO: Done: Result Analysis
-2016-09-06 08:06:05,968 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:06:05,968 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:06:05,969 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:06:05,969 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:06:05,969 INFO: Info: Shape of View1 :(300, 8)
-2016-09-06 08:06:05,969 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:06:05,970 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 08:06:05,970 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:06:05,970 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 08:06:05,970 INFO: Info: Shape of View1 :(300, 8)
-2016-09-06 08:06:05,970 INFO: Done: Read Database Files
-2016-09-06 08:06:05,970 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:06:05,971 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 08:06:05,971 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 08:06:05,971 INFO: Done: Read Database Files
-2016-09-06 08:06:05,971 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:06:05,974 INFO: Done: Determine validation split
-2016-09-06 08:06:05,974 INFO: Start: Determine 5 folds
-2016-09-06 08:06:05,975 INFO: Done: Determine validation split
-2016-09-06 08:06:05,975 INFO: Start: Determine 5 folds
-2016-09-06 08:06:05,982 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:06:05,983 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:06:05,983 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:06:05,983 INFO: Done: Determine folds
-2016-09-06 08:06:05,983 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:06:05,983 INFO: Start: Classification
-2016-09-06 08:06:05,983 INFO: Start: Fold number 1
-2016-09-06 08:06:05,985 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:06:05,985 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:06:05,985 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:06:05,985 INFO: Done: Determine folds
-2016-09-06 08:06:05,985 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:06:05,985 INFO: Start: Classification
-2016-09-06 08:06:05,985 INFO: Start: Fold number 1
-2016-09-06 08:06:06,069 INFO: Start: Classification
-2016-09-06 08:06:06,144 INFO: Done: Fold number 1
-2016-09-06 08:06:06,144 INFO: Start: Fold number 2
-2016-09-06 08:06:06,223 INFO: Start: Classification
-2016-09-06 08:06:06,299 INFO: Done: Fold number 2
-2016-09-06 08:06:06,299 INFO: Start: Fold number 3
-2016-09-06 08:06:06,379 INFO: Start: Classification
-2016-09-06 08:06:06,454 INFO: Done: Fold number 3
-2016-09-06 08:06:06,454 INFO: Start: Fold number 4
-2016-09-06 08:06:06,531 INFO: Start: Classification
-2016-09-06 08:06:06,601 INFO: Done: Fold number 4
-2016-09-06 08:06:06,601 INFO: Start: Fold number 5
-2016-09-06 08:06:06,676 INFO: Start: Classification
-2016-09-06 08:06:06,745 INFO: Done: Fold number 5
-2016-09-06 08:06:06,745 INFO: Done: Classification
-2016-09-06 08:06:06,745 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:06:06,745 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:06:06,751 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 54.2857142857
- -On Validation : 86.2921348315
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - SVM Linear with C : 9141
- - SVM Linear with C : 9141
- - SVM Linear with C : 9141
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:06:06,751 INFO: Done: Result Analysis
-2016-09-06 08:06:07,997 DEBUG: Start: Iteration 1
-2016-09-06 08:06:08,005 DEBUG: View 0 : 0.491803278689
-2016-09-06 08:06:08,012 DEBUG: View 1 : 0.48087431694
-2016-09-06 08:06:08,019 DEBUG: View 2 : 0.535519125683
-2016-09-06 08:06:08,026 DEBUG: View 3 : 0.497267759563
-2016-09-06 08:06:08,073 DEBUG: Best view : View2
-2016-09-06 08:06:08,157 DEBUG: Start: Iteration 2
-2016-09-06 08:06:08,165 DEBUG: View 0 : 0.633879781421
-2016-09-06 08:06:08,173 DEBUG: View 1 : 0.655737704918
-2016-09-06 08:06:08,180 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:08,187 DEBUG: View 3 : 0.710382513661
-2016-09-06 08:06:08,246 DEBUG: Best view : View3
-2016-09-06 08:06:08,399 DEBUG: Start: Iteration 3
-2016-09-06 08:06:08,407 DEBUG: View 0 : 0.633879781421
-2016-09-06 08:06:08,414 DEBUG: View 1 : 0.655737704918
-2016-09-06 08:06:08,422 DEBUG: View 2 : 0.72131147541
-2016-09-06 08:06:08,429 DEBUG: View 3 : 0.710382513661
-2016-09-06 08:06:08,487 DEBUG: Best view : View2
-2016-09-06 08:06:08,711 DEBUG: Start: Iteration 4
-2016-09-06 08:06:08,719 DEBUG: View 0 : 0.650273224044
-2016-09-06 08:06:08,726 DEBUG: View 1 : 0.639344262295
-2016-09-06 08:06:08,733 DEBUG: View 2 : 0.72131147541
-2016-09-06 08:06:08,740 DEBUG: View 3 : 0.688524590164
-2016-09-06 08:06:08,801 DEBUG: Best view : View2
-2016-09-06 08:06:09,093 DEBUG: Start: Iteration 5
-2016-09-06 08:06:09,100 DEBUG: View 0 : 0.650273224044
-2016-09-06 08:06:09,107 DEBUG: View 1 : 0.672131147541
-2016-09-06 08:06:09,114 DEBUG: View 2 : 0.644808743169
-2016-09-06 08:06:09,121 DEBUG: View 3 : 0.672131147541
-2016-09-06 08:06:09,185 DEBUG: Best view : View2
-2016-09-06 08:06:09,547 DEBUG: Start: Iteration 6
-2016-09-06 08:06:09,555 DEBUG: View 0 : 0.644808743169
-2016-09-06 08:06:09,562 DEBUG: View 1 : 0.677595628415
-2016-09-06 08:06:09,569 DEBUG: View 2 : 0.644808743169
-2016-09-06 08:06:09,577 DEBUG: View 3 : 0.672131147541
-2016-09-06 08:06:09,649 DEBUG: Best view : View2
-2016-09-06 08:06:10,082 DEBUG: Start: Iteration 7
-2016-09-06 08:06:10,089 DEBUG: View 0 : 0.644808743169
-2016-09-06 08:06:10,096 DEBUG: View 1 : 0.644808743169
-2016-09-06 08:06:10,103 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:10,110 DEBUG: View 3 : 0.650273224044
-2016-09-06 08:06:10,179 DEBUG: Best view : View3
-2016-09-06 08:06:10,679 DEBUG: Start: Iteration 8
-2016-09-06 08:06:10,687 DEBUG: View 0 : 0.672131147541
-2016-09-06 08:06:10,694 DEBUG: View 1 : 0.644808743169
-2016-09-06 08:06:10,700 DEBUG: View 2 : 0.726775956284
-2016-09-06 08:06:10,708 DEBUG: View 3 : 0.68306010929
-2016-09-06 08:06:10,780 DEBUG: Best view : View2
-2016-09-06 08:06:11,369 DEBUG: Start: Iteration 9
-2016-09-06 08:06:11,376 DEBUG: View 0 : 0.672131147541
-2016-09-06 08:06:11,384 DEBUG: View 1 : 0.655737704918
-2016-09-06 08:06:11,391 DEBUG: View 2 : 0.726775956284
-2016-09-06 08:06:11,397 DEBUG: View 3 : 0.710382513661
-2016-09-06 08:06:11,471 DEBUG: Best view : View2
-2016-09-06 08:06:12,105 DEBUG: Start: Iteration 10
-2016-09-06 08:06:12,113 DEBUG: View 0 : 0.655737704918
-2016-09-06 08:06:12,120 DEBUG: View 1 : 0.655737704918
-2016-09-06 08:06:12,127 DEBUG: View 2 : 0.650273224044
-2016-09-06 08:06:12,134 DEBUG: View 3 : 0.710382513661
-2016-09-06 08:06:12,210 DEBUG: Best view : View3
-2016-09-06 08:06:12,922 DEBUG: Start: Iteration 11
-2016-09-06 08:06:12,929 DEBUG: View 0 : 0.633879781421
-2016-09-06 08:06:12,936 DEBUG: View 1 : 0.655737704918
-2016-09-06 08:06:12,944 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:12,950 DEBUG: View 3 : 0.710382513661
-2016-09-06 08:06:13,034 DEBUG: Best view : View3
-2016-09-06 08:06:13,809 DEBUG: Start: Iteration 12
-2016-09-06 08:06:13,816 DEBUG: View 0 : 0.633879781421
-2016-09-06 08:06:13,823 DEBUG: View 1 : 0.622950819672
-2016-09-06 08:06:13,830 DEBUG: View 2 : 0.661202185792
-2016-09-06 08:06:13,837 DEBUG: View 3 : 0.688524590164
-2016-09-06 08:06:13,918 DEBUG: Best view : View2
-2016-09-06 08:06:14,758 DEBUG: Start: Iteration 13
-2016-09-06 08:06:14,765 DEBUG: View 0 : 0.633879781421
-2016-09-06 08:06:14,772 DEBUG: View 1 : 0.622950819672
-2016-09-06 08:06:14,780 DEBUG: View 2 : 0.661202185792
-2016-09-06 08:06:14,788 DEBUG: View 3 : 0.666666666667
-2016-09-06 08:06:14,872 DEBUG: Best view : View2
-2016-09-06 08:06:15,801 DEBUG: Start: Iteration 14
-2016-09-06 08:06:15,808 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:06:15,815 DEBUG: View 1 : 0.639344262295
-2016-09-06 08:06:15,822 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:15,829 DEBUG: View 3 : 0.672131147541
-2016-09-06 08:06:15,916 DEBUG: Best view : View2
-2016-09-06 08:06:16,895 DEBUG: Start: Iteration 15
-2016-09-06 08:06:16,902 DEBUG: View 0 : 0.639344262295
-2016-09-06 08:06:16,909 DEBUG: View 1 : 0.639344262295
-2016-09-06 08:06:16,917 DEBUG: View 2 : 0.644808743169
-2016-09-06 08:06:16,924 DEBUG: View 3 : 0.677595628415
-2016-09-06 08:06:17,013 DEBUG: Best view : View2
-2016-09-06 08:06:18,061 DEBUG: Start: Iteration 16
-2016-09-06 08:06:18,069 DEBUG: View 0 : 0.639344262295
-2016-09-06 08:06:18,075 DEBUG: View 1 : 0.639344262295
-2016-09-06 08:06:18,083 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:18,089 DEBUG: View 3 : 0.693989071038
-2016-09-06 08:06:18,182 DEBUG: Best view : View2
-2016-09-06 08:06:19,305 DEBUG: Start: Iteration 17
-2016-09-06 08:06:19,312 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:06:19,320 DEBUG: View 1 : 0.644808743169
-2016-09-06 08:06:19,326 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:19,333 DEBUG: View 3 : 0.710382513661
-2016-09-06 08:06:19,427 DEBUG: Best view : View3
-2016-09-06 08:06:20,613 DEBUG: Start: Iteration 18
-2016-09-06 08:06:20,620 DEBUG: View 0 : 0.633879781421
-2016-09-06 08:06:20,627 DEBUG: View 1 : 0.644808743169
-2016-09-06 08:06:20,634 DEBUG: View 2 : 0.644808743169
-2016-09-06 08:06:20,641 DEBUG: View 3 : 0.68306010929
-2016-09-06 08:06:20,741 DEBUG: Best view : View3
-2016-09-06 08:06:22,010 DEBUG: Start: Iteration 19
-2016-09-06 08:06:22,017 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:06:22,024 DEBUG: View 1 : 0.655737704918
-2016-09-06 08:06:22,031 DEBUG: View 2 : 0.644808743169
-2016-09-06 08:06:22,038 DEBUG: View 3 : 0.661202185792
-2016-09-06 08:06:22,142 DEBUG: Best view : View3
-2016-09-06 08:06:23,499 DEBUG: Start: Iteration 20
-2016-09-06 08:06:23,508 DEBUG: View 0 : 0.590163934426
-2016-09-06 08:06:23,516 DEBUG: View 1 : 0.655737704918
-2016-09-06 08:06:23,527 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:23,536 DEBUG: View 3 : 0.688524590164
-2016-09-06 08:06:23,672 DEBUG: Best view : View2
-2016-09-06 08:06:25,199 DEBUG: Start: Iteration 21
-2016-09-06 08:06:25,213 DEBUG: View 0 : 0.590163934426
-2016-09-06 08:06:25,227 DEBUG: View 1 : 0.655737704918
-2016-09-06 08:06:25,241 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:25,255 DEBUG: View 3 : 0.672131147541
-2016-09-06 08:06:25,398 DEBUG: Best view : View2
-2016-09-06 08:06:27,290 DEBUG: Start: Iteration 22
-2016-09-06 08:06:27,298 DEBUG: View 0 : 0.628415300546
-2016-09-06 08:06:27,306 DEBUG: View 1 : 0.639344262295
-2016-09-06 08:06:27,314 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:27,321 DEBUG: View 3 : 0.672131147541
-2016-09-06 08:06:27,439 DEBUG: Best view : View2
-2016-09-06 08:06:29,011 DEBUG: Start: Iteration 23
-2016-09-06 08:06:29,020 DEBUG: View 0 : 0.628415300546
-2016-09-06 08:06:29,027 DEBUG: View 1 : 0.644808743169
-2016-09-06 08:06:29,035 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:29,042 DEBUG: View 3 : 0.677595628415
-2016-09-06 08:06:29,157 DEBUG: Best view : View2
-2016-09-06 08:06:30,789 DEBUG: Start: Iteration 24
-2016-09-06 08:06:30,797 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:06:30,805 DEBUG: View 1 : 0.633879781421
-2016-09-06 08:06:30,813 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:30,821 DEBUG: View 3 : 0.710382513661
-2016-09-06 08:06:30,938 DEBUG: Best view : View3
-2016-09-06 08:06:32,645 DEBUG: Start: Iteration 25
-2016-09-06 08:06:32,653 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:06:32,661 DEBUG: View 1 : 0.633879781421
-2016-09-06 08:06:32,668 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:32,676 DEBUG: View 3 : 0.704918032787
-2016-09-06 08:06:32,792 DEBUG: Best view : View3
-2016-09-06 08:06:34,560 DEBUG: Start: Iteration 26
-2016-09-06 08:06:34,568 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:06:34,576 DEBUG: View 1 : 0.661202185792
-2016-09-06 08:06:34,583 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:34,590 DEBUG: View 3 : 0.704918032787
-2016-09-06 08:06:34,708 DEBUG: Best view : View3
-2016-09-06 08:06:36,580 DEBUG: Start: Iteration 27
-2016-09-06 08:06:36,588 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:06:36,596 DEBUG: View 1 : 0.661202185792
-2016-09-06 08:06:36,603 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:36,611 DEBUG: View 3 : 0.693989071038
-2016-09-06 08:06:36,738 DEBUG: Best view : View3
-2016-09-06 08:06:38,662 DEBUG: Start: Iteration 28
-2016-09-06 08:06:38,670 DEBUG: View 0 : 0.590163934426
-2016-09-06 08:06:38,677 DEBUG: View 1 : 0.639344262295
-2016-09-06 08:06:38,684 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:38,691 DEBUG: View 3 : 0.650273224044
-2016-09-06 08:06:38,818 DEBUG: Best view : View3
-2016-09-06 08:06:40,822 DEBUG: Start: Iteration 29
-2016-09-06 08:06:40,830 DEBUG: View 0 : 0.639344262295
-2016-09-06 08:06:40,837 DEBUG: View 1 : 0.633879781421
-2016-09-06 08:06:40,844 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:40,852 DEBUG: View 3 : 0.650273224044
-2016-09-06 08:06:40,982 DEBUG: Best view : View3
-2016-09-06 08:06:43,053 DEBUG: Start: Iteration 30
-2016-09-06 08:06:43,061 DEBUG: View 0 : 0.639344262295
-2016-09-06 08:06:43,097 DEBUG: View 1 : 0.633879781421
-2016-09-06 08:06:43,105 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:43,112 DEBUG: View 3 : 0.693989071038
-2016-09-06 08:06:43,245 DEBUG: Best view : View3
-2016-09-06 08:06:45,351 DEBUG: Start: Iteration 31
-2016-09-06 08:06:45,359 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:06:45,366 DEBUG: View 1 : 0.633879781421
-2016-09-06 08:06:45,373 DEBUG: View 2 : 0.650273224044
-2016-09-06 08:06:45,380 DEBUG: View 3 : 0.710382513661
-2016-09-06 08:06:45,563 DEBUG: Best view : View2
-2016-09-06 08:06:47,829 DEBUG: Start: Iteration 32
-2016-09-06 08:06:47,836 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:06:47,843 DEBUG: View 1 : 0.661202185792
-2016-09-06 08:06:47,851 DEBUG: View 2 : 0.650273224044
-2016-09-06 08:06:47,858 DEBUG: View 3 : 0.704918032787
-2016-09-06 08:06:48,005 DEBUG: Best view : View2
-2016-09-06 08:06:50,295 DEBUG: Start: Iteration 33
-2016-09-06 08:06:50,302 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:06:50,310 DEBUG: View 1 : 0.661202185792
-2016-09-06 08:06:50,317 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:50,324 DEBUG: View 3 : 0.688524590164
-2016-09-06 08:06:50,457 DEBUG: Best view : View3
-2016-09-06 08:06:52,732 DEBUG: Start: Iteration 34
-2016-09-06 08:06:52,740 DEBUG: View 0 : 0.590163934426
-2016-09-06 08:06:52,747 DEBUG: View 1 : 0.644808743169
-2016-09-06 08:06:52,755 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:52,763 DEBUG: View 3 : 0.693989071038
-2016-09-06 08:06:52,916 DEBUG: Best view : View3
-2016-09-06 08:06:55,509 DEBUG: Start: Iteration 35
-2016-09-06 08:06:55,516 DEBUG: View 0 : 0.590163934426
-2016-09-06 08:06:55,524 DEBUG: View 1 : 0.644808743169
-2016-09-06 08:06:55,531 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:55,538 DEBUG: View 3 : 0.672131147541
-2016-09-06 08:06:55,678 DEBUG: Best view : View3
-2016-09-06 08:06:58,112 DEBUG: Start: Iteration 36
-2016-09-06 08:06:58,119 DEBUG: View 0 : 0.590163934426
-2016-09-06 08:06:58,127 DEBUG: View 1 : 0.633879781421
-2016-09-06 08:06:58,133 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:06:58,140 DEBUG: View 3 : 0.672131147541
-2016-09-06 08:06:58,287 DEBUG: Best view : View3
-2016-09-06 08:07:00,814 DEBUG: Start: Iteration 37
-2016-09-06 08:07:00,822 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:07:00,829 DEBUG: View 1 : 0.639344262295
-2016-09-06 08:07:00,836 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:07:00,843 DEBUG: View 3 : 0.672131147541
-2016-09-06 08:07:01,100 DEBUG: Best view : View3
-2016-09-06 08:07:04,473 DEBUG: Start: Iteration 38
-2016-09-06 08:07:04,485 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:07:04,498 DEBUG: View 1 : 0.661202185792
-2016-09-06 08:07:04,511 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:07:04,523 DEBUG: View 3 : 0.68306010929
-2016-09-06 08:07:04,691 DEBUG: Best view : View3
-2016-09-06 08:07:07,716 DEBUG: Start: Iteration 39
-2016-09-06 08:07:07,723 DEBUG: View 0 : 0.628415300546
-2016-09-06 08:07:07,731 DEBUG: View 1 : 0.655737704918
-2016-09-06 08:07:07,738 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:07:07,745 DEBUG: View 3 : 0.677595628415
-2016-09-06 08:07:07,897 DEBUG: Best view : View3
-2016-09-06 08:07:10,829 DEBUG: Start: Iteration 40
-2016-09-06 08:07:10,836 DEBUG: View 0 : 0.628415300546
-2016-09-06 08:07:10,843 DEBUG: View 1 : 0.633879781421
-2016-09-06 08:07:10,851 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:07:10,858 DEBUG: View 3 : 0.704918032787
-2016-09-06 08:07:11,018 DEBUG: Best view : View3
-2016-09-06 08:07:14,057 DEBUG: Start: Iteration 41
-2016-09-06 08:07:14,066 DEBUG: View 0 : 0.622950819672
-2016-09-06 08:07:14,074 DEBUG: View 1 : 0.633879781421
-2016-09-06 08:07:14,081 DEBUG: View 2 : 0.655737704918
-2016-09-06 08:07:14,088 DEBUG: View 3 : 0.677595628415
-2016-09-06 08:07:14,253 DEBUG: Best view : View3
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8e044ac968e34b626dc65ee34b355bd67b3cef66..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3c3ec1a1db2f13c480bb8bb2ad4d68735c44212e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5344b79059602ca4654fec80f36429255a27edfe..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080600Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 24
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d80134df8c28777c0f5c774aba08571236818adc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b3a3a6b256a77a4a21eec24aca6bb72c6fc8ac49..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fd0eb02b2dd2ea0cbcf6bd9655adaac307f932f5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 24
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1aecca626a6c07177217b88961c32e35a3e17e03..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 9, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bace8efdadc027dea5c84b7031d99886fdf75ebd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0ac6d6d226116873669af3fbefea0af50e50ca7d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4502c840d527366b14d943ebb5743c6e4259a68f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4367
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2af377a8aab0811e64fb3b83a228829caebf45be..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080601Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index da1e51dbdaa0cf272e15296cd993f48c7bc2db54..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 24, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4e9bf54f3e09d2b9c9c9ed289b2d19b596b479bc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 44dcaf4d09c37d51477b7aa2950af0a8882c95b6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080602Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 14e81ebce3a4de4fee29303b79880f6400d33931..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1648788e189a40dfc9166c573064e9cd4bc25c3b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f3a622e213f61c70ed70a9a0e6a7df6d51a886e2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 24
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index aba772dc42004c265c6aaaef5f4a9835d7302d7c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 9, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 19891edee76a3c298f11329e1d635d780767918a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index eba1f50422b04c549bc3f5af9f63ae136eab66fe..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080603Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5138179afb48930dae04b64c12e0ea6ae29b1b63..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d42d8911001cbdfd7e84b8b2b47263705ae85206..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 57f4930110d3cb65f82f31a31a081581197d9725..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 68ccb89bc392fe8e5be2a35b3bd58544db9223aa..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bd049f033179d2deb1a82f358fe9f371598b72d5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index efb8261d61d847fd678cbf388889e90b68bcae3a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.980952380952
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b069286b8eae8fe63658b603f3355f49ff3e9774..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080604Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cc9e53b0cd01798655637250c871240c3d4e2342..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 24, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5c02b6e02d21830411b75e0ad679046bb2f38b4b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d70b02cb182bfd739ec4417c5cbb5e825d0e8a19..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080605Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9141
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080606Results-Fusion-LateFusion-BayesianInference-Adaboost-SVMLinear-SVMLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080606Results-Fusion-LateFusion-BayesianInference-Adaboost-SVMLinear-SVMLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index e95028d164729452099609c510e7f7c8c74d164f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080606Results-Fusion-LateFusion-BayesianInference-Adaboost-SVMLinear-SVMLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 54.2857142857
- -On Validation : 86.2921348315
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - SVM Linear with C : 9141
- - SVM Linear with C : 9141
- - SVM Linear with C : 9141
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080717-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-080717-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index a4e3af56f0ee6818e8e5264bc03a303fdc9d0b2a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080717-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1762 +0,0 @@
-2016-09-06 08:07:17,026 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:07:17,027 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00012634375 Gbytes /!\
-2016-09-06 08:07:22,035 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:07:22,037 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:07:22,084 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,084 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,084 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:07:22,084 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:07:22,084 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:22,084 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:22,085 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,085 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,085 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,085 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:22,085 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,085 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,085 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:22,085 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,145 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:22,145 DEBUG: Start: Training
-2016-09-06 08:07:22,147 DEBUG: Info: Time for Training: 0.0636520385742[s]
-2016-09-06 08:07:22,147 DEBUG: Done: Training
-2016-09-06 08:07:22,147 DEBUG: Start: Predicting
-2016-09-06 08:07:22,150 DEBUG: Done: Predicting
-2016-09-06 08:07:22,150 DEBUG: Start: Getting Results
-2016-09-06 08:07:22,151 DEBUG: Done: Getting Results
-2016-09-06 08:07:22,151 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,151 INFO: Done: Result Analysis
-2016-09-06 08:07:22,172 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:22,172 DEBUG: Start: Training
-2016-09-06 08:07:22,176 DEBUG: Info: Time for Training: 0.092915058136[s]
-2016-09-06 08:07:22,176 DEBUG: Done: Training
-2016-09-06 08:07:22,177 DEBUG: Start: Predicting
-2016-09-06 08:07:22,179 DEBUG: Done: Predicting
-2016-09-06 08:07:22,179 DEBUG: Start: Getting Results
-2016-09-06 08:07:22,181 DEBUG: Done: Getting Results
-2016-09-06 08:07:22,181 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,181 INFO: Done: Result Analysis
-2016-09-06 08:07:22,335 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,335 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,335 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:07:22,335 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:07:22,336 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:22,336 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:22,336 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,336 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,336 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,336 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,336 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:22,336 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:22,337 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,337 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,393 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:22,393 DEBUG: Start: Training
-2016-09-06 08:07:22,394 DEBUG: Info: Time for Training: 0.0591959953308[s]
-2016-09-06 08:07:22,394 DEBUG: Done: Training
-2016-09-06 08:07:22,394 DEBUG: Start: Predicting
-2016-09-06 08:07:22,402 DEBUG: Done: Predicting
-2016-09-06 08:07:22,402 DEBUG: Start: Getting Results
-2016-09-06 08:07:22,403 DEBUG: Done: Getting Results
-2016-09-06 08:07:22,403 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.661904761905
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,403 INFO: Done: Result Analysis
-2016-09-06 08:07:22,641 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:22,641 DEBUG: Start: Training
-2016-09-06 08:07:22,660 DEBUG: Info: Time for Training: 0.32483792305[s]
-2016-09-06 08:07:22,660 DEBUG: Done: Training
-2016-09-06 08:07:22,660 DEBUG: Start: Predicting
-2016-09-06 08:07:22,664 DEBUG: Done: Predicting
-2016-09-06 08:07:22,664 DEBUG: Start: Getting Results
-2016-09-06 08:07:22,665 DEBUG: Done: Getting Results
-2016-09-06 08:07:22,665 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.952380952381
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,665 INFO: Done: Result Analysis
-2016-09-06 08:07:22,783 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,783 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:22,783 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:07:22,783 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:07:22,783 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:22,783 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:22,784 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,784 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:22,784 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,784 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:22,784 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:22,784 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:22,784 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,784 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:22,905 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:22,905 DEBUG: Start: Training
-2016-09-06 08:07:22,907 DEBUG: Info: Time for Training: 0.12481212616[s]
-2016-09-06 08:07:22,907 DEBUG: Done: Training
-2016-09-06 08:07:22,907 DEBUG: Start: Predicting
-2016-09-06 08:07:22,916 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:22,917 DEBUG: Start: Training
-2016-09-06 08:07:22,918 DEBUG: Done: Predicting
-2016-09-06 08:07:22,918 DEBUG: Start: Getting Results
-2016-09-06 08:07:22,919 DEBUG: Done: Getting Results
-2016-09-06 08:07:22,919 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,919 INFO: Done: Result Analysis
-2016-09-06 08:07:22,936 DEBUG: Info: Time for Training: 0.154469013214[s]
-2016-09-06 08:07:22,937 DEBUG: Done: Training
-2016-09-06 08:07:22,937 DEBUG: Start: Predicting
-2016-09-06 08:07:22,940 DEBUG: Done: Predicting
-2016-09-06 08:07:22,940 DEBUG: Start: Getting Results
-2016-09-06 08:07:22,941 DEBUG: Done: Getting Results
-2016-09-06 08:07:22,941 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.438095238095
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.438095238095
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:07:22,941 INFO: Done: Result Analysis
-2016-09-06 08:07:23,022 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,022 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,022 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:07:23,022 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:07:23,022 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:23,022 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:23,023 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:23,023 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:07:23,023 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:23,023 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:07:23,023 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:23,023 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:23,023 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,023 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,106 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:23,107 DEBUG: Start: Training
-2016-09-06 08:07:23,112 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:23,112 DEBUG: Start: Training
-2016-09-06 08:07:23,126 DEBUG: Info: Time for Training: 0.104226112366[s]
-2016-09-06 08:07:23,126 DEBUG: Done: Training
-2016-09-06 08:07:23,126 DEBUG: Start: Predicting
-2016-09-06 08:07:23,132 DEBUG: Done: Predicting
-2016-09-06 08:07:23,132 DEBUG: Info: Time for Training: 0.110702037811[s]
-2016-09-06 08:07:23,132 DEBUG: Start: Getting Results
-2016-09-06 08:07:23,132 DEBUG: Done: Training
-2016-09-06 08:07:23,132 DEBUG: Start: Predicting
-2016-09-06 08:07:23,133 DEBUG: Done: Getting Results
-2016-09-06 08:07:23,133 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,133 INFO: Done: Result Analysis
-2016-09-06 08:07:23,136 DEBUG: Done: Predicting
-2016-09-06 08:07:23,137 DEBUG: Start: Getting Results
-2016-09-06 08:07:23,138 DEBUG: Done: Getting Results
-2016-09-06 08:07:23,138 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,138 INFO: Done: Result Analysis
-2016-09-06 08:07:23,270 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,270 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,270 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:07:23,270 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:07:23,271 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:23,271 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:23,271 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,271 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,271 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,271 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,271 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:23,271 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:23,271 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,272 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,329 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:23,329 DEBUG: Start: Training
-2016-09-06 08:07:23,331 DEBUG: Info: Time for Training: 0.0611419677734[s]
-2016-09-06 08:07:23,331 DEBUG: Done: Training
-2016-09-06 08:07:23,331 DEBUG: Start: Predicting
-2016-09-06 08:07:23,333 DEBUG: Done: Predicting
-2016-09-06 08:07:23,333 DEBUG: Start: Getting Results
-2016-09-06 08:07:23,334 DEBUG: Done: Getting Results
-2016-09-06 08:07:23,334 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,335 INFO: Done: Result Analysis
-2016-09-06 08:07:23,365 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:23,365 DEBUG: Start: Training
-2016-09-06 08:07:23,369 DEBUG: Info: Time for Training: 0.0994069576263[s]
-2016-09-06 08:07:23,369 DEBUG: Done: Training
-2016-09-06 08:07:23,369 DEBUG: Start: Predicting
-2016-09-06 08:07:23,372 DEBUG: Done: Predicting
-2016-09-06 08:07:23,373 DEBUG: Start: Getting Results
-2016-09-06 08:07:23,374 DEBUG: Done: Getting Results
-2016-09-06 08:07:23,374 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,374 INFO: Done: Result Analysis
-2016-09-06 08:07:23,524 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,524 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,524 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:07:23,524 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:07:23,524 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:23,524 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:23,525 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,525 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,526 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,526 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,526 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:23,526 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:23,526 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,526 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,587 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:23,587 DEBUG: Start: Training
-2016-09-06 08:07:23,587 DEBUG: Info: Time for Training: 0.0643999576569[s]
-2016-09-06 08:07:23,588 DEBUG: Done: Training
-2016-09-06 08:07:23,588 DEBUG: Start: Predicting
-2016-09-06 08:07:23,594 DEBUG: Done: Predicting
-2016-09-06 08:07:23,594 DEBUG: Start: Getting Results
-2016-09-06 08:07:23,595 DEBUG: Done: Getting Results
-2016-09-06 08:07:23,595 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,595 INFO: Done: Result Analysis
-2016-09-06 08:07:23,872 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:23,872 DEBUG: Start: Training
-2016-09-06 08:07:23,901 DEBUG: Info: Time for Training: 0.377696990967[s]
-2016-09-06 08:07:23,901 DEBUG: Done: Training
-2016-09-06 08:07:23,901 DEBUG: Start: Predicting
-2016-09-06 08:07:23,906 DEBUG: Done: Predicting
-2016-09-06 08:07:23,906 DEBUG: Start: Getting Results
-2016-09-06 08:07:23,907 DEBUG: Done: Getting Results
-2016-09-06 08:07:23,907 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:07:23,908 INFO: Done: Result Analysis
-2016-09-06 08:07:23,970 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,970 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:23,970 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:07:23,970 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:07:23,970 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:23,970 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:23,971 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,971 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:23,971 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,971 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:23,971 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:23,971 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:23,971 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:23,971 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,054 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:24,055 DEBUG: Start: Training
-2016-09-06 08:07:24,055 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:24,055 DEBUG: Start: Training
-2016-09-06 08:07:24,055 DEBUG: Info: Time for Training: 0.0859999656677[s]
-2016-09-06 08:07:24,055 DEBUG: Done: Training
-2016-09-06 08:07:24,056 DEBUG: Start: Predicting
-2016-09-06 08:07:24,070 DEBUG: Done: Predicting
-2016-09-06 08:07:24,070 DEBUG: Start: Getting Results
-2016-09-06 08:07:24,071 DEBUG: Done: Getting Results
-2016-09-06 08:07:24,071 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,072 INFO: Done: Result Analysis
-2016-09-06 08:07:24,074 DEBUG: Info: Time for Training: 0.104997873306[s]
-2016-09-06 08:07:24,074 DEBUG: Done: Training
-2016-09-06 08:07:24,075 DEBUG: Start: Predicting
-2016-09-06 08:07:24,078 DEBUG: Done: Predicting
-2016-09-06 08:07:24,078 DEBUG: Start: Getting Results
-2016-09-06 08:07:24,079 DEBUG: Done: Getting Results
-2016-09-06 08:07:24,079 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,079 INFO: Done: Result Analysis
-2016-09-06 08:07:24,230 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,230 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,230 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:07:24,230 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:07:24,230 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:24,230 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:24,231 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:24,231 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:07:24,231 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:24,231 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:07:24,232 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:24,232 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:24,232 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,232 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,329 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:24,329 DEBUG: Start: Training
-2016-09-06 08:07:24,335 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:24,335 DEBUG: Start: Training
-2016-09-06 08:07:24,346 DEBUG: Info: Time for Training: 0.11728310585[s]
-2016-09-06 08:07:24,346 DEBUG: Done: Training
-2016-09-06 08:07:24,346 DEBUG: Start: Predicting
-2016-09-06 08:07:24,352 DEBUG: Done: Predicting
-2016-09-06 08:07:24,352 DEBUG: Start: Getting Results
-2016-09-06 08:07:24,353 DEBUG: Done: Getting Results
-2016-09-06 08:07:24,353 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,353 INFO: Done: Result Analysis
-2016-09-06 08:07:24,354 DEBUG: Info: Time for Training: 0.124927043915[s]
-2016-09-06 08:07:24,354 DEBUG: Done: Training
-2016-09-06 08:07:24,354 DEBUG: Start: Predicting
-2016-09-06 08:07:24,359 DEBUG: Done: Predicting
-2016-09-06 08:07:24,360 DEBUG: Start: Getting Results
-2016-09-06 08:07:24,361 DEBUG: Done: Getting Results
-2016-09-06 08:07:24,361 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.644444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.644444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,361 INFO: Done: Result Analysis
-2016-09-06 08:07:24,480 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,480 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:07:24,480 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:24,481 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,481 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:07:24,481 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:24,481 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:24,481 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:24,481 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:24,482 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,482 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:24,482 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:24,482 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:24,482 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,552 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:24,552 DEBUG: Start: Training
-2016-09-06 08:07:24,554 DEBUG: Info: Time for Training: 0.0741329193115[s]
-2016-09-06 08:07:24,555 DEBUG: Done: Training
-2016-09-06 08:07:24,555 DEBUG: Start: Predicting
-2016-09-06 08:07:24,557 DEBUG: Done: Predicting
-2016-09-06 08:07:24,557 DEBUG: Start: Getting Results
-2016-09-06 08:07:24,558 DEBUG: Done: Getting Results
-2016-09-06 08:07:24,559 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,559 INFO: Done: Result Analysis
-2016-09-06 08:07:24,569 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:24,570 DEBUG: Start: Training
-2016-09-06 08:07:24,573 DEBUG: Info: Time for Training: 0.0938920974731[s]
-2016-09-06 08:07:24,574 DEBUG: Done: Training
-2016-09-06 08:07:24,574 DEBUG: Start: Predicting
-2016-09-06 08:07:24,576 DEBUG: Done: Predicting
-2016-09-06 08:07:24,577 DEBUG: Start: Getting Results
-2016-09-06 08:07:24,578 DEBUG: Done: Getting Results
-2016-09-06 08:07:24,578 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,578 INFO: Done: Result Analysis
-2016-09-06 08:07:24,728 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,728 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:24,729 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:07:24,729 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:07:24,729 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:24,729 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:24,730 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:24,730 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:24,730 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:24,730 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:24,730 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:24,730 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:24,730 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,730 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:24,793 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:24,793 DEBUG: Start: Training
-2016-09-06 08:07:24,794 DEBUG: Info: Time for Training: 0.0663559436798[s]
-2016-09-06 08:07:24,794 DEBUG: Done: Training
-2016-09-06 08:07:24,794 DEBUG: Start: Predicting
-2016-09-06 08:07:24,801 DEBUG: Done: Predicting
-2016-09-06 08:07:24,801 DEBUG: Start: Getting Results
-2016-09-06 08:07:24,803 DEBUG: Done: Getting Results
-2016-09-06 08:07:24,803 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.652380952381
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:07:24,803 INFO: Done: Result Analysis
-2016-09-06 08:07:25,043 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:25,044 DEBUG: Start: Training
-2016-09-06 08:07:25,083 DEBUG: Info: Time for Training: 0.35576415062[s]
-2016-09-06 08:07:25,083 DEBUG: Done: Training
-2016-09-06 08:07:25,083 DEBUG: Start: Predicting
-2016-09-06 08:07:25,088 DEBUG: Done: Predicting
-2016-09-06 08:07:25,088 DEBUG: Start: Getting Results
-2016-09-06 08:07:25,089 DEBUG: Done: Getting Results
-2016-09-06 08:07:25,089 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 10, max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.957142857143
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,089 INFO: Done: Result Analysis
-2016-09-06 08:07:25,174 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,174 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,174 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:07:25,174 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:07:25,174 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:25,174 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:25,175 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:25,175 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:25,175 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:25,175 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:25,175 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:25,175 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:25,176 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,176 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,253 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:25,253 DEBUG: Start: Training
-2016-09-06 08:07:25,254 DEBUG: Info: Time for Training: 0.0804960727692[s]
-2016-09-06 08:07:25,254 DEBUG: Done: Training
-2016-09-06 08:07:25,254 DEBUG: Start: Predicting
-2016-09-06 08:07:25,273 DEBUG: Done: Predicting
-2016-09-06 08:07:25,273 DEBUG: Start: Getting Results
-2016-09-06 08:07:25,274 DEBUG: Done: Getting Results
-2016-09-06 08:07:25,274 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,275 INFO: Done: Result Analysis
-2016-09-06 08:07:25,277 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:25,277 DEBUG: Start: Training
-2016-09-06 08:07:25,297 DEBUG: Info: Time for Training: 0.123291015625[s]
-2016-09-06 08:07:25,297 DEBUG: Done: Training
-2016-09-06 08:07:25,297 DEBUG: Start: Predicting
-2016-09-06 08:07:25,300 DEBUG: Done: Predicting
-2016-09-06 08:07:25,300 DEBUG: Start: Getting Results
-2016-09-06 08:07:25,301 DEBUG: Done: Getting Results
-2016-09-06 08:07:25,301 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,302 INFO: Done: Result Analysis
-2016-09-06 08:07:25,422 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,422 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,423 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:07:25,423 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:07:25,423 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:25,423 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:25,423 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:25,423 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:07:25,423 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:25,423 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:25,424 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:07:25,424 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,424 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:25,424 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,509 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:25,510 DEBUG: Start: Training
-2016-09-06 08:07:25,513 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:25,514 DEBUG: Start: Training
-2016-09-06 08:07:25,528 DEBUG: Info: Time for Training: 0.106248855591[s]
-2016-09-06 08:07:25,528 DEBUG: Done: Training
-2016-09-06 08:07:25,528 DEBUG: Start: Predicting
-2016-09-06 08:07:25,531 DEBUG: Info: Time for Training: 0.109098911285[s]
-2016-09-06 08:07:25,531 DEBUG: Done: Training
-2016-09-06 08:07:25,531 DEBUG: Start: Predicting
-2016-09-06 08:07:25,535 DEBUG: Done: Predicting
-2016-09-06 08:07:25,535 DEBUG: Start: Getting Results
-2016-09-06 08:07:25,535 DEBUG: Done: Predicting
-2016-09-06 08:07:25,535 DEBUG: Start: Getting Results
-2016-09-06 08:07:25,536 DEBUG: Done: Getting Results
-2016-09-06 08:07:25,536 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,536 INFO: Done: Result Analysis
-2016-09-06 08:07:25,537 DEBUG: Done: Getting Results
-2016-09-06 08:07:25,537 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,537 INFO: Done: Result Analysis
-2016-09-06 08:07:25,671 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,671 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:07:25,672 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:25,672 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,672 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:07:25,672 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:25,673 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:25,673 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:25,673 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:25,673 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:25,673 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:25,673 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:25,673 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,673 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,732 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:25,732 DEBUG: Start: Training
-2016-09-06 08:07:25,734 DEBUG: Info: Time for Training: 0.0624670982361[s]
-2016-09-06 08:07:25,734 DEBUG: Done: Training
-2016-09-06 08:07:25,734 DEBUG: Start: Predicting
-2016-09-06 08:07:25,736 DEBUG: Done: Predicting
-2016-09-06 08:07:25,737 DEBUG: Start: Getting Results
-2016-09-06 08:07:25,738 DEBUG: Done: Getting Results
-2016-09-06 08:07:25,738 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,738 INFO: Done: Result Analysis
-2016-09-06 08:07:25,759 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:25,759 DEBUG: Start: Training
-2016-09-06 08:07:25,763 DEBUG: Info: Time for Training: 0.0922710895538[s]
-2016-09-06 08:07:25,763 DEBUG: Done: Training
-2016-09-06 08:07:25,763 DEBUG: Start: Predicting
-2016-09-06 08:07:25,766 DEBUG: Done: Predicting
-2016-09-06 08:07:25,766 DEBUG: Start: Getting Results
-2016-09-06 08:07:25,768 DEBUG: Done: Getting Results
-2016-09-06 08:07:25,768 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,768 INFO: Done: Result Analysis
-2016-09-06 08:07:25,919 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,919 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:25,919 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:07:25,919 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:07:25,919 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:25,919 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:25,920 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:25,920 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:25,920 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:25,920 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:25,920 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:25,920 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:25,920 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,920 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:25,974 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:25,974 DEBUG: Start: Training
-2016-09-06 08:07:25,975 DEBUG: Info: Time for Training: 0.0564980506897[s]
-2016-09-06 08:07:25,975 DEBUG: Done: Training
-2016-09-06 08:07:25,975 DEBUG: Start: Predicting
-2016-09-06 08:07:25,981 DEBUG: Done: Predicting
-2016-09-06 08:07:25,981 DEBUG: Start: Getting Results
-2016-09-06 08:07:25,982 DEBUG: Done: Getting Results
-2016-09-06 08:07:25,983 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:07:25,983 INFO: Done: Result Analysis
-2016-09-06 08:07:26,231 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:26,231 DEBUG: Start: Training
-2016-09-06 08:07:26,249 DEBUG: Info: Time for Training: 0.331297874451[s]
-2016-09-06 08:07:26,250 DEBUG: Done: Training
-2016-09-06 08:07:26,250 DEBUG: Start: Predicting
-2016-09-06 08:07:26,254 DEBUG: Done: Predicting
-2016-09-06 08:07:26,254 DEBUG: Start: Getting Results
-2016-09-06 08:07:26,255 DEBUG: Done: Getting Results
-2016-09-06 08:07:26,255 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:07:26,255 INFO: Done: Result Analysis
-2016-09-06 08:07:26,364 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:26,364 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:07:26,364 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:07:26,364 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:07:26,364 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:26,364 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:07:26,365 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:26,365 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 08:07:26,365 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:26,365 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 08:07:26,366 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:26,366 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:07:26,366 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:26,366 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:07:26,446 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:26,446 DEBUG: Start: Training
-2016-09-06 08:07:26,447 DEBUG: Info: Time for Training: 0.0832591056824[s]
-2016-09-06 08:07:26,447 DEBUG: Done: Training
-2016-09-06 08:07:26,447 DEBUG: Start: Predicting
-2016-09-06 08:07:26,454 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:07:26,454 DEBUG: Start: Training
-2016-09-06 08:07:26,466 DEBUG: Done: Predicting
-2016-09-06 08:07:26,466 DEBUG: Start: Getting Results
-2016-09-06 08:07:26,467 DEBUG: Done: Getting Results
-2016-09-06 08:07:26,467 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:07:26,467 INFO: Done: Result Analysis
-2016-09-06 08:07:26,472 DEBUG: Info: Time for Training: 0.108740091324[s]
-2016-09-06 08:07:26,472 DEBUG: Done: Training
-2016-09-06 08:07:26,473 DEBUG: Start: Predicting
-2016-09-06 08:07:26,478 DEBUG: Done: Predicting
-2016-09-06 08:07:26,479 DEBUG: Start: Getting Results
-2016-09-06 08:07:26,480 DEBUG: Done: Getting Results
-2016-09-06 08:07:26,480 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:07:26,480 INFO: Done: Result Analysis
-2016-09-06 08:07:26,764 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:07:26,764 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:07:26,765 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:07:26,765 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:07:26,765 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:07:26,765 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:07:26,766 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:07:26,766 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:07:26,767 INFO: Info: Shape of View2 :(300, 13)
-2016-09-06 08:07:26,767 INFO: Info: Shape of View2 :(300, 13)
-2016-09-06 08:07:26,768 INFO: Info: Shape of View3 :(300, 9)
-2016-09-06 08:07:26,768 INFO: Info: Shape of View3 :(300, 9)
-2016-09-06 08:07:26,768 INFO: Done: Read Database Files
-2016-09-06 08:07:26,768 INFO: Done: Read Database Files
-2016-09-06 08:07:26,768 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:07:26,768 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:07:26,776 INFO: Done: Determine validation split
-2016-09-06 08:07:26,776 INFO: Done: Determine validation split
-2016-09-06 08:07:26,776 INFO: Start: Determine 5 folds
-2016-09-06 08:07:26,776 INFO: Start: Determine 5 folds
-2016-09-06 08:07:26,786 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:07:26,786 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:07:26,787 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:07:26,787 INFO: Done: Determine folds
-2016-09-06 08:07:26,787 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:07:26,787 INFO: Start: Classification
-2016-09-06 08:07:26,787 INFO: Start: Fold number 1
-2016-09-06 08:07:26,788 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:07:26,788 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:07:26,789 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:07:26,789 INFO: Done: Determine folds
-2016-09-06 08:07:26,789 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:07:26,789 INFO: Start: Classification
-2016-09-06 08:07:26,789 INFO: Start: Fold number 1
-2016-09-06 08:07:26,842 DEBUG: Start: Iteration 1
-2016-09-06 08:07:26,855 DEBUG: View 0 : 0.412087912088
-2016-09-06 08:07:26,867 DEBUG: View 1 : 0.412087912088
-2016-09-06 08:07:26,879 DEBUG: View 2 : 0.412087912088
-2016-09-06 08:07:26,889 DEBUG: View 3 : 0.412087912088
-2016-09-06 08:07:26,889 WARNING: WARNING: All bad for iteration 0
-2016-09-06 08:07:26,891 INFO: Start: Classification
-2016-09-06 08:07:26,952 INFO: Done: Fold number 1
-2016-09-06 08:07:26,953 INFO: Start: Fold number 2
-2016-09-06 08:07:26,954 DEBUG: Best view : View0
-2016-09-06 08:07:27,056 INFO: Start: Classification
-2016-09-06 08:07:27,077 DEBUG: Start: Iteration 2
-2016-09-06 08:07:27,091 DEBUG: View 0 : 0.725274725275
-2016-09-06 08:07:27,104 DEBUG: View 1 : 0.703296703297
-2016-09-06 08:07:27,117 INFO: Done: Fold number 2
-2016-09-06 08:07:27,117 INFO: Start: Fold number 3
-2016-09-06 08:07:27,117 DEBUG: View 2 : 0.725274725275
-2016-09-06 08:07:27,131 DEBUG: View 3 : 0.653846153846
-2016-09-06 08:07:27,203 DEBUG: Best view : View0
-2016-09-06 08:07:27,218 INFO: Start: Classification
-2016-09-06 08:07:27,255 INFO: Done: Fold number 3
-2016-09-06 08:07:27,255 INFO: Start: Fold number 4
-2016-09-06 08:07:27,320 INFO: Start: Classification
-2016-09-06 08:07:27,355 INFO: Done: Fold number 4
-2016-09-06 08:07:27,355 INFO: Start: Fold number 5
-2016-09-06 08:07:27,420 INFO: Start: Classification
-2016-09-06 08:07:27,454 INFO: Done: Fold number 5
-2016-09-06 08:07:27,454 INFO: Done: Classification
-2016-09-06 08:07:27,454 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:07:27,454 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:07:27,458 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 59.262305365
- -On Test : 55.6097560976
- -On Validation : 57.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - SVM Linear with C : 5372
- - SVM Linear with C : 5372
- - K nearest Neighbors with n_neighbors: 31
- - SVM Linear with C : 5372
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:07:27,459 INFO: Done: Result Analysis
-2016-09-06 08:07:27,568 DEBUG: Start: Iteration 3
-2016-09-06 08:07:27,578 DEBUG: View 0 : 0.725274725275
-2016-09-06 08:07:27,587 DEBUG: View 1 : 0.703296703297
-2016-09-06 08:07:27,594 DEBUG: View 2 : 0.725274725275
-2016-09-06 08:07:27,601 DEBUG: View 3 : 0.653846153846
-2016-09-06 08:07:27,643 DEBUG: Best view : View0
-2016-09-06 08:07:27,903 DEBUG: Start: Iteration 4
-2016-09-06 08:07:27,910 DEBUG: View 0 : 0.708791208791
-2016-09-06 08:07:27,917 DEBUG: View 1 : 0.642857142857
-2016-09-06 08:07:27,925 DEBUG: View 2 : 0.714285714286
-2016-09-06 08:07:27,932 DEBUG: View 3 : 0.642857142857
-2016-09-06 08:07:27,977 DEBUG: Best view : View0
-2016-09-06 08:07:28,265 INFO: Start: Classification
-2016-09-06 08:07:28,735 INFO: Done: Fold number 1
-2016-09-06 08:07:28,735 INFO: Start: Fold number 2
-2016-09-06 08:07:28,764 DEBUG: Start: Iteration 1
-2016-09-06 08:07:28,771 DEBUG: View 0 : 0.576923076923
-2016-09-06 08:07:28,778 DEBUG: View 1 : 0.576923076923
-2016-09-06 08:07:28,784 DEBUG: View 2 : 0.576923076923
-2016-09-06 08:07:28,791 DEBUG: View 3 : 0.576923076923
-2016-09-06 08:07:28,823 DEBUG: Best view : View0
-2016-09-06 08:07:28,902 DEBUG: Start: Iteration 2
-2016-09-06 08:07:28,910 DEBUG: View 0 : 0.736263736264
-2016-09-06 08:07:28,917 DEBUG: View 1 : 0.692307692308
-2016-09-06 08:07:28,924 DEBUG: View 2 : 0.681318681319
-2016-09-06 08:07:28,938 DEBUG: View 3 : 0.642857142857
-2016-09-06 08:07:28,979 DEBUG: Best view : View0
-2016-09-06 08:07:29,128 DEBUG: Start: Iteration 3
-2016-09-06 08:07:29,135 DEBUG: View 0 : 0.736263736264
-2016-09-06 08:07:29,143 DEBUG: View 1 : 0.692307692308
-2016-09-06 08:07:29,150 DEBUG: View 2 : 0.681318681319
-2016-09-06 08:07:29,157 DEBUG: View 3 : 0.642857142857
-2016-09-06 08:07:29,198 DEBUG: Best view : View0
-2016-09-06 08:07:29,430 DEBUG: Start: Iteration 4
-2016-09-06 08:07:29,438 DEBUG: View 0 : 0.708791208791
-2016-09-06 08:07:29,445 DEBUG: View 1 : 0.675824175824
-2016-09-06 08:07:29,453 DEBUG: View 2 : 0.659340659341
-2016-09-06 08:07:29,460 DEBUG: View 3 : 0.571428571429
-2016-09-06 08:07:29,504 DEBUG: Best view : View0
-2016-09-06 08:07:29,794 INFO: Start: Classification
-2016-09-06 08:07:30,272 INFO: Done: Fold number 2
-2016-09-06 08:07:30,272 INFO: Start: Fold number 3
-2016-09-06 08:07:30,304 DEBUG: Start: Iteration 1
-2016-09-06 08:07:30,311 DEBUG: View 0 : 0.582857142857
-2016-09-06 08:07:30,318 DEBUG: View 1 : 0.582857142857
-2016-09-06 08:07:30,326 DEBUG: View 2 : 0.582857142857
-2016-09-06 08:07:30,333 DEBUG: View 3 : 0.582857142857
-2016-09-06 08:07:30,367 DEBUG: Best view : View0
-2016-09-06 08:07:30,447 DEBUG: Start: Iteration 2
-2016-09-06 08:07:30,455 DEBUG: View 0 : 0.702857142857
-2016-09-06 08:07:30,463 DEBUG: View 1 : 0.691428571429
-2016-09-06 08:07:30,471 DEBUG: View 2 : 0.737142857143
-2016-09-06 08:07:30,479 DEBUG: View 3 : 0.725714285714
-2016-09-06 08:07:30,520 DEBUG: Best view : View2
-2016-09-06 08:07:30,672 DEBUG: Start: Iteration 3
-2016-09-06 08:07:30,680 DEBUG: View 0 : 0.702857142857
-2016-09-06 08:07:30,688 DEBUG: View 1 : 0.691428571429
-2016-09-06 08:07:30,695 DEBUG: View 2 : 0.737142857143
-2016-09-06 08:07:30,703 DEBUG: View 3 : 0.725714285714
-2016-09-06 08:07:30,744 DEBUG: Best view : View2
-2016-09-06 08:07:30,957 DEBUG: Start: Iteration 4
-2016-09-06 08:07:30,964 DEBUG: View 0 : 0.742857142857
-2016-09-06 08:07:30,971 DEBUG: View 1 : 0.725714285714
-2016-09-06 08:07:30,978 DEBUG: View 2 : 0.708571428571
-2016-09-06 08:07:30,985 DEBUG: View 3 : 0.657142857143
-2016-09-06 08:07:31,027 DEBUG: Best view : View0
-2016-09-06 08:07:31,306 INFO: Start: Classification
-2016-09-06 08:07:31,772 INFO: Done: Fold number 3
-2016-09-06 08:07:31,773 INFO: Start: Fold number 4
-2016-09-06 08:07:31,804 DEBUG: Start: Iteration 1
-2016-09-06 08:07:31,811 DEBUG: View 0 : 0.41847826087
-2016-09-06 08:07:31,817 DEBUG: View 1 : 0.41847826087
-2016-09-06 08:07:31,824 DEBUG: View 2 : 0.41847826087
-2016-09-06 08:07:31,831 DEBUG: View 3 : 0.41847826087
-2016-09-06 08:07:31,831 WARNING: WARNING: All bad for iteration 0
-2016-09-06 08:07:31,863 DEBUG: Best view : View0
-2016-09-06 08:07:31,945 DEBUG: Start: Iteration 2
-2016-09-06 08:07:31,952 DEBUG: View 0 : 0.717391304348
-2016-09-06 08:07:31,960 DEBUG: View 1 : 0.739130434783
-2016-09-06 08:07:31,967 DEBUG: View 2 : 0.66847826087
-2016-09-06 08:07:31,974 DEBUG: View 3 : 0.673913043478
-2016-09-06 08:07:32,013 DEBUG: Best view : View1
-2016-09-06 08:07:32,165 DEBUG: Start: Iteration 3
-2016-09-06 08:07:32,173 DEBUG: View 0 : 0.717391304348
-2016-09-06 08:07:32,180 DEBUG: View 1 : 0.739130434783
-2016-09-06 08:07:32,187 DEBUG: View 2 : 0.66847826087
-2016-09-06 08:07:32,194 DEBUG: View 3 : 0.673913043478
-2016-09-06 08:07:32,238 DEBUG: Best view : View1
-2016-09-06 08:07:32,460 DEBUG: Start: Iteration 4
-2016-09-06 08:07:32,467 DEBUG: View 0 : 0.760869565217
-2016-09-06 08:07:32,474 DEBUG: View 1 : 0.701086956522
-2016-09-06 08:07:32,482 DEBUG: View 2 : 0.695652173913
-2016-09-06 08:07:32,489 DEBUG: View 3 : 0.641304347826
-2016-09-06 08:07:32,533 DEBUG: Best view : View0
-2016-09-06 08:07:32,825 INFO: Start: Classification
-2016-09-06 08:07:33,307 INFO: Done: Fold number 4
-2016-09-06 08:07:33,307 INFO: Start: Fold number 5
-2016-09-06 08:07:33,337 DEBUG: Start: Iteration 1
-2016-09-06 08:07:33,344 DEBUG: View 0 : 0.579787234043
-2016-09-06 08:07:33,351 DEBUG: View 1 : 0.579787234043
-2016-09-06 08:07:33,358 DEBUG: View 2 : 0.579787234043
-2016-09-06 08:07:33,364 DEBUG: View 3 : 0.579787234043
-2016-09-06 08:07:33,398 DEBUG: Best view : View0
-2016-09-06 08:07:33,484 DEBUG: Start: Iteration 2
-2016-09-06 08:07:33,492 DEBUG: View 0 : 0.728723404255
-2016-09-06 08:07:33,499 DEBUG: View 1 : 0.696808510638
-2016-09-06 08:07:33,506 DEBUG: View 2 : 0.632978723404
-2016-09-06 08:07:33,513 DEBUG: View 3 : 0.696808510638
-2016-09-06 08:07:33,553 DEBUG: Best view : View0
-2016-09-06 08:07:33,709 DEBUG: Start: Iteration 3
-2016-09-06 08:07:33,717 DEBUG: View 0 : 0.728723404255
-2016-09-06 08:07:33,725 DEBUG: View 1 : 0.696808510638
-2016-09-06 08:07:33,733 DEBUG: View 2 : 0.632978723404
-2016-09-06 08:07:33,740 DEBUG: View 3 : 0.696808510638
-2016-09-06 08:07:33,783 DEBUG: Best view : View0
-2016-09-06 08:07:34,011 DEBUG: Start: Iteration 4
-2016-09-06 08:07:34,019 DEBUG: View 0 : 0.755319148936
-2016-09-06 08:07:34,026 DEBUG: View 1 : 0.664893617021
-2016-09-06 08:07:34,033 DEBUG: View 2 : 0.664893617021
-2016-09-06 08:07:34,040 DEBUG: View 3 : 0.617021276596
-2016-09-06 08:07:34,085 DEBUG: Best view : View0
-2016-09-06 08:07:34,384 INFO: Start: Classification
-2016-09-06 08:07:34,871 INFO: Done: Fold number 5
-2016-09-06 08:07:34,871 INFO: Done: Classification
-2016-09-06 08:07:34,871 INFO: Info: Time for Classification: 8[s]
-2016-09-06 08:07:34,871 INFO: Start: Result Analysis for Mumbo
-2016-09-06 08:07:37,434 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 73.3307031544
- -On Test : 49.756097561
- -On Validation : 67.5555555556Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 14), View1 of shape (300, 10), View2 of shape (300, 13), View3 of shape (300, 9)
- -5 folds
- - Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:03 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:06 0:00:00
- Fold 5 0:00:07 0:00:00
- Total 0:00:22 0:00:02
- So a total classification time of 0:00:08.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.257142857143
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.246153846154
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.257692307692
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.236263736264
- - Percentage of time chosen : 0.0
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.275824175824
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.263736263736
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.25989010989
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.243406593407
- - Percentage of time chosen : 0.0
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.273142857143
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.269142857143
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.276571428571
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.269142857143
- - Percentage of time chosen : 0.0
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.261413043478
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.259782608696
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.245108695652
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.240760869565
- - Percentage of time chosen : 0.0
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.279255319149
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.263829787234
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.251063829787
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.259042553191
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 41.2087912088
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 2
- Accuracy on train : 42.3076923077
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 3
- Accuracy on train : 41.7142857143
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 4
- Accuracy on train : 41.847826087
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 5
- Accuracy on train : 42.0212765957
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 72.5274725275
- Accuracy on test : 0.0
- Accuracy on validation : 62.2222222222
- Selected View : View0
- Fold 2
- Accuracy on train : 73.6263736264
- Accuracy on test : 0.0
- Accuracy on validation : 65.5555555556
- Selected View : View0
- Fold 3
- Accuracy on train : 73.7142857143
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View2
- Fold 4
- Accuracy on train : 73.9130434783
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View1
- Fold 5
- Accuracy on train : 72.8723404255
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- - Iteration 3
- Fold 1
- Accuracy on train : 72.5274725275
- Accuracy on test : 0.0
- Accuracy on validation : 62.2222222222
- Selected View : View0
- Fold 2
- Accuracy on train : 73.6263736264
- Accuracy on test : 0.0
- Accuracy on validation : 65.5555555556
- Selected View : View0
- Fold 3
- Accuracy on train : 73.7142857143
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View2
- Fold 4
- Accuracy on train : 73.9130434783
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View1
- Fold 5
- Accuracy on train : 72.8723404255
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- - Iteration 4
- Fold 1
- Accuracy on train : 72.5274725275
- Accuracy on test : 0.0
- Accuracy on validation : 62.2222222222
- Selected View : View0
- Fold 2
- Accuracy on train : 73.6263736264
- Accuracy on test : 0.0
- Accuracy on validation : 65.5555555556
- Selected View : View0
- Fold 3
- Accuracy on train : 73.7142857143
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View0
- Fold 4
- Accuracy on train : 73.9130434783
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- Fold 5
- Accuracy on train : 72.8723404255
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- - Iteration 5
- Fold 1
- Accuracy on train : 41.2087912088
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 2
- Accuracy on train : 42.3076923077
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 3
- Accuracy on train : 41.7142857143
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 4
- Accuracy on train : 41.847826087
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 5
- Accuracy on train : 42.0212765957
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
-2016-09-06 08:07:37,700 INFO: Done: Result Analysis
-2016-09-06 08:07:37,830 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:07:37,831 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:07:37,831 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:07:37,831 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:07:37,831 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:07:37,831 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:07:37,832 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:07:37,832 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:07:37,832 INFO: Info: Shape of View2 :(300, 13)
-2016-09-06 08:07:37,832 INFO: Info: Shape of View2 :(300, 13)
-2016-09-06 08:07:37,833 INFO: Info: Shape of View3 :(300, 9)
-2016-09-06 08:07:37,833 INFO: Info: Shape of View3 :(300, 9)
-2016-09-06 08:07:37,833 INFO: Done: Read Database Files
-2016-09-06 08:07:37,833 INFO: Done: Read Database Files
-2016-09-06 08:07:37,833 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:07:37,833 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:07:37,837 INFO: Done: Determine validation split
-2016-09-06 08:07:37,837 INFO: Start: Determine 5 folds
-2016-09-06 08:07:37,839 INFO: Done: Determine validation split
-2016-09-06 08:07:37,839 INFO: Start: Determine 5 folds
-2016-09-06 08:07:37,845 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:07:37,845 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:07:37,845 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:07:37,846 INFO: Done: Determine folds
-2016-09-06 08:07:37,846 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:07:37,846 INFO: Start: Classification
-2016-09-06 08:07:37,846 INFO: Start: Fold number 1
-2016-09-06 08:07:37,846 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:07:37,846 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:07:37,846 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:07:37,846 INFO: Done: Determine folds
-2016-09-06 08:07:37,847 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:07:37,847 INFO: Start: Classification
-2016-09-06 08:07:37,847 INFO: Start: Fold number 1
-2016-09-06 08:07:37,913 INFO: Start: Classification
-2016-09-06 08:07:37,948 INFO: Start: Classification
-2016-09-06 08:07:37,996 INFO: Done: Fold number 1
-2016-09-06 08:07:37,996 INFO: Start: Fold number 2
-2016-09-06 08:07:38,059 INFO: Start: Classification
-2016-09-06 08:07:38,143 INFO: Done: Fold number 2
-2016-09-06 08:07:38,143 INFO: Start: Fold number 3
-2016-09-06 08:07:38,210 INFO: Start: Classification
-2016-09-06 08:07:38,291 INFO: Done: Fold number 3
-2016-09-06 08:07:38,292 INFO: Start: Fold number 4
-2016-09-06 08:07:38,354 INFO: Start: Classification
-2016-09-06 08:07:38,433 INFO: Done: Fold number 4
-2016-09-06 08:07:38,433 INFO: Start: Fold number 5
-2016-09-06 08:07:38,497 INFO: Start: Classification
-2016-09-06 08:07:38,576 INFO: Done: Fold number 5
-2016-09-06 08:07:38,576 INFO: Done: Classification
-2016-09-06 08:07:38,576 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:07:38,576 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:07:38,581 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 96.1190234432
- -On Test : 51.2195121951
- -On Validation : 81.5555555556
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SVM Linear with C : 5372
- - SVM Linear with C : 5372
- - K nearest Neighbors with n_neighbors: 31
- - SVM Linear with C : 5372
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:07:38,581 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5d72613ca46183aa55e1de5b3ec1eccfe3a9922f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 071824583d238313018b9daa085c90ab6ed76218..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b21aab1da585f4ff9ccc74baa558c230a666f2f7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.661904761905
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a62664a6a18411dc1ccc9bd86df24d292c4ed038..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.952380952381
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a776fa3a23c2f83e32d25d410af832c0a953c00d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 90ec5cdd4e79111d08bfef107b0f050d38dbca0c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080722Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.438095238095
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.438095238095
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8b12b25b6fefa917db8246fc86ed2d5f86a6b2d8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 86262c36784369f2190a8652344e0719865d9704..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4acfa749678c58cc1b87eb80513f605c81afd14b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75cb380f2385316262fe817cd1ee2f0bc1b71433..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6fa7b0fa48de79a53c4277641f45551c0fe5b9e0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6f07c75d987a66bc99a650fbffaac0718de02f50..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080723Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b781bf5f3b149d64f5234cdf4ec95598f7b26e5c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8152c45e09cc9ad438bd221fcdb1423972814d2d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ec646fc888e4b710a61e7b546b0a99d752f8bc61..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.652380952381
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c70d609742b9cdc52191664aefd3e6b8862678f4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e3194ef94e281196e35aa3ff366d24982944d33b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ceeff8e1e29f65a396b47d4d80b2e7334d4adf1a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.644444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.644444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 106a3d98cb56e5435939a67544e6e8f7822a49d9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080724Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 308bfe6378b3f83816db75df1cd29aa0c828059b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2757a0f81557830e1f9e60b1a2cd32afc3e5635b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9cd6b78092e25867acd5cb2daf9d8a71ac2836fb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 31
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b28792ca6561102a3f3079fa7c80e05d1a78387e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 10, max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.957142857143
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cf748fb9ada5285f27d9e77604827a4f5fb6cf13..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7f27478ab4bee817e353513e726a9f0663547ee9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c388e77d0e04af637f94ae47fba793ee40f59576..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 36c54288a0668048403e3039751019fa21eb5379..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080725Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bbf9b155cb3391334082999e4fbcc2f7e0aa67e6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f2ce79715bb2b7a74b4ac6fb70d1a63baeb882f7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 91bd3e565fb55e4d9b3d15a19f72c92fb8d2be09..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080726Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5372
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080727Results-Fusion-LateFusion-BayesianInference-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080727Results-Fusion-LateFusion-BayesianInference-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5bdf5b85e7d1e759173e15772786b3911d070317..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080727Results-Fusion-LateFusion-BayesianInference-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 59.262305365
- -On Test : 55.6097560976
- -On Validation : 57.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - SVM Linear with C : 5372
- - SVM Linear with C : 5372
- - K nearest Neighbors with n_neighbors: 31
- - SVM Linear with C : 5372
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index c0a61fb8d4ae160471d9076e8e3b16fc4e5ff5af..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 9361b2c8fee53e1c0b007b81020fa8084cb71a47..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080737Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,235 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 73.3307031544
- -On Test : 49.756097561
- -On Validation : 67.5555555556Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 14), View1 of shape (300, 10), View2 of shape (300, 13), View3 of shape (300, 9)
- -5 folds
- - Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:03 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:06 0:00:00
- Fold 5 0:00:07 0:00:00
- Total 0:00:22 0:00:02
- So a total classification time of 0:00:08.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.257142857143
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.246153846154
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.257692307692
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.236263736264
- - Percentage of time chosen : 0.0
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.275824175824
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.263736263736
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.25989010989
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.243406593407
- - Percentage of time chosen : 0.0
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.273142857143
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.269142857143
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.276571428571
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.269142857143
- - Percentage of time chosen : 0.0
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.261413043478
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.259782608696
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.245108695652
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.240760869565
- - Percentage of time chosen : 0.0
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.279255319149
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.263829787234
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.251063829787
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.259042553191
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 41.2087912088
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 2
- Accuracy on train : 42.3076923077
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 3
- Accuracy on train : 41.7142857143
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 4
- Accuracy on train : 41.847826087
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 5
- Accuracy on train : 42.0212765957
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 72.5274725275
- Accuracy on test : 0.0
- Accuracy on validation : 62.2222222222
- Selected View : View0
- Fold 2
- Accuracy on train : 73.6263736264
- Accuracy on test : 0.0
- Accuracy on validation : 65.5555555556
- Selected View : View0
- Fold 3
- Accuracy on train : 73.7142857143
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View2
- Fold 4
- Accuracy on train : 73.9130434783
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View1
- Fold 5
- Accuracy on train : 72.8723404255
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- - Iteration 3
- Fold 1
- Accuracy on train : 72.5274725275
- Accuracy on test : 0.0
- Accuracy on validation : 62.2222222222
- Selected View : View0
- Fold 2
- Accuracy on train : 73.6263736264
- Accuracy on test : 0.0
- Accuracy on validation : 65.5555555556
- Selected View : View0
- Fold 3
- Accuracy on train : 73.7142857143
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View2
- Fold 4
- Accuracy on train : 73.9130434783
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View1
- Fold 5
- Accuracy on train : 72.8723404255
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- - Iteration 4
- Fold 1
- Accuracy on train : 72.5274725275
- Accuracy on test : 0.0
- Accuracy on validation : 62.2222222222
- Selected View : View0
- Fold 2
- Accuracy on train : 73.6263736264
- Accuracy on test : 0.0
- Accuracy on validation : 65.5555555556
- Selected View : View0
- Fold 3
- Accuracy on train : 73.7142857143
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View0
- Fold 4
- Accuracy on train : 73.9130434783
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- Fold 5
- Accuracy on train : 72.8723404255
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- - Iteration 5
- Fold 1
- Accuracy on train : 41.2087912088
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 2
- Accuracy on train : 42.3076923077
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 3
- Accuracy on train : 41.7142857143
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 4
- Accuracy on train : 41.847826087
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
- Fold 5
- Accuracy on train : 42.0212765957
- Accuracy on test : 0.0
- Accuracy on validation : 43.3333333333
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080738Results-Fusion-LateFusion-MajorityVoting-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080738Results-Fusion-LateFusion-MajorityVoting-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index fec3e54a26bb6e5fc9e87c474a513612f8ce0933..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080738Results-Fusion-LateFusion-MajorityVoting-SVMPoly-SVMPoly-KNN-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 96.1190234432
- -On Test : 51.2195121951
- -On Validation : 81.5555555556
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SVM Linear with C : 5372
- - SVM Linear with C : 5372
- - K nearest Neighbors with n_neighbors: 31
- - SVM Linear with C : 5372
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080839-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-080839-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index f9eb792511033732d217aecb017bcbf4d4dcee29..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080839-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1840 +0,0 @@
-2016-09-06 08:08:39,145 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:08:39,145 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00014040625 Gbytes /!\
-2016-09-06 08:08:44,159 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:08:44,161 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:08:44,213 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:44,213 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:44,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:08:44,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:08:44,213 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:44,213 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:44,214 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:44,214 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:44,214 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:44,214 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:44,214 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:44,214 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:44,214 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:44,214 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:44,272 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:44,272 DEBUG: Start: Training
-2016-09-06 08:08:44,274 DEBUG: Info: Time for Training: 0.0613279342651[s]
-2016-09-06 08:08:44,274 DEBUG: Done: Training
-2016-09-06 08:08:44,274 DEBUG: Start: Predicting
-2016-09-06 08:08:44,276 DEBUG: Done: Predicting
-2016-09-06 08:08:44,276 DEBUG: Start: Getting Results
-2016-09-06 08:08:44,277 DEBUG: Done: Getting Results
-2016-09-06 08:08:44,278 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:08:44,278 INFO: Done: Result Analysis
-2016-09-06 08:08:44,308 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:44,309 DEBUG: Start: Training
-2016-09-06 08:08:44,313 DEBUG: Info: Time for Training: 0.100487947464[s]
-2016-09-06 08:08:44,313 DEBUG: Done: Training
-2016-09-06 08:08:44,313 DEBUG: Start: Predicting
-2016-09-06 08:08:44,315 DEBUG: Done: Predicting
-2016-09-06 08:08:44,316 DEBUG: Start: Getting Results
-2016-09-06 08:08:44,317 DEBUG: Done: Getting Results
-2016-09-06 08:08:44,317 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:08:44,317 INFO: Done: Result Analysis
-2016-09-06 08:08:44,464 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:44,464 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:08:44,464 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:44,464 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:44,465 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:08:44,465 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:44,465 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:44,466 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:44,466 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:44,466 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:44,466 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:44,466 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:44,466 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:44,466 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:44,519 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:44,519 DEBUG: Start: Training
-2016-09-06 08:08:44,520 DEBUG: Info: Time for Training: 0.0568120479584[s]
-2016-09-06 08:08:44,520 DEBUG: Done: Training
-2016-09-06 08:08:44,520 DEBUG: Start: Predicting
-2016-09-06 08:08:44,527 DEBUG: Done: Predicting
-2016-09-06 08:08:44,528 DEBUG: Start: Getting Results
-2016-09-06 08:08:44,529 DEBUG: Done: Getting Results
-2016-09-06 08:08:44,529 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 49
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:44,529 INFO: Done: Result Analysis
-2016-09-06 08:08:45,064 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:45,064 DEBUG: Start: Training
-2016-09-06 08:08:45,119 DEBUG: Info: Time for Training: 0.655100107193[s]
-2016-09-06 08:08:45,119 DEBUG: Done: Training
-2016-09-06 08:08:45,119 DEBUG: Start: Predicting
-2016-09-06 08:08:45,126 DEBUG: Done: Predicting
-2016-09-06 08:08:45,126 DEBUG: Start: Getting Results
-2016-09-06 08:08:45,127 DEBUG: Done: Getting Results
-2016-09-06 08:08:45,127 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 21, max_depth : 29
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,127 INFO: Done: Result Analysis
-2016-09-06 08:08:45,213 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,213 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:08:45,213 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:08:45,214 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:45,214 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:45,214 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:45,214 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:45,214 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:45,214 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:45,215 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:45,215 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:45,215 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,215 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,299 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:45,299 DEBUG: Start: Training
-2016-09-06 08:08:45,300 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:45,300 DEBUG: Start: Training
-2016-09-06 08:08:45,301 DEBUG: Info: Time for Training: 0.0881788730621[s]
-2016-09-06 08:08:45,301 DEBUG: Done: Training
-2016-09-06 08:08:45,301 DEBUG: Start: Predicting
-2016-09-06 08:08:45,310 DEBUG: Done: Predicting
-2016-09-06 08:08:45,310 DEBUG: Start: Getting Results
-2016-09-06 08:08:45,311 DEBUG: Done: Getting Results
-2016-09-06 08:08:45,311 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,312 INFO: Done: Result Analysis
-2016-09-06 08:08:45,318 DEBUG: Info: Time for Training: 0.105075836182[s]
-2016-09-06 08:08:45,318 DEBUG: Done: Training
-2016-09-06 08:08:45,318 DEBUG: Start: Predicting
-2016-09-06 08:08:45,321 DEBUG: Done: Predicting
-2016-09-06 08:08:45,321 DEBUG: Start: Getting Results
-2016-09-06 08:08:45,322 DEBUG: Done: Getting Results
-2016-09-06 08:08:45,322 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,322 INFO: Done: Result Analysis
-2016-09-06 08:08:45,460 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,460 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,460 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:08:45,460 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:08:45,460 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:45,460 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:45,461 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:45,461 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:45,461 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:08:45,461 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:45,461 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:08:45,461 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,461 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:45,461 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,550 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:45,550 DEBUG: Start: Training
-2016-09-06 08:08:45,561 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:45,561 DEBUG: Start: Training
-2016-09-06 08:08:45,567 DEBUG: Info: Time for Training: 0.108205080032[s]
-2016-09-06 08:08:45,567 DEBUG: Done: Training
-2016-09-06 08:08:45,568 DEBUG: Start: Predicting
-2016-09-06 08:08:45,573 DEBUG: Done: Predicting
-2016-09-06 08:08:45,573 DEBUG: Start: Getting Results
-2016-09-06 08:08:45,574 DEBUG: Done: Getting Results
-2016-09-06 08:08:45,574 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,574 INFO: Done: Result Analysis
-2016-09-06 08:08:45,578 DEBUG: Info: Time for Training: 0.118767976761[s]
-2016-09-06 08:08:45,578 DEBUG: Done: Training
-2016-09-06 08:08:45,578 DEBUG: Start: Predicting
-2016-09-06 08:08:45,582 DEBUG: Done: Predicting
-2016-09-06 08:08:45,582 DEBUG: Start: Getting Results
-2016-09-06 08:08:45,583 DEBUG: Done: Getting Results
-2016-09-06 08:08:45,583 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,583 INFO: Done: Result Analysis
-2016-09-06 08:08:45,709 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,709 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:08:45,709 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,709 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:45,709 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:08:45,709 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:45,710 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:45,710 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:45,710 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:45,710 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:45,710 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:45,710 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,710 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:45,710 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,775 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:45,775 DEBUG: Start: Training
-2016-09-06 08:08:45,778 DEBUG: Info: Time for Training: 0.0693180561066[s]
-2016-09-06 08:08:45,778 DEBUG: Done: Training
-2016-09-06 08:08:45,778 DEBUG: Start: Predicting
-2016-09-06 08:08:45,780 DEBUG: Done: Predicting
-2016-09-06 08:08:45,780 DEBUG: Start: Getting Results
-2016-09-06 08:08:45,781 DEBUG: Done: Getting Results
-2016-09-06 08:08:45,781 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,782 INFO: Done: Result Analysis
-2016-09-06 08:08:45,801 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:45,801 DEBUG: Start: Training
-2016-09-06 08:08:45,806 DEBUG: Info: Time for Training: 0.0974469184875[s]
-2016-09-06 08:08:45,806 DEBUG: Done: Training
-2016-09-06 08:08:45,806 DEBUG: Start: Predicting
-2016-09-06 08:08:45,809 DEBUG: Done: Predicting
-2016-09-06 08:08:45,809 DEBUG: Start: Getting Results
-2016-09-06 08:08:45,811 DEBUG: Done: Getting Results
-2016-09-06 08:08:45,811 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:45,811 INFO: Done: Result Analysis
-2016-09-06 08:08:45,957 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,957 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:45,957 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:08:45,957 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:08:45,957 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:45,957 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:45,958 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:45,958 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:45,958 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:45,958 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:45,958 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:45,958 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:45,958 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:45,958 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:46,020 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:46,020 DEBUG: Start: Training
-2016-09-06 08:08:46,021 DEBUG: Info: Time for Training: 0.0644130706787[s]
-2016-09-06 08:08:46,021 DEBUG: Done: Training
-2016-09-06 08:08:46,021 DEBUG: Start: Predicting
-2016-09-06 08:08:46,029 DEBUG: Done: Predicting
-2016-09-06 08:08:46,029 DEBUG: Start: Getting Results
-2016-09-06 08:08:46,031 DEBUG: Done: Getting Results
-2016-09-06 08:08:46,031 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:08:46,031 INFO: Done: Result Analysis
-2016-09-06 08:08:46,554 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:46,554 DEBUG: Start: Training
-2016-09-06 08:08:46,597 DEBUG: Info: Time for Training: 0.641263008118[s]
-2016-09-06 08:08:46,598 DEBUG: Done: Training
-2016-09-06 08:08:46,598 DEBUG: Start: Predicting
-2016-09-06 08:08:46,604 DEBUG: Done: Predicting
-2016-09-06 08:08:46,604 DEBUG: Start: Getting Results
-2016-09-06 08:08:46,605 DEBUG: Done: Getting Results
-2016-09-06 08:08:46,605 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 16, max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:46,605 INFO: Done: Result Analysis
-2016-09-06 08:08:46,711 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:46,711 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:46,711 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:08:46,711 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:08:46,711 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:46,711 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:46,712 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:46,712 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:46,712 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:46,712 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:46,712 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:46,712 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:46,712 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:46,712 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:46,788 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:46,788 DEBUG: Start: Training
-2016-09-06 08:08:46,789 DEBUG: Info: Time for Training: 0.0784420967102[s]
-2016-09-06 08:08:46,789 DEBUG: Done: Training
-2016-09-06 08:08:46,789 DEBUG: Start: Predicting
-2016-09-06 08:08:46,800 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:46,800 DEBUG: Start: Training
-2016-09-06 08:08:46,812 DEBUG: Done: Predicting
-2016-09-06 08:08:46,812 DEBUG: Start: Getting Results
-2016-09-06 08:08:46,815 DEBUG: Done: Getting Results
-2016-09-06 08:08:46,815 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:46,816 INFO: Done: Result Analysis
-2016-09-06 08:08:46,831 DEBUG: Info: Time for Training: 0.120301008224[s]
-2016-09-06 08:08:46,831 DEBUG: Done: Training
-2016-09-06 08:08:46,831 DEBUG: Start: Predicting
-2016-09-06 08:08:46,834 DEBUG: Done: Predicting
-2016-09-06 08:08:46,835 DEBUG: Start: Getting Results
-2016-09-06 08:08:46,835 DEBUG: Done: Getting Results
-2016-09-06 08:08:46,836 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:08:46,836 INFO: Done: Result Analysis
-2016-09-06 08:08:46,957 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:46,957 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:46,957 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:08:46,957 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:08:46,957 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:46,957 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:46,958 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:46,958 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:08:46,958 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:46,958 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:08:46,958 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:46,958 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:46,959 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:46,959 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:47,044 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:47,044 DEBUG: Start: Training
-2016-09-06 08:08:47,054 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:47,055 DEBUG: Start: Training
-2016-09-06 08:08:47,064 DEBUG: Info: Time for Training: 0.107657909393[s]
-2016-09-06 08:08:47,064 DEBUG: Done: Training
-2016-09-06 08:08:47,064 DEBUG: Start: Predicting
-2016-09-06 08:08:47,070 DEBUG: Done: Predicting
-2016-09-06 08:08:47,070 DEBUG: Start: Getting Results
-2016-09-06 08:08:47,072 DEBUG: Done: Getting Results
-2016-09-06 08:08:47,072 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:08:47,072 INFO: Done: Result Analysis
-2016-09-06 08:08:47,073 DEBUG: Info: Time for Training: 0.116585016251[s]
-2016-09-06 08:08:47,073 DEBUG: Done: Training
-2016-09-06 08:08:47,073 DEBUG: Start: Predicting
-2016-09-06 08:08:47,077 DEBUG: Done: Predicting
-2016-09-06 08:08:47,077 DEBUG: Start: Getting Results
-2016-09-06 08:08:47,078 DEBUG: Done: Getting Results
-2016-09-06 08:08:47,078 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9418
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:47,078 INFO: Done: Result Analysis
-2016-09-06 08:08:47,207 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:47,207 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:08:47,207 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:47,208 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:47,208 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:47,208 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:08:47,208 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:47,208 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:47,208 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:47,209 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:47,209 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:47,210 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:47,210 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:47,210 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:47,283 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:47,283 DEBUG: Start: Training
-2016-09-06 08:08:47,286 DEBUG: Info: Time for Training: 0.0787489414215[s]
-2016-09-06 08:08:47,286 DEBUG: Done: Training
-2016-09-06 08:08:47,286 DEBUG: Start: Predicting
-2016-09-06 08:08:47,289 DEBUG: Done: Predicting
-2016-09-06 08:08:47,290 DEBUG: Start: Getting Results
-2016-09-06 08:08:47,291 DEBUG: Done: Getting Results
-2016-09-06 08:08:47,291 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:47,291 INFO: Done: Result Analysis
-2016-09-06 08:08:47,302 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:47,302 DEBUG: Start: Training
-2016-09-06 08:08:47,307 DEBUG: Info: Time for Training: 0.101135015488[s]
-2016-09-06 08:08:47,307 DEBUG: Done: Training
-2016-09-06 08:08:47,307 DEBUG: Start: Predicting
-2016-09-06 08:08:47,310 DEBUG: Done: Predicting
-2016-09-06 08:08:47,310 DEBUG: Start: Getting Results
-2016-09-06 08:08:47,312 DEBUG: Done: Getting Results
-2016-09-06 08:08:47,312 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:08:47,312 INFO: Done: Result Analysis
-2016-09-06 08:08:47,457 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:47,457 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:47,457 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:08:47,457 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:08:47,458 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:47,458 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:47,459 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:47,459 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:47,459 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:47,459 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:47,459 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:47,459 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:47,459 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:47,459 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:47,513 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:47,514 DEBUG: Start: Training
-2016-09-06 08:08:47,514 DEBUG: Info: Time for Training: 0.0577688217163[s]
-2016-09-06 08:08:47,514 DEBUG: Done: Training
-2016-09-06 08:08:47,514 DEBUG: Start: Predicting
-2016-09-06 08:08:47,523 DEBUG: Done: Predicting
-2016-09-06 08:08:47,523 DEBUG: Start: Getting Results
-2016-09-06 08:08:47,524 DEBUG: Done: Getting Results
-2016-09-06 08:08:47,524 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:47,524 INFO: Done: Result Analysis
-2016-09-06 08:08:48,130 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:48,130 DEBUG: Start: Training
-2016-09-06 08:08:48,193 DEBUG: Info: Time for Training: 0.736048936844[s]
-2016-09-06 08:08:48,193 DEBUG: Done: Training
-2016-09-06 08:08:48,193 DEBUG: Start: Predicting
-2016-09-06 08:08:48,201 DEBUG: Done: Predicting
-2016-09-06 08:08:48,201 DEBUG: Start: Getting Results
-2016-09-06 08:08:48,202 DEBUG: Done: Getting Results
-2016-09-06 08:08:48,203 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 21, max_depth : 29
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,203 INFO: Done: Result Analysis
-2016-09-06 08:08:48,315 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,315 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,316 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:08:48,316 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:08:48,316 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:48,316 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:48,316 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:48,316 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:48,317 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:48,317 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:48,317 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:48,317 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:48,317 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,317 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,391 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:48,391 DEBUG: Start: Training
-2016-09-06 08:08:48,391 DEBUG: Info: Time for Training: 0.0768928527832[s]
-2016-09-06 08:08:48,392 DEBUG: Done: Training
-2016-09-06 08:08:48,392 DEBUG: Start: Predicting
-2016-09-06 08:08:48,403 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:48,404 DEBUG: Start: Training
-2016-09-06 08:08:48,423 DEBUG: Done: Predicting
-2016-09-06 08:08:48,423 DEBUG: Start: Getting Results
-2016-09-06 08:08:48,424 DEBUG: Done: Getting Results
-2016-09-06 08:08:48,424 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,425 INFO: Done: Result Analysis
-2016-09-06 08:08:48,425 DEBUG: Info: Time for Training: 0.110107898712[s]
-2016-09-06 08:08:48,425 DEBUG: Done: Training
-2016-09-06 08:08:48,425 DEBUG: Start: Predicting
-2016-09-06 08:08:48,429 DEBUG: Done: Predicting
-2016-09-06 08:08:48,429 DEBUG: Start: Getting Results
-2016-09-06 08:08:48,430 DEBUG: Done: Getting Results
-2016-09-06 08:08:48,430 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,430 INFO: Done: Result Analysis
-2016-09-06 08:08:48,564 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:08:48,564 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:48,564 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:08:48,565 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:48,565 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:48,565 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:48,565 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:48,565 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,565 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:08:48,565 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:08:48,565 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:48,566 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,648 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:48,649 DEBUG: Start: Training
-2016-09-06 08:08:48,668 DEBUG: Info: Time for Training: 0.104422092438[s]
-2016-09-06 08:08:48,668 DEBUG: Done: Training
-2016-09-06 08:08:48,668 DEBUG: Start: Predicting
-2016-09-06 08:08:48,670 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:48,670 DEBUG: Start: Training
-2016-09-06 08:08:48,674 DEBUG: Done: Predicting
-2016-09-06 08:08:48,674 DEBUG: Start: Getting Results
-2016-09-06 08:08:48,675 DEBUG: Done: Getting Results
-2016-09-06 08:08:48,675 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,676 INFO: Done: Result Analysis
-2016-09-06 08:08:48,690 DEBUG: Info: Time for Training: 0.126782894135[s]
-2016-09-06 08:08:48,690 DEBUG: Done: Training
-2016-09-06 08:08:48,690 DEBUG: Start: Predicting
-2016-09-06 08:08:48,695 DEBUG: Done: Predicting
-2016-09-06 08:08:48,695 DEBUG: Start: Getting Results
-2016-09-06 08:08:48,697 DEBUG: Done: Getting Results
-2016-09-06 08:08:48,697 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9418
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,697 INFO: Done: Result Analysis
-2016-09-06 08:08:48,814 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,815 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:08:48,814 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:48,815 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:48,815 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:08:48,815 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:48,815 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:48,816 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:48,816 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:48,816 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,816 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:48,816 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:48,816 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:48,817 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:48,874 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:48,875 DEBUG: Start: Training
-2016-09-06 08:08:48,876 DEBUG: Info: Time for Training: 0.0622010231018[s]
-2016-09-06 08:08:48,876 DEBUG: Done: Training
-2016-09-06 08:08:48,876 DEBUG: Start: Predicting
-2016-09-06 08:08:48,878 DEBUG: Done: Predicting
-2016-09-06 08:08:48,878 DEBUG: Start: Getting Results
-2016-09-06 08:08:48,879 DEBUG: Done: Getting Results
-2016-09-06 08:08:48,880 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,880 INFO: Done: Result Analysis
-2016-09-06 08:08:48,902 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:48,903 DEBUG: Start: Training
-2016-09-06 08:08:48,906 DEBUG: Info: Time for Training: 0.092826128006[s]
-2016-09-06 08:08:48,907 DEBUG: Done: Training
-2016-09-06 08:08:48,907 DEBUG: Start: Predicting
-2016-09-06 08:08:48,909 DEBUG: Done: Predicting
-2016-09-06 08:08:48,909 DEBUG: Start: Getting Results
-2016-09-06 08:08:48,911 DEBUG: Done: Getting Results
-2016-09-06 08:08:48,911 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:08:48,911 INFO: Done: Result Analysis
-2016-09-06 08:08:49,061 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:49,061 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:08:49,061 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:49,062 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:49,062 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:49,062 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:49,062 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:08:49,062 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:49,062 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:49,062 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:49,063 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:49,063 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:49,063 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:49,063 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:49,113 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:49,113 DEBUG: Start: Training
-2016-09-06 08:08:49,114 DEBUG: Info: Time for Training: 0.0540471076965[s]
-2016-09-06 08:08:49,114 DEBUG: Done: Training
-2016-09-06 08:08:49,114 DEBUG: Start: Predicting
-2016-09-06 08:08:49,121 DEBUG: Done: Predicting
-2016-09-06 08:08:49,121 DEBUG: Start: Getting Results
-2016-09-06 08:08:49,122 DEBUG: Done: Getting Results
-2016-09-06 08:08:49,122 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 08:08:49,122 INFO: Done: Result Analysis
-2016-09-06 08:08:49,693 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:49,693 DEBUG: Start: Training
-2016-09-06 08:08:49,747 DEBUG: Info: Time for Training: 0.685566186905[s]
-2016-09-06 08:08:49,747 DEBUG: Done: Training
-2016-09-06 08:08:49,747 DEBUG: Start: Predicting
-2016-09-06 08:08:49,754 DEBUG: Done: Predicting
-2016-09-06 08:08:49,754 DEBUG: Start: Getting Results
-2016-09-06 08:08:49,755 DEBUG: Done: Getting Results
-2016-09-06 08:08:49,755 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 21, max_depth : 29
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:08:49,755 INFO: Done: Result Analysis
-2016-09-06 08:08:49,919 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:49,919 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:08:49,919 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:08:49,919 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:08:49,920 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:49,920 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:08:49,921 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:49,921 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:08:49,921 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:49,921 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:08:49,921 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:49,921 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:08:49,921 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:49,921 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:08:50,039 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:50,039 DEBUG: Start: Training
-2016-09-06 08:08:50,040 DEBUG: Info: Time for Training: 0.121908903122[s]
-2016-09-06 08:08:50,040 DEBUG: Done: Training
-2016-09-06 08:08:50,040 DEBUG: Start: Predicting
-2016-09-06 08:08:50,042 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:08:50,042 DEBUG: Start: Training
-2016-09-06 08:08:50,059 DEBUG: Info: Time for Training: 0.140678882599[s]
-2016-09-06 08:08:50,059 DEBUG: Done: Training
-2016-09-06 08:08:50,059 DEBUG: Start: Predicting
-2016-09-06 08:08:50,061 DEBUG: Done: Predicting
-2016-09-06 08:08:50,061 DEBUG: Start: Getting Results
-2016-09-06 08:08:50,061 DEBUG: Done: Predicting
-2016-09-06 08:08:50,061 DEBUG: Start: Getting Results
-2016-09-06 08:08:50,062 DEBUG: Done: Getting Results
-2016-09-06 08:08:50,062 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:50,062 INFO: Done: Result Analysis
-2016-09-06 08:08:50,063 DEBUG: Done: Getting Results
-2016-09-06 08:08:50,063 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:08:50,063 INFO: Done: Result Analysis
-2016-09-06 08:08:50,318 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:50,319 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:08:50,319 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:50,320 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:08:50,321 INFO: Info: Shape of View0 :(300, 10)
-2016-09-06 08:08:50,321 INFO: Info: Shape of View0 :(300, 10)
-2016-09-06 08:08:50,322 INFO: Info: Shape of View1 :(300, 18)
-2016-09-06 08:08:50,322 INFO: Info: Shape of View1 :(300, 18)
-2016-09-06 08:08:50,323 INFO: Info: Shape of View2 :(300, 19)
-2016-09-06 08:08:50,323 INFO: Info: Shape of View2 :(300, 19)
-2016-09-06 08:08:50,323 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 08:08:50,324 INFO: Done: Read Database Files
-2016-09-06 08:08:50,324 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 08:08:50,324 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:08:50,324 INFO: Done: Read Database Files
-2016-09-06 08:08:50,324 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:08:50,330 INFO: Done: Determine validation split
-2016-09-06 08:08:50,330 INFO: Done: Determine validation split
-2016-09-06 08:08:50,330 INFO: Start: Determine 5 folds
-2016-09-06 08:08:50,330 INFO: Start: Determine 5 folds
-2016-09-06 08:08:50,340 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:08:50,340 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:08:50,340 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:08:50,340 INFO: Done: Determine folds
-2016-09-06 08:08:50,340 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:08:50,340 INFO: Start: Classification
-2016-09-06 08:08:50,341 INFO: Start: Fold number 1
-2016-09-06 08:08:50,341 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:08:50,341 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:08:50,341 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:08:50,342 INFO: Done: Determine folds
-2016-09-06 08:08:50,342 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:08:50,342 INFO: Start: Classification
-2016-09-06 08:08:50,342 INFO: Start: Fold number 1
-2016-09-06 08:08:50,397 DEBUG: Start: Iteration 1
-2016-09-06 08:08:50,407 INFO: Start: Classification
-2016-09-06 08:08:50,410 DEBUG: View 0 : 0.418079096045
-2016-09-06 08:08:50,422 DEBUG: View 1 : 0.418079096045
-2016-09-06 08:08:50,433 DEBUG: View 2 : 0.418079096045
-2016-09-06 08:08:50,444 DEBUG: View 3 : 0.418079096045
-2016-09-06 08:08:50,444 WARNING: WARNING: All bad for iteration 0
-2016-09-06 08:08:50,481 INFO: Done: Fold number 1
-2016-09-06 08:08:50,481 INFO: Start: Fold number 2
-2016-09-06 08:08:50,508 DEBUG: Best view : View0
-2016-09-06 08:08:50,550 INFO: Start: Classification
-2016-09-06 08:08:50,626 INFO: Done: Fold number 2
-2016-09-06 08:08:50,626 INFO: Start: Fold number 3
-2016-09-06 08:08:50,627 DEBUG: Start: Iteration 2
-2016-09-06 08:08:50,640 DEBUG: View 0 : 0.723163841808
-2016-09-06 08:08:50,653 DEBUG: View 1 : 0.649717514124
-2016-09-06 08:08:50,665 DEBUG: View 2 : 0.683615819209
-2016-09-06 08:08:50,675 DEBUG: View 3 : 0.666666666667
-2016-09-06 08:08:50,691 INFO: Start: Classification
-2016-09-06 08:08:50,749 DEBUG: Best view : View0
-2016-09-06 08:08:50,767 INFO: Done: Fold number 3
-2016-09-06 08:08:50,768 INFO: Start: Fold number 4
-2016-09-06 08:08:50,831 INFO: Start: Classification
-2016-09-06 08:08:50,906 INFO: Done: Fold number 4
-2016-09-06 08:08:50,906 INFO: Start: Fold number 5
-2016-09-06 08:08:50,953 INFO: Start: Classification
-2016-09-06 08:08:50,965 DEBUG: Start: Iteration 3
-2016-09-06 08:08:50,972 DEBUG: View 0 : 0.723163841808
-2016-09-06 08:08:50,980 DEBUG: View 1 : 0.649717514124
-2016-09-06 08:08:50,988 DEBUG: View 2 : 0.683615819209
-2016-09-06 08:08:50,996 DEBUG: View 3 : 0.666666666667
-2016-09-06 08:08:50,999 INFO: Done: Fold number 5
-2016-09-06 08:08:50,999 INFO: Done: Classification
-2016-09-06 08:08:50,999 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:08:50,999 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:08:51,004 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 58.6889730599
- -On Test : 58.5365853659
- -On Validation : 58.8764044944
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 49
- - K nearest Neighbors with n_neighbors: 48
- - SVM Linear with C : 9418
- - K nearest Neighbors with n_neighbors: 48
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:08:51,004 INFO: Done: Result Analysis
-2016-09-06 08:08:51,042 DEBUG: Best view : View0
-2016-09-06 08:08:51,279 INFO: Start: Classification
-2016-09-06 08:08:51,627 INFO: Done: Fold number 1
-2016-09-06 08:08:51,627 INFO: Start: Fold number 2
-2016-09-06 08:08:51,658 DEBUG: Start: Iteration 1
-2016-09-06 08:08:51,664 DEBUG: View 0 : 0.424731182796
-2016-09-06 08:08:51,671 DEBUG: View 1 : 0.424731182796
-2016-09-06 08:08:51,678 DEBUG: View 2 : 0.424731182796
-2016-09-06 08:08:51,685 DEBUG: View 3 : 0.424731182796
-2016-09-06 08:08:51,685 WARNING: WARNING: All bad for iteration 0
-2016-09-06 08:08:51,718 DEBUG: Best view : View0
-2016-09-06 08:08:51,800 DEBUG: Start: Iteration 2
-2016-09-06 08:08:51,807 DEBUG: View 0 : 0.682795698925
-2016-09-06 08:08:51,815 DEBUG: View 1 : 0.731182795699
-2016-09-06 08:08:51,822 DEBUG: View 2 : 0.709677419355
-2016-09-06 08:08:51,829 DEBUG: View 3 : 0.666666666667
-2016-09-06 08:08:51,868 DEBUG: Best view : View1
-2016-09-06 08:08:52,019 DEBUG: Start: Iteration 3
-2016-09-06 08:08:52,026 DEBUG: View 0 : 0.682795698925
-2016-09-06 08:08:52,034 DEBUG: View 1 : 0.731182795699
-2016-09-06 08:08:52,041 DEBUG: View 2 : 0.709677419355
-2016-09-06 08:08:52,048 DEBUG: View 3 : 0.666666666667
-2016-09-06 08:08:52,090 DEBUG: Best view : View1
-2016-09-06 08:08:52,311 INFO: Start: Classification
-2016-09-06 08:08:52,667 INFO: Done: Fold number 2
-2016-09-06 08:08:52,667 INFO: Start: Fold number 3
-2016-09-06 08:08:52,697 DEBUG: Start: Iteration 1
-2016-09-06 08:08:52,704 DEBUG: View 0 : 0.421621621622
-2016-09-06 08:08:52,710 DEBUG: View 1 : 0.421621621622
-2016-09-06 08:08:52,717 DEBUG: View 2 : 0.421621621622
-2016-09-06 08:08:52,724 DEBUG: View 3 : 0.421621621622
-2016-09-06 08:08:52,724 WARNING: WARNING: All bad for iteration 0
-2016-09-06 08:08:52,757 DEBUG: Best view : View0
-2016-09-06 08:08:52,838 DEBUG: Start: Iteration 2
-2016-09-06 08:08:52,845 DEBUG: View 0 : 0.675675675676
-2016-09-06 08:08:52,853 DEBUG: View 1 : 0.713513513514
-2016-09-06 08:08:52,860 DEBUG: View 2 : 0.648648648649
-2016-09-06 08:08:52,867 DEBUG: View 3 : 0.664864864865
-2016-09-06 08:08:52,908 DEBUG: Best view : View1
-2016-09-06 08:08:53,059 DEBUG: Start: Iteration 3
-2016-09-06 08:08:53,066 DEBUG: View 0 : 0.675675675676
-2016-09-06 08:08:53,074 DEBUG: View 1 : 0.713513513514
-2016-09-06 08:08:53,081 DEBUG: View 2 : 0.648648648649
-2016-09-06 08:08:53,088 DEBUG: View 3 : 0.664864864865
-2016-09-06 08:08:53,131 DEBUG: Best view : View1
-2016-09-06 08:08:53,352 INFO: Start: Classification
-2016-09-06 08:08:53,708 INFO: Done: Fold number 3
-2016-09-06 08:08:53,708 INFO: Start: Fold number 4
-2016-09-06 08:08:53,752 DEBUG: Start: Iteration 1
-2016-09-06 08:08:53,761 DEBUG: View 0 : 0.585635359116
-2016-09-06 08:08:53,767 DEBUG: View 1 : 0.585635359116
-2016-09-06 08:08:53,775 DEBUG: View 2 : 0.585635359116
-2016-09-06 08:08:53,782 DEBUG: View 3 : 0.585635359116
-2016-09-06 08:08:53,819 DEBUG: Best view : View0
-2016-09-06 08:08:53,907 DEBUG: Start: Iteration 2
-2016-09-06 08:08:53,917 DEBUG: View 0 : 0.635359116022
-2016-09-06 08:08:53,924 DEBUG: View 1 : 0.729281767956
-2016-09-06 08:08:53,934 DEBUG: View 2 : 0.657458563536
-2016-09-06 08:08:53,940 DEBUG: View 3 : 0.685082872928
-2016-09-06 08:08:53,985 DEBUG: Best view : View1
-2016-09-06 08:08:54,147 DEBUG: Start: Iteration 3
-2016-09-06 08:08:54,156 DEBUG: View 0 : 0.635359116022
-2016-09-06 08:08:54,164 DEBUG: View 1 : 0.729281767956
-2016-09-06 08:08:54,173 DEBUG: View 2 : 0.657458563536
-2016-09-06 08:08:54,180 DEBUG: View 3 : 0.685082872928
-2016-09-06 08:08:54,228 DEBUG: Best view : View1
-2016-09-06 08:08:54,460 INFO: Start: Classification
-2016-09-06 08:08:54,814 INFO: Done: Fold number 4
-2016-09-06 08:08:54,814 INFO: Start: Fold number 5
-2016-09-06 08:08:54,844 DEBUG: Start: Iteration 1
-2016-09-06 08:08:54,851 DEBUG: View 0 : 0.406593406593
-2016-09-06 08:08:54,857 DEBUG: View 1 : 0.406593406593
-2016-09-06 08:08:54,864 DEBUG: View 2 : 0.406593406593
-2016-09-06 08:08:54,871 DEBUG: View 3 : 0.406593406593
-2016-09-06 08:08:54,871 WARNING: WARNING: All bad for iteration 0
-2016-09-06 08:08:54,904 DEBUG: Best view : View0
-2016-09-06 08:08:54,985 DEBUG: Start: Iteration 2
-2016-09-06 08:08:54,992 DEBUG: View 0 : 0.725274725275
-2016-09-06 08:08:55,000 DEBUG: View 1 : 0.747252747253
-2016-09-06 08:08:55,007 DEBUG: View 2 : 0.714285714286
-2016-09-06 08:08:55,014 DEBUG: View 3 : 0.675824175824
-2016-09-06 08:08:55,053 DEBUG: Best view : View1
-2016-09-06 08:08:55,204 DEBUG: Start: Iteration 3
-2016-09-06 08:08:55,211 DEBUG: View 0 : 0.725274725275
-2016-09-06 08:08:55,219 DEBUG: View 1 : 0.747252747253
-2016-09-06 08:08:55,226 DEBUG: View 2 : 0.714285714286
-2016-09-06 08:08:55,233 DEBUG: View 3 : 0.675824175824
-2016-09-06 08:08:55,274 DEBUG: Best view : View1
-2016-09-06 08:08:55,492 INFO: Start: Classification
-2016-09-06 08:08:55,848 INFO: Done: Fold number 5
-2016-09-06 08:08:55,849 INFO: Done: Classification
-2016-09-06 08:08:55,849 INFO: Info: Time for Classification: 5[s]
-2016-09-06 08:08:55,849 INFO: Start: Result Analysis for Mumbo
-2016-09-06 08:08:57,746 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 72.7748989743
- -On Test : 51.2195121951
- -On Validation : 68.5393258427Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 10), View1 of shape (300, 18), View2 of shape (300, 19), View3 of shape (300, 5)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:01 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:04 0:00:00
- Fold 5 0:00:05 0:00:00
- Total 0:00:15 0:00:01
- So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 4
- - On View0 :
- - Mean average Accuracy : 0.186440677966
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.171751412429
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.178531073446
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.175141242938
- - Percentage of time chosen : 0.0
- - Fold 1, used 4
- - On View0 :
- - Mean average Accuracy : 0.179032258065
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.188709677419
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.184408602151
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.175806451613
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.177297297297
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.184864864865
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.171891891892
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.175135135135
- - Percentage of time chosen : 0.0
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.185635359116
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.204419889503
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.190055248619
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.195580110497
- - Percentage of time chosen : 0.0
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.185714285714
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.19010989011
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.183516483516
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.175824175824
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 58.1920903955
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 2
- Accuracy on train : 57.5268817204
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 3
- Accuracy on train : 57.8378378378
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 4
- Accuracy on train : 58.5635359116
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 5
- Accuracy on train : 59.3406593407
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 72.3163841808
- Accuracy on test : 0.0
- Accuracy on validation : 70.7865168539
- Selected View : View0
- Fold 2
- Accuracy on train : 73.1182795699
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View1
- Fold 3
- Accuracy on train : 71.3513513514
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View1
- Fold 4
- Accuracy on train : 72.9281767956
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View1
- Fold 5
- Accuracy on train : 74.7252747253
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View1
- - Iteration 3
- Fold 1
- Accuracy on train : 71.7514124294
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View0
- Fold 2
- Accuracy on train : 73.1182795699
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View1
- Fold 3
- Accuracy on train : 71.3513513514
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View1
- Fold 4
- Accuracy on train : 72.9281767956
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View1
- Fold 5
- Accuracy on train : 74.7252747253
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View1
- - Iteration 4
- Fold 1
- Accuracy on train : 58.1920903955
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 2
- Accuracy on train : 57.5268817204
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 3
- Accuracy on train : 57.8378378378
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 4
- Accuracy on train : 58.5635359116
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 5
- Accuracy on train : 59.3406593407
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
-2016-09-06 08:08:57,922 INFO: Done: Result Analysis
-2016-09-06 08:08:58,079 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:58,080 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:08:58,080 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:58,080 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:08:58,080 INFO: Info: Shape of View0 :(300, 10)
-2016-09-06 08:08:58,081 INFO: Info: Shape of View0 :(300, 10)
-2016-09-06 08:08:58,081 INFO: Info: Shape of View1 :(300, 18)
-2016-09-06 08:08:58,081 INFO: Info: Shape of View1 :(300, 18)
-2016-09-06 08:08:58,082 INFO: Info: Shape of View2 :(300, 19)
-2016-09-06 08:08:58,082 INFO: Info: Shape of View2 :(300, 19)
-2016-09-06 08:08:58,082 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 08:08:58,083 INFO: Done: Read Database Files
-2016-09-06 08:08:58,083 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:08:58,083 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 08:08:58,083 INFO: Done: Read Database Files
-2016-09-06 08:08:58,083 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:08:58,087 INFO: Done: Determine validation split
-2016-09-06 08:08:58,087 INFO: Start: Determine 5 folds
-2016-09-06 08:08:58,089 INFO: Done: Determine validation split
-2016-09-06 08:08:58,089 INFO: Start: Determine 5 folds
-2016-09-06 08:08:58,095 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:08:58,095 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:08:58,095 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:08:58,095 INFO: Done: Determine folds
-2016-09-06 08:08:58,095 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:08:58,095 INFO: Start: Classification
-2016-09-06 08:08:58,095 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:08:58,095 INFO: Start: Fold number 1
-2016-09-06 08:08:58,095 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:08:58,095 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:08:58,096 INFO: Done: Determine folds
-2016-09-06 08:08:58,096 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:08:58,096 INFO: Start: Classification
-2016-09-06 08:08:58,096 INFO: Start: Fold number 1
-2016-09-06 08:08:58,144 INFO: Start: Classification
-2016-09-06 08:08:58,164 INFO: Start: Classification
-2016-09-06 08:08:58,209 INFO: Done: Fold number 1
-2016-09-06 08:08:58,209 INFO: Start: Fold number 2
-2016-09-06 08:08:58,245 INFO: Done: Fold number 1
-2016-09-06 08:08:58,245 INFO: Start: Fold number 2
-2016-09-06 08:08:58,275 INFO: Start: Classification
-2016-09-06 08:08:58,292 INFO: Start: Classification
-2016-09-06 08:08:58,318 INFO: Done: Fold number 2
-2016-09-06 08:08:58,318 INFO: Start: Fold number 3
-2016-09-06 08:08:58,383 INFO: Start: Classification
-2016-09-06 08:08:58,395 INFO: Done: Fold number 2
-2016-09-06 08:08:58,395 INFO: Start: Fold number 3
-2016-09-06 08:08:58,426 INFO: Done: Fold number 3
-2016-09-06 08:08:58,426 INFO: Start: Fold number 4
-2016-09-06 08:08:58,438 INFO: Start: Classification
-2016-09-06 08:08:58,493 INFO: Start: Classification
-2016-09-06 08:08:58,537 INFO: Done: Fold number 4
-2016-09-06 08:08:58,537 INFO: Start: Fold number 5
-2016-09-06 08:08:58,540 INFO: Done: Fold number 3
-2016-09-06 08:08:58,541 INFO: Start: Fold number 4
-2016-09-06 08:08:58,582 INFO: Start: Classification
-2016-09-06 08:08:58,605 INFO: Start: Classification
-2016-09-06 08:08:58,649 INFO: Done: Fold number 5
-2016-09-06 08:08:58,649 INFO: Done: Classification
-2016-09-06 08:08:58,649 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:08:58,650 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:08:58,654 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 58.5365853659
- -On Validation : 88.7640449438
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 49
- - K nearest Neighbors with n_neighbors: 48
- - SVM Linear with C : 9418
- - K nearest Neighbors with n_neighbors: 48
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:08:58,654 INFO: Done: Result Analysis
-2016-09-06 08:08:58,680 INFO: Done: Fold number 4
-2016-09-06 08:08:58,680 INFO: Start: Fold number 5
-2016-09-06 08:08:58,723 INFO: Start: Classification
-2016-09-06 08:08:58,820 INFO: Done: Fold number 5
-2016-09-06 08:08:58,820 INFO: Done: Classification
-2016-09-06 08:08:58,820 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:08:58,820 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:08:58,824 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 60.3193359885
- -On Test : 58.5365853659
- -On Validation : 60.4494382022
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 49
- - K nearest Neighbors with n_neighbors: 48
- - SVM Linear with C : 9418
- - K nearest Neighbors with n_neighbors: 48
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:08:58,824 INFO: Done: Result Analysis
-2016-09-06 08:08:58,929 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:58,929 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:08:58,930 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:08:58,930 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:08:58,930 INFO: Info: Shape of View0 :(300, 10)
-2016-09-06 08:08:58,930 INFO: Info: Shape of View0 :(300, 10)
-2016-09-06 08:08:58,931 INFO: Info: Shape of View1 :(300, 18)
-2016-09-06 08:08:58,931 INFO: Info: Shape of View1 :(300, 18)
-2016-09-06 08:08:58,931 INFO: Info: Shape of View2 :(300, 19)
-2016-09-06 08:08:58,932 INFO: Info: Shape of View2 :(300, 19)
-2016-09-06 08:08:58,932 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 08:08:58,932 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 08:08:58,932 INFO: Done: Read Database Files
-2016-09-06 08:08:58,933 INFO: Done: Read Database Files
-2016-09-06 08:08:58,933 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:08:58,933 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:08:58,940 INFO: Done: Determine validation split
-2016-09-06 08:08:58,940 INFO: Start: Determine 5 folds
-2016-09-06 08:08:58,942 INFO: Done: Determine validation split
-2016-09-06 08:08:58,942 INFO: Start: Determine 5 folds
-2016-09-06 08:08:58,947 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:08:58,948 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:08:58,948 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:08:58,948 INFO: Done: Determine folds
-2016-09-06 08:08:58,948 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:08:58,948 INFO: Start: Classification
-2016-09-06 08:08:58,948 INFO: Start: Fold number 1
-2016-09-06 08:08:58,951 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:08:58,951 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:08:58,951 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:08:58,951 INFO: Done: Determine folds
-2016-09-06 08:08:58,951 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:08:58,951 INFO: Start: Classification
-2016-09-06 08:08:58,952 INFO: Start: Fold number 1
-2016-09-06 08:08:58,990 INFO: Start: Classification
-2016-09-06 08:08:58,994 INFO: Start: Classification
-2016-09-06 08:08:59,020 INFO: Done: Fold number 1
-2016-09-06 08:08:59,020 INFO: Start: Fold number 2
-2016-09-06 08:08:59,047 INFO: Start: Classification
-2016-09-06 08:08:59,078 INFO: Done: Fold number 2
-2016-09-06 08:08:59,079 INFO: Start: Fold number 3
-2016-09-06 08:08:59,106 INFO: Start: Classification
-2016-09-06 08:08:59,134 INFO: Done: Fold number 3
-2016-09-06 08:08:59,134 INFO: Start: Fold number 4
-2016-09-06 08:08:59,163 INFO: Start: Classification
-2016-09-06 08:08:59,192 INFO: Done: Fold number 4
-2016-09-06 08:08:59,192 INFO: Start: Fold number 5
-2016-09-06 08:08:59,218 INFO: Start: Classification
-2016-09-06 08:08:59,249 INFO: Done: Fold number 5
-2016-09-06 08:08:59,249 INFO: Done: Classification
-2016-09-06 08:08:59,249 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:08:59,249 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:08:59,254 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 52.1951219512
- -On Validation : 87.191011236
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:08:59,255 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bc68c031010210e169d2d809f520e3847c6c08ff..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e44f25c9e95475fde56fbf77525b0818a8eed8c0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cc69610050ac777c62bac5db7d35ba1a824fb0d4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080844Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 49
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7bcd03e6aa638831e4c58645a42b2ffbed93a680..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b3acd10cc6006fec45f8a010288c85fa12f156d4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d66d65e340d7160c41b34e159a49cbd8b325408d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 21, max_depth : 29
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index eb325db8f4d2cee8b843e32337767ee0b322c4e6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8c02c6bd45023d223b393d5135687e2ecc69b0b1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fddb5536642d7e4aa376e1a9acb1bb90ece12769..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 53820b696eed2b0722a9599e9c20a17f7588dd0a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080845Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d49255a9322f2b9ac428ec2544f67c5c189fb91f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dd1c0db16e1fe02ddec587d60ffd1ede31a461da..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 16, max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cb31d3e03634759b181058808bf0766ea119814e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cfbc23e9399385766ab01673b61f8f159df77a85..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080846Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 595f1759879666cc943b4e4113a41f37dfd5e39f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6dfced7ab86761cdc0b3135db15ffe3dc174f164..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6a32a48800d2e6e12d83db9b38179af8573d77f9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 192c222e2718c4af95f198e72a8269bd1c3fc8f8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9418
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75c23f1937a773c25af63ab61420313dbb7704da..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080847Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8ba8b1363fde21a92d624d60679767ede65fd842..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f3b9d99b4ba41e41c462b2eb7a02804e63577e91..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 39843a282c82511777c4a921122a1f869ac570dd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 21, max_depth : 29
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 477543be95fc0879a0a924343af52303f14c3792..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5210714f51782fdf13d52246648171e8afffaa31..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e1fc28cfcd0b90f78c579e628b33ccd6ba99eb08..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9418
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0fb8d0835d1e17bcef141d9bb79b387a93fc88cc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080848Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e34c4d685155cef8a69c9f65c986e25d797df46a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 78e057af1bf98d98c0ede2a882eb3758ec231f23..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080849Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 21, max_depth : 29
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-Fusion-LateFusion-BayesianInference-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-Fusion-LateFusion-BayesianInference-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 49ba6c0681781f5f26d8fc6c3d4ec7b3a511a066..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-Fusion-LateFusion-BayesianInference-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 58.6889730599
- -On Test : 58.5365853659
- -On Validation : 58.8764044944
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 49
- - K nearest Neighbors with n_neighbors: 48
- - SVM Linear with C : 9418
- - K nearest Neighbors with n_neighbors: 48
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f0efbadd997b2cb9c02ff600429ade006e9ede08..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1a399586a566eedbc2bc050abf234932244dd3e4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080850Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9265
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index d6b6883e5caebf37821340382ae1eabf20654c03..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5f59f5e7ff3f27e2cca266dacf6dc13ed5d1416e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080857Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,209 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 72.7748989743
- -On Test : 51.2195121951
- -On Validation : 68.5393258427Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 10), View1 of shape (300, 18), View2 of shape (300, 19), View3 of shape (300, 5)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:01 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:04 0:00:00
- Fold 5 0:00:05 0:00:00
- Total 0:00:15 0:00:01
- So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 4
- - On View0 :
- - Mean average Accuracy : 0.186440677966
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.171751412429
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.178531073446
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.175141242938
- - Percentage of time chosen : 0.0
- - Fold 1, used 4
- - On View0 :
- - Mean average Accuracy : 0.179032258065
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.188709677419
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.184408602151
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.175806451613
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.177297297297
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.184864864865
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.171891891892
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.175135135135
- - Percentage of time chosen : 0.0
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.185635359116
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.204419889503
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.190055248619
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.195580110497
- - Percentage of time chosen : 0.0
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.185714285714
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.19010989011
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.183516483516
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.175824175824
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 58.1920903955
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 2
- Accuracy on train : 57.5268817204
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 3
- Accuracy on train : 57.8378378378
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 4
- Accuracy on train : 58.5635359116
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 5
- Accuracy on train : 59.3406593407
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 72.3163841808
- Accuracy on test : 0.0
- Accuracy on validation : 70.7865168539
- Selected View : View0
- Fold 2
- Accuracy on train : 73.1182795699
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View1
- Fold 3
- Accuracy on train : 71.3513513514
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View1
- Fold 4
- Accuracy on train : 72.9281767956
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View1
- Fold 5
- Accuracy on train : 74.7252747253
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View1
- - Iteration 3
- Fold 1
- Accuracy on train : 71.7514124294
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View0
- Fold 2
- Accuracy on train : 73.1182795699
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View1
- Fold 3
- Accuracy on train : 71.3513513514
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View1
- Fold 4
- Accuracy on train : 72.9281767956
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View1
- Fold 5
- Accuracy on train : 74.7252747253
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View1
- - Iteration 4
- Fold 1
- Accuracy on train : 58.1920903955
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 2
- Accuracy on train : 57.5268817204
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 3
- Accuracy on train : 57.8378378378
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 4
- Accuracy on train : 58.5635359116
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
- Fold 5
- Accuracy on train : 59.3406593407
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-MajorityVoting-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-MajorityVoting-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 66ff9806866a54960f477e7ab06612f9014eae42..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-MajorityVoting-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 60.3193359885
- -On Test : 58.5365853659
- -On Validation : 60.4494382022
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 49
- - K nearest Neighbors with n_neighbors: 48
- - SVM Linear with C : 9418
- - K nearest Neighbors with n_neighbors: 48
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-SVMForLinear-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-SVMForLinear-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 36bb41159a2ee16cb7b4f5bd9d3b38d8316ddc80..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080858Results-Fusion-LateFusion-SVMForLinear-KNN-KNN-SVMPoly-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 58.5365853659
- -On Validation : 88.7640449438
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 49
- - K nearest Neighbors with n_neighbors: 48
- - SVM Linear with C : 9418
- - K nearest Neighbors with n_neighbors: 48
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080859Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080859Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index cde5d32e63635dd81f66bc760d0028ed64bff976..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080859Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 52.1951219512
- -On Validation : 87.191011236
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-080940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 1c841d1c8398ec7a85fee901a162b9e058ebf3ef..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1580 +0,0 @@
-2016-09-06 08:09:40,131 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:09:40,131 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00013103125 Gbytes /!\
-2016-09-06 08:09:45,141 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:09:45,143 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:09:45,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,196 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:09:45,196 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:09:45,196 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:45,196 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:45,197 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,197 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,197 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,197 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,197 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:45,197 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:45,198 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:45,198 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:45,253 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:45,253 DEBUG: Start: Training
-2016-09-06 08:09:45,255 DEBUG: Info: Time for Training: 0.0599348545074[s]
-2016-09-06 08:09:45,255 DEBUG: Done: Training
-2016-09-06 08:09:45,255 DEBUG: Start: Predicting
-2016-09-06 08:09:45,258 DEBUG: Done: Predicting
-2016-09-06 08:09:45,258 DEBUG: Start: Getting Results
-2016-09-06 08:09:45,259 DEBUG: Done: Getting Results
-2016-09-06 08:09:45,259 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:09:45,259 INFO: Done: Result Analysis
-2016-09-06 08:09:45,286 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:45,286 DEBUG: Start: Training
-2016-09-06 08:09:45,290 DEBUG: Info: Time for Training: 0.095223903656[s]
-2016-09-06 08:09:45,290 DEBUG: Done: Training
-2016-09-06 08:09:45,290 DEBUG: Start: Predicting
-2016-09-06 08:09:45,293 DEBUG: Done: Predicting
-2016-09-06 08:09:45,293 DEBUG: Start: Getting Results
-2016-09-06 08:09:45,295 DEBUG: Done: Getting Results
-2016-09-06 08:09:45,295 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:09:45,295 INFO: Done: Result Analysis
-2016-09-06 08:09:45,443 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,443 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:09:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:09:45,443 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:45,443 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:45,444 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,444 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,444 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,444 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:45,444 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,444 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:45,444 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:45,445 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:45,496 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:45,497 DEBUG: Start: Training
-2016-09-06 08:09:45,497 DEBUG: Info: Time for Training: 0.05499792099[s]
-2016-09-06 08:09:45,497 DEBUG: Done: Training
-2016-09-06 08:09:45,497 DEBUG: Start: Predicting
-2016-09-06 08:09:45,502 DEBUG: Done: Predicting
-2016-09-06 08:09:45,502 DEBUG: Start: Getting Results
-2016-09-06 08:09:45,503 DEBUG: Done: Getting Results
-2016-09-06 08:09:45,503 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.780952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.780952380952
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:09:45,504 INFO: Done: Result Analysis
-2016-09-06 08:09:45,806 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:45,806 DEBUG: Start: Training
-2016-09-06 08:09:45,850 DEBUG: Info: Time for Training: 0.407772064209[s]
-2016-09-06 08:09:45,850 DEBUG: Done: Training
-2016-09-06 08:09:45,850 DEBUG: Start: Predicting
-2016-09-06 08:09:45,856 DEBUG: Done: Predicting
-2016-09-06 08:09:45,856 DEBUG: Start: Getting Results
-2016-09-06 08:09:45,857 DEBUG: Done: Getting Results
-2016-09-06 08:09:45,857 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 17, max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:09:45,857 INFO: Done: Result Analysis
-2016-09-06 08:09:45,992 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,992 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:45,992 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:09:45,992 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:09:45,992 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:45,992 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:45,993 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,993 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:45,993 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,993 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:45,993 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:45,993 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:45,993 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:45,993 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,067 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:46,067 DEBUG: Start: Training
-2016-09-06 08:09:46,068 DEBUG: Info: Time for Training: 0.076208114624[s]
-2016-09-06 08:09:46,068 DEBUG: Done: Training
-2016-09-06 08:09:46,068 DEBUG: Start: Predicting
-2016-09-06 08:09:46,078 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:46,078 DEBUG: Start: Training
-2016-09-06 08:09:46,101 DEBUG: Done: Predicting
-2016-09-06 08:09:46,101 DEBUG: Start: Getting Results
-2016-09-06 08:09:46,103 DEBUG: Done: Getting Results
-2016-09-06 08:09:46,103 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,103 INFO: Done: Result Analysis
-2016-09-06 08:09:46,103 DEBUG: Info: Time for Training: 0.111602067947[s]
-2016-09-06 08:09:46,103 DEBUG: Done: Training
-2016-09-06 08:09:46,103 DEBUG: Start: Predicting
-2016-09-06 08:09:46,106 DEBUG: Done: Predicting
-2016-09-06 08:09:46,106 DEBUG: Start: Getting Results
-2016-09-06 08:09:46,108 DEBUG: Done: Getting Results
-2016-09-06 08:09:46,108 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,108 INFO: Done: Result Analysis
-2016-09-06 08:09:46,239 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,239 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,239 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:09:46,239 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:09:46,239 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:46,239 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:46,240 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:46,240 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:09:46,240 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:46,240 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:09:46,240 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:46,240 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:46,241 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,241 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,321 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:46,321 DEBUG: Start: Training
-2016-09-06 08:09:46,332 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:46,332 DEBUG: Start: Training
-2016-09-06 08:09:46,338 DEBUG: Info: Time for Training: 0.0998349189758[s]
-2016-09-06 08:09:46,338 DEBUG: Done: Training
-2016-09-06 08:09:46,338 DEBUG: Start: Predicting
-2016-09-06 08:09:46,344 DEBUG: Done: Predicting
-2016-09-06 08:09:46,344 DEBUG: Start: Getting Results
-2016-09-06 08:09:46,345 DEBUG: Done: Getting Results
-2016-09-06 08:09:46,345 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,345 INFO: Done: Result Analysis
-2016-09-06 08:09:46,350 DEBUG: Info: Time for Training: 0.111637830734[s]
-2016-09-06 08:09:46,350 DEBUG: Done: Training
-2016-09-06 08:09:46,350 DEBUG: Start: Predicting
-2016-09-06 08:09:46,354 DEBUG: Done: Predicting
-2016-09-06 08:09:46,354 DEBUG: Start: Getting Results
-2016-09-06 08:09:46,355 DEBUG: Done: Getting Results
-2016-09-06 08:09:46,355 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,355 INFO: Done: Result Analysis
-2016-09-06 08:09:46,495 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,495 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,496 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:09:46,496 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:09:46,496 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:46,496 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:46,497 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:46,497 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:46,497 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:46,497 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:46,497 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:46,497 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:46,497 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,497 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,578 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:46,579 DEBUG: Start: Training
-2016-09-06 08:09:46,580 DEBUG: Info: Time for Training: 0.0850560665131[s]
-2016-09-06 08:09:46,580 DEBUG: Done: Training
-2016-09-06 08:09:46,580 DEBUG: Start: Predicting
-2016-09-06 08:09:46,583 DEBUG: Done: Predicting
-2016-09-06 08:09:46,584 DEBUG: Start: Getting Results
-2016-09-06 08:09:46,585 DEBUG: Done: Getting Results
-2016-09-06 08:09:46,585 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.666666666667
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.666666666667
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,586 INFO: Done: Result Analysis
-2016-09-06 08:09:46,609 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:46,609 DEBUG: Start: Training
-2016-09-06 08:09:46,612 DEBUG: Info: Time for Training: 0.117750167847[s]
-2016-09-06 08:09:46,612 DEBUG: Done: Training
-2016-09-06 08:09:46,612 DEBUG: Start: Predicting
-2016-09-06 08:09:46,615 DEBUG: Done: Predicting
-2016-09-06 08:09:46,615 DEBUG: Start: Getting Results
-2016-09-06 08:09:46,617 DEBUG: Done: Getting Results
-2016-09-06 08:09:46,617 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,617 INFO: Done: Result Analysis
-2016-09-06 08:09:46,744 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,744 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:46,744 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:09:46,744 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:09:46,744 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:46,744 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:46,745 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:46,745 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:46,745 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:46,745 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:46,745 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:46,745 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:46,745 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,745 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:46,801 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:46,801 DEBUG: Start: Training
-2016-09-06 08:09:46,802 DEBUG: Info: Time for Training: 0.0580749511719[s]
-2016-09-06 08:09:46,802 DEBUG: Done: Training
-2016-09-06 08:09:46,802 DEBUG: Start: Predicting
-2016-09-06 08:09:46,807 DEBUG: Done: Predicting
-2016-09-06 08:09:46,807 DEBUG: Start: Getting Results
-2016-09-06 08:09:46,808 DEBUG: Done: Getting Results
-2016-09-06 08:09:46,809 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.733333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.733333333333
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:09:46,809 INFO: Done: Result Analysis
-2016-09-06 08:09:47,068 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:47,068 DEBUG: Start: Training
-2016-09-06 08:09:47,075 DEBUG: Info: Time for Training: 0.331625938416[s]
-2016-09-06 08:09:47,075 DEBUG: Done: Training
-2016-09-06 08:09:47,075 DEBUG: Start: Predicting
-2016-09-06 08:09:47,078 DEBUG: Done: Predicting
-2016-09-06 08:09:47,079 DEBUG: Start: Getting Results
-2016-09-06 08:09:47,080 DEBUG: Done: Getting Results
-2016-09-06 08:09:47,080 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.714285714286
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.714285714286
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,080 INFO: Done: Result Analysis
-2016-09-06 08:09:47,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,196 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:09:47,196 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:09:47,197 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:47,197 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:47,198 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:47,198 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:47,198 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:47,198 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:47,198 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:47,198 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:47,199 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,199 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,313 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:47,314 DEBUG: Start: Training
-2016-09-06 08:09:47,315 DEBUG: Info: Time for Training: 0.119544029236[s]
-2016-09-06 08:09:47,315 DEBUG: Done: Training
-2016-09-06 08:09:47,315 DEBUG: Start: Predicting
-2016-09-06 08:09:47,320 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:47,320 DEBUG: Start: Training
-2016-09-06 08:09:47,331 DEBUG: Done: Predicting
-2016-09-06 08:09:47,331 DEBUG: Start: Getting Results
-2016-09-06 08:09:47,333 DEBUG: Done: Getting Results
-2016-09-06 08:09:47,333 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,333 INFO: Done: Result Analysis
-2016-09-06 08:09:47,343 DEBUG: Info: Time for Training: 0.147957086563[s]
-2016-09-06 08:09:47,343 DEBUG: Done: Training
-2016-09-06 08:09:47,343 DEBUG: Start: Predicting
-2016-09-06 08:09:47,346 DEBUG: Done: Predicting
-2016-09-06 08:09:47,346 DEBUG: Start: Getting Results
-2016-09-06 08:09:47,347 DEBUG: Done: Getting Results
-2016-09-06 08:09:47,347 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,348 INFO: Done: Result Analysis
-2016-09-06 08:09:47,444 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,444 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,444 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:09:47,444 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:09:47,445 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:47,445 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:47,445 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:47,445 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:09:47,446 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:47,446 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:09:47,446 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:47,446 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:47,446 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,446 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,565 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:47,565 DEBUG: Start: Training
-2016-09-06 08:09:47,577 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:47,577 DEBUG: Start: Training
-2016-09-06 08:09:47,589 DEBUG: Info: Time for Training: 0.145581960678[s]
-2016-09-06 08:09:47,589 DEBUG: Done: Training
-2016-09-06 08:09:47,589 DEBUG: Start: Predicting
-2016-09-06 08:09:47,596 DEBUG: Done: Predicting
-2016-09-06 08:09:47,596 DEBUG: Start: Getting Results
-2016-09-06 08:09:47,598 DEBUG: Done: Getting Results
-2016-09-06 08:09:47,598 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,598 INFO: Done: Result Analysis
-2016-09-06 08:09:47,601 DEBUG: Info: Time for Training: 0.157520055771[s]
-2016-09-06 08:09:47,601 DEBUG: Done: Training
-2016-09-06 08:09:47,601 DEBUG: Start: Predicting
-2016-09-06 08:09:47,604 DEBUG: Done: Predicting
-2016-09-06 08:09:47,604 DEBUG: Start: Getting Results
-2016-09-06 08:09:47,605 DEBUG: Done: Getting Results
-2016-09-06 08:09:47,605 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.695238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9103
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.695238095238
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,605 INFO: Done: Result Analysis
-2016-09-06 08:09:47,692 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,692 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,692 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:09:47,692 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:09:47,692 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:47,692 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:47,692 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:47,692 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:47,693 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:47,693 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:47,693 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:47,693 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:47,693 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,693 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,754 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:47,754 DEBUG: Start: Training
-2016-09-06 08:09:47,757 DEBUG: Info: Time for Training: 0.065906047821[s]
-2016-09-06 08:09:47,757 DEBUG: Done: Training
-2016-09-06 08:09:47,757 DEBUG: Start: Predicting
-2016-09-06 08:09:47,760 DEBUG: Done: Predicting
-2016-09-06 08:09:47,760 DEBUG: Start: Getting Results
-2016-09-06 08:09:47,761 DEBUG: Done: Getting Results
-2016-09-06 08:09:47,761 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,761 INFO: Done: Result Analysis
-2016-09-06 08:09:47,795 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:47,795 DEBUG: Start: Training
-2016-09-06 08:09:47,800 DEBUG: Info: Time for Training: 0.109374046326[s]
-2016-09-06 08:09:47,801 DEBUG: Done: Training
-2016-09-06 08:09:47,801 DEBUG: Start: Predicting
-2016-09-06 08:09:47,804 DEBUG: Done: Predicting
-2016-09-06 08:09:47,804 DEBUG: Start: Getting Results
-2016-09-06 08:09:47,805 DEBUG: Done: Getting Results
-2016-09-06 08:09:47,805 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:09:47,805 INFO: Done: Result Analysis
-2016-09-06 08:09:47,942 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,942 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:09:47,943 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:47,943 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:47,943 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:47,944 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:47,943 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:47,944 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,944 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:09:47,944 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:47,945 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:47,945 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:47,945 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:47,945 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:47,995 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:47,995 DEBUG: Start: Training
-2016-09-06 08:09:47,995 DEBUG: Info: Time for Training: 0.0538508892059[s]
-2016-09-06 08:09:47,996 DEBUG: Done: Training
-2016-09-06 08:09:47,996 DEBUG: Start: Predicting
-2016-09-06 08:09:48,001 DEBUG: Done: Predicting
-2016-09-06 08:09:48,001 DEBUG: Start: Getting Results
-2016-09-06 08:09:48,002 DEBUG: Done: Getting Results
-2016-09-06 08:09:48,002 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.738095238095
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,002 INFO: Done: Result Analysis
-2016-09-06 08:09:48,305 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:48,305 DEBUG: Start: Training
-2016-09-06 08:09:48,313 DEBUG: Info: Time for Training: 0.370137929916[s]
-2016-09-06 08:09:48,313 DEBUG: Done: Training
-2016-09-06 08:09:48,313 DEBUG: Start: Predicting
-2016-09-06 08:09:48,318 DEBUG: Done: Predicting
-2016-09-06 08:09:48,318 DEBUG: Start: Getting Results
-2016-09-06 08:09:48,319 DEBUG: Done: Getting Results
-2016-09-06 08:09:48,319 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.728571428571
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.728571428571
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,319 INFO: Done: Result Analysis
-2016-09-06 08:09:48,392 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,392 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:09:48,393 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:48,393 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,393 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:09:48,394 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:48,394 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:48,394 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:48,394 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:48,394 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,394 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:48,395 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:48,395 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:48,395 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,467 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:48,467 DEBUG: Start: Training
-2016-09-06 08:09:48,468 DEBUG: Info: Time for Training: 0.0765888690948[s]
-2016-09-06 08:09:48,468 DEBUG: Done: Training
-2016-09-06 08:09:48,468 DEBUG: Start: Predicting
-2016-09-06 08:09:48,490 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:48,490 DEBUG: Start: Training
-2016-09-06 08:09:48,492 DEBUG: Done: Predicting
-2016-09-06 08:09:48,492 DEBUG: Start: Getting Results
-2016-09-06 08:09:48,494 DEBUG: Done: Getting Results
-2016-09-06 08:09:48,494 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,495 INFO: Done: Result Analysis
-2016-09-06 08:09:48,515 DEBUG: Info: Time for Training: 0.12251996994[s]
-2016-09-06 08:09:48,515 DEBUG: Done: Training
-2016-09-06 08:09:48,515 DEBUG: Start: Predicting
-2016-09-06 08:09:48,519 DEBUG: Done: Predicting
-2016-09-06 08:09:48,519 DEBUG: Start: Getting Results
-2016-09-06 08:09:48,520 DEBUG: Done: Getting Results
-2016-09-06 08:09:48,520 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,520 INFO: Done: Result Analysis
-2016-09-06 08:09:48,637 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,637 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,637 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:09:48,637 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:09:48,637 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:48,637 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:48,638 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:48,638 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:09:48,638 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:48,638 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:09:48,638 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:48,638 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:48,638 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,638 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,723 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:48,723 DEBUG: Start: Training
-2016-09-06 08:09:48,744 DEBUG: Info: Time for Training: 0.107270956039[s]
-2016-09-06 08:09:48,744 DEBUG: Done: Training
-2016-09-06 08:09:48,744 DEBUG: Start: Predicting
-2016-09-06 08:09:48,748 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:48,748 DEBUG: Start: Training
-2016-09-06 08:09:48,750 DEBUG: Done: Predicting
-2016-09-06 08:09:48,750 DEBUG: Start: Getting Results
-2016-09-06 08:09:48,751 DEBUG: Done: Getting Results
-2016-09-06 08:09:48,752 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,752 INFO: Done: Result Analysis
-2016-09-06 08:09:48,768 DEBUG: Info: Time for Training: 0.132148981094[s]
-2016-09-06 08:09:48,769 DEBUG: Done: Training
-2016-09-06 08:09:48,769 DEBUG: Start: Predicting
-2016-09-06 08:09:48,773 DEBUG: Done: Predicting
-2016-09-06 08:09:48,773 DEBUG: Start: Getting Results
-2016-09-06 08:09:48,774 DEBUG: Done: Getting Results
-2016-09-06 08:09:48,774 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,774 INFO: Done: Result Analysis
-2016-09-06 08:09:48,886 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,886 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:48,887 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:09:48,887 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:09:48,887 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:48,887 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:48,887 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:48,887 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:48,887 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:48,887 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:48,888 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:48,888 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:48,888 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,888 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:48,942 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:48,942 DEBUG: Start: Training
-2016-09-06 08:09:48,943 DEBUG: Info: Time for Training: 0.0570180416107[s]
-2016-09-06 08:09:48,943 DEBUG: Done: Training
-2016-09-06 08:09:48,943 DEBUG: Start: Predicting
-2016-09-06 08:09:48,946 DEBUG: Done: Predicting
-2016-09-06 08:09:48,946 DEBUG: Start: Getting Results
-2016-09-06 08:09:48,947 DEBUG: Done: Getting Results
-2016-09-06 08:09:48,947 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.685714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.685714285714
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,947 INFO: Done: Result Analysis
-2016-09-06 08:09:48,972 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:48,972 DEBUG: Start: Training
-2016-09-06 08:09:48,976 DEBUG: Info: Time for Training: 0.0900959968567[s]
-2016-09-06 08:09:48,976 DEBUG: Done: Training
-2016-09-06 08:09:48,976 DEBUG: Start: Predicting
-2016-09-06 08:09:48,979 DEBUG: Done: Predicting
-2016-09-06 08:09:48,979 DEBUG: Start: Getting Results
-2016-09-06 08:09:48,980 DEBUG: Done: Getting Results
-2016-09-06 08:09:48,980 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.388888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.388888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:09:48,981 INFO: Done: Result Analysis
-2016-09-06 08:09:49,135 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:49,135 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:49,135 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:09:49,135 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:09:49,135 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:49,135 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:49,136 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:49,136 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:49,136 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:49,136 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:49,136 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:49,136 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:49,136 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:49,136 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:49,195 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:49,195 DEBUG: Start: Training
-2016-09-06 08:09:49,195 DEBUG: Info: Time for Training: 0.0612609386444[s]
-2016-09-06 08:09:49,196 DEBUG: Done: Training
-2016-09-06 08:09:49,196 DEBUG: Start: Predicting
-2016-09-06 08:09:49,200 DEBUG: Done: Predicting
-2016-09-06 08:09:49,200 DEBUG: Start: Getting Results
-2016-09-06 08:09:49,201 DEBUG: Done: Getting Results
-2016-09-06 08:09:49,202 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.728571428571
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.728571428571
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:09:49,202 INFO: Done: Result Analysis
-2016-09-06 08:09:49,466 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:49,466 DEBUG: Start: Training
-2016-09-06 08:09:49,474 DEBUG: Info: Time for Training: 0.339534044266[s]
-2016-09-06 08:09:49,474 DEBUG: Done: Training
-2016-09-06 08:09:49,474 DEBUG: Start: Predicting
-2016-09-06 08:09:49,477 DEBUG: Done: Predicting
-2016-09-06 08:09:49,478 DEBUG: Start: Getting Results
-2016-09-06 08:09:49,479 DEBUG: Done: Getting Results
-2016-09-06 08:09:49,479 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.680952380952
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.680952380952
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:09:49,479 INFO: Done: Result Analysis
-2016-09-06 08:09:49,586 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:49,586 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:09:49,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:09:49,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:09:49,586 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:49,586 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:09:49,587 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:49,587 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:09:49,587 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:49,587 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:09:49,587 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:49,587 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:09:49,587 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:49,587 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:09:49,663 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:49,663 DEBUG: Start: Training
-2016-09-06 08:09:49,663 DEBUG: Info: Time for Training: 0.078113079071[s]
-2016-09-06 08:09:49,664 DEBUG: Done: Training
-2016-09-06 08:09:49,664 DEBUG: Start: Predicting
-2016-09-06 08:09:49,674 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:09:49,674 DEBUG: Start: Training
-2016-09-06 08:09:49,680 DEBUG: Done: Predicting
-2016-09-06 08:09:49,680 DEBUG: Start: Getting Results
-2016-09-06 08:09:49,681 DEBUG: Done: Getting Results
-2016-09-06 08:09:49,681 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:09:49,681 INFO: Done: Result Analysis
-2016-09-06 08:09:49,695 DEBUG: Info: Time for Training: 0.109401941299[s]
-2016-09-06 08:09:49,695 DEBUG: Done: Training
-2016-09-06 08:09:49,695 DEBUG: Start: Predicting
-2016-09-06 08:09:49,699 DEBUG: Done: Predicting
-2016-09-06 08:09:49,699 DEBUG: Start: Getting Results
-2016-09-06 08:09:49,700 DEBUG: Done: Getting Results
-2016-09-06 08:09:49,700 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:09:49,700 INFO: Done: Result Analysis
-2016-09-06 08:09:49,982 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:09:49,983 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:09:49,983 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:09:49,984 INFO: Info: Shape of View0 :(300, 11)
-2016-09-06 08:09:49,984 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:09:49,984 INFO: Info: Shape of View1 :(300, 6)
-2016-09-06 08:09:49,985 INFO: Info: Shape of View0 :(300, 11)
-2016-09-06 08:09:49,985 INFO: Info: Shape of View2 :(300, 19)
-2016-09-06 08:09:49,986 INFO: Info: Shape of View3 :(300, 12)
-2016-09-06 08:09:49,986 INFO: Done: Read Database Files
-2016-09-06 08:09:49,986 INFO: Info: Shape of View1 :(300, 6)
-2016-09-06 08:09:49,986 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:09:49,987 INFO: Info: Shape of View2 :(300, 19)
-2016-09-06 08:09:49,988 INFO: Info: Shape of View3 :(300, 12)
-2016-09-06 08:09:49,988 INFO: Done: Read Database Files
-2016-09-06 08:09:49,988 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:09:49,990 INFO: Done: Determine validation split
-2016-09-06 08:09:49,990 INFO: Start: Determine 5 folds
-2016-09-06 08:09:49,993 INFO: Done: Determine validation split
-2016-09-06 08:09:49,993 INFO: Start: Determine 5 folds
-2016-09-06 08:09:49,997 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:09:49,997 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:09:49,998 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:09:49,998 INFO: Done: Determine folds
-2016-09-06 08:09:49,998 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:09:49,998 INFO: Start: Classification
-2016-09-06 08:09:49,998 INFO: Start: Fold number 1
-2016-09-06 08:09:49,999 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:09:49,999 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:09:50,000 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:09:50,000 INFO: Done: Determine folds
-2016-09-06 08:09:50,000 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:09:50,000 INFO: Start: Classification
-2016-09-06 08:09:50,000 INFO: Start: Fold number 1
-2016-09-06 08:09:50,031 DEBUG: Start: Iteration 1
-2016-09-06 08:09:50,038 DEBUG: View 0 : 0.488888888889
-2016-09-06 08:09:50,046 DEBUG: View 1 : 0.533333333333
-2016-09-06 08:09:50,052 INFO: Start: Classification
-2016-09-06 08:09:50,053 DEBUG: View 2 : 0.544444444444
-2016-09-06 08:09:50,060 DEBUG: View 3 : 0.455555555556
-2016-09-06 08:09:50,093 DEBUG: Best view : View2
-2016-09-06 08:09:50,176 DEBUG: Start: Iteration 2
-2016-09-06 08:09:50,183 DEBUG: View 0 : 0.688888888889
-2016-09-06 08:09:50,190 DEBUG: View 1 : 0.611111111111
-2016-09-06 08:09:50,198 DEBUG: View 2 : 0.727777777778
-2016-09-06 08:09:50,206 DEBUG: View 3 : 0.694444444444
-2016-09-06 08:09:50,246 DEBUG: Best view : View2
-2016-09-06 08:09:50,398 DEBUG: Start: Iteration 3
-2016-09-06 08:09:50,405 DEBUG: View 0 : 0.688888888889
-2016-09-06 08:09:50,414 DEBUG: View 1 : 0.611111111111
-2016-09-06 08:09:50,422 DEBUG: View 2 : 0.727777777778
-2016-09-06 08:09:50,430 DEBUG: View 3 : 0.694444444444
-2016-09-06 08:09:50,470 DEBUG: Best view : View2
-2016-09-06 08:09:50,684 INFO: Start: Classification
-2016-09-06 08:09:51,034 INFO: Done: Fold number 1
-2016-09-06 08:09:51,035 INFO: Start: Fold number 2
-2016-09-06 08:09:51,063 DEBUG: Start: Iteration 1
-2016-09-06 08:09:51,070 DEBUG: View 0 : 0.530726256983
-2016-09-06 08:09:51,076 DEBUG: View 1 : 0.530726256983
-2016-09-06 08:09:51,083 DEBUG: View 2 : 0.530726256983
-2016-09-06 08:09:51,089 DEBUG: View 3 : 0.530726256983
-2016-09-06 08:09:51,121 DEBUG: Best view : View0
-2016-09-06 08:09:51,199 DEBUG: Start: Iteration 2
-2016-09-06 08:09:51,206 DEBUG: View 0 : 0.620111731844
-2016-09-06 08:09:51,213 DEBUG: View 1 : 0.63687150838
-2016-09-06 08:09:51,220 DEBUG: View 2 : 0.759776536313
-2016-09-06 08:09:51,228 DEBUG: View 3 : 0.720670391061
-2016-09-06 08:09:51,265 DEBUG: Best view : View2
-2016-09-06 08:09:51,412 DEBUG: Start: Iteration 3
-2016-09-06 08:09:51,419 DEBUG: View 0 : 0.620111731844
-2016-09-06 08:09:51,426 DEBUG: View 1 : 0.63687150838
-2016-09-06 08:09:51,433 DEBUG: View 2 : 0.759776536313
-2016-09-06 08:09:51,440 DEBUG: View 3 : 0.720670391061
-2016-09-06 08:09:51,481 DEBUG: Best view : View2
-2016-09-06 08:09:51,695 INFO: Start: Classification
-2016-09-06 08:09:52,044 INFO: Done: Fold number 2
-2016-09-06 08:09:52,044 INFO: Start: Fold number 3
-2016-09-06 08:09:52,074 DEBUG: Start: Iteration 1
-2016-09-06 08:09:52,081 DEBUG: View 0 : 0.508196721311
-2016-09-06 08:09:52,088 DEBUG: View 1 : 0.55737704918
-2016-09-06 08:09:52,094 DEBUG: View 2 : 0.497267759563
-2016-09-06 08:09:52,101 DEBUG: View 3 : 0.48087431694
-2016-09-06 08:09:52,134 DEBUG: Best view : View2
-2016-09-06 08:09:52,214 DEBUG: Start: Iteration 2
-2016-09-06 08:09:52,221 DEBUG: View 0 : 0.699453551913
-2016-09-06 08:09:52,228 DEBUG: View 1 : 0.666666666667
-2016-09-06 08:09:52,236 DEBUG: View 2 : 0.743169398907
-2016-09-06 08:09:52,243 DEBUG: View 3 : 0.699453551913
-2016-09-06 08:09:52,281 DEBUG: Best view : View2
-2016-09-06 08:09:52,436 DEBUG: Start: Iteration 3
-2016-09-06 08:09:52,444 DEBUG: View 0 : 0.699453551913
-2016-09-06 08:09:52,451 DEBUG: View 1 : 0.666666666667
-2016-09-06 08:09:52,459 DEBUG: View 2 : 0.743169398907
-2016-09-06 08:09:52,466 DEBUG: View 3 : 0.699453551913
-2016-09-06 08:09:52,507 DEBUG: Best view : View2
-2016-09-06 08:09:52,725 INFO: Start: Classification
-2016-09-06 08:09:53,079 INFO: Done: Fold number 3
-2016-09-06 08:09:53,079 INFO: Start: Fold number 4
-2016-09-06 08:09:53,108 DEBUG: Start: Iteration 1
-2016-09-06 08:09:53,115 DEBUG: View 0 : 0.491712707182
-2016-09-06 08:09:53,122 DEBUG: View 1 : 0.519337016575
-2016-09-06 08:09:53,129 DEBUG: View 2 : 0.546961325967
-2016-09-06 08:09:53,135 DEBUG: View 3 : 0.508287292818
-2016-09-06 08:09:53,167 DEBUG: Best view : View3
-2016-09-06 08:09:53,246 DEBUG: Start: Iteration 2
-2016-09-06 08:09:53,253 DEBUG: View 0 : 0.60773480663
-2016-09-06 08:09:53,260 DEBUG: View 1 : 0.60773480663
-2016-09-06 08:09:53,268 DEBUG: View 2 : 0.657458563536
-2016-09-06 08:09:53,275 DEBUG: View 3 : 0.696132596685
-2016-09-06 08:09:53,313 DEBUG: Best view : View3
-2016-09-06 08:09:53,461 DEBUG: Start: Iteration 3
-2016-09-06 08:09:53,468 DEBUG: View 0 : 0.60773480663
-2016-09-06 08:09:53,475 DEBUG: View 1 : 0.60773480663
-2016-09-06 08:09:53,482 DEBUG: View 2 : 0.657458563536
-2016-09-06 08:09:53,489 DEBUG: View 3 : 0.696132596685
-2016-09-06 08:09:53,530 DEBUG: Best view : View3
-2016-09-06 08:09:53,745 INFO: Start: Classification
-2016-09-06 08:09:54,094 INFO: Done: Fold number 4
-2016-09-06 08:09:54,094 INFO: Start: Fold number 5
-2016-09-06 08:09:54,125 DEBUG: Start: Iteration 1
-2016-09-06 08:09:54,132 DEBUG: View 0 : 0.489361702128
-2016-09-06 08:09:54,139 DEBUG: View 1 : 0.478723404255
-2016-09-06 08:09:54,146 DEBUG: View 2 : 0.457446808511
-2016-09-06 08:09:54,153 DEBUG: View 3 : 0.521276595745
-2016-09-06 08:09:54,186 DEBUG: Best view : View1
-2016-09-06 08:09:54,268 DEBUG: Start: Iteration 2
-2016-09-06 08:09:54,275 DEBUG: View 0 : 0.675531914894
-2016-09-06 08:09:54,282 DEBUG: View 1 : 0.664893617021
-2016-09-06 08:09:54,289 DEBUG: View 2 : 0.787234042553
-2016-09-06 08:09:54,297 DEBUG: View 3 : 0.734042553191
-2016-09-06 08:09:54,337 DEBUG: Best view : View2
-2016-09-06 08:09:54,490 DEBUG: Start: Iteration 3
-2016-09-06 08:09:54,497 DEBUG: View 0 : 0.675531914894
-2016-09-06 08:09:54,504 DEBUG: View 1 : 0.664893617021
-2016-09-06 08:09:54,512 DEBUG: View 2 : 0.787234042553
-2016-09-06 08:09:54,519 DEBUG: View 3 : 0.734042553191
-2016-09-06 08:09:54,561 DEBUG: Best view : View2
-2016-09-06 08:09:54,785 DEBUG: Start: Iteration 4
-2016-09-06 08:09:54,792 DEBUG: View 0 : 0.606382978723
-2016-09-06 08:09:54,799 DEBUG: View 1 : 0.579787234043
-2016-09-06 08:09:54,807 DEBUG: View 2 : 0.718085106383
-2016-09-06 08:09:54,814 DEBUG: View 3 : 0.696808510638
-2016-09-06 08:09:54,859 DEBUG: Best view : View2
-2016-09-06 08:09:55,154 INFO: Start: Classification
-2016-09-06 08:09:55,630 INFO: Done: Fold number 5
-2016-09-06 08:09:55,630 INFO: Done: Classification
-2016-09-06 08:09:55,630 INFO: Info: Time for Classification: 5[s]
-2016-09-06 08:09:55,630 INFO: Start: Result Analysis for Mumbo
-2016-09-06 08:09:57,632 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 74.2818070447
- -On Test : 53.1707317073
- -On Validation : 68.0898876404Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 11), View1 of shape (300, 6), View2 of shape (300, 19), View3 of shape (300, 12)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:01 0:00:00
- Fold 3 0:00:02 0:00:00
- Fold 4 0:00:03 0:00:00
- Fold 5 0:00:05 0:00:00
- Total 0:00:14 0:00:01
- So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 4
- - On View0 :
- - Mean average Accuracy : 0.186666666667
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.175555555556
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.2
- - Percentage of time chosen : 0.3
- - On View3 :
- - Mean average Accuracy : 0.184444444444
- - Percentage of time chosen : 0.0
- - Fold 1, used 4
- - On View0 :
- - Mean average Accuracy : 0.177094972067
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.180446927374
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.205027932961
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.197206703911
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.190710382514
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.189071038251
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.198360655738
- - Percentage of time chosen : 0.3
- - On View3 :
- - Mean average Accuracy : 0.187978142077
- - Percentage of time chosen : 0.0
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.170718232044
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.173480662983
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.186187845304
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.190055248619
- - Percentage of time chosen : 0.3
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.244680851064
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.238829787234
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.275
- - Percentage of time chosen : 0.3
- - On View3 :
- - Mean average Accuracy : 0.268617021277
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 51.6666666667
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View2
- Fold 2
- Accuracy on train : 53.0726256983
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
- Fold 3
- Accuracy on train : 53.0054644809
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View2
- Fold 4
- Accuracy on train : 51.9337016575
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View3
- Fold 5
- Accuracy on train : 50.5319148936
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View1
- - Iteration 2
- Fold 1
- Accuracy on train : 72.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View2
- Fold 2
- Accuracy on train : 75.9776536313
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View2
- Fold 3
- Accuracy on train : 74.3169398907
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View2
- Fold 4
- Accuracy on train : 69.6132596685
- Accuracy on test : 0.0
- Accuracy on validation : 59.5505617978
- Selected View : View3
- Fold 5
- Accuracy on train : 78.7234042553
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View2
- - Iteration 3
- Fold 1
- Accuracy on train : 72.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View2
- Fold 2
- Accuracy on train : 75.9776536313
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View2
- Fold 3
- Accuracy on train : 74.3169398907
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View2
- Fold 4
- Accuracy on train : 69.6132596685
- Accuracy on test : 0.0
- Accuracy on validation : 59.5505617978
- Selected View : View3
- Fold 5
- Accuracy on train : 78.7234042553
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View2
- - Iteration 4
- Fold 1
- Accuracy on train : 51.6666666667
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
- Fold 2
- Accuracy on train : 53.0726256983
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
- Fold 3
- Accuracy on train : 53.0054644809
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
- Fold 4
- Accuracy on train : 51.9337016575
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
- Fold 5
- Accuracy on train : 78.7234042553
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View2
- - Iteration 5
- Fold 5
- Accuracy on train : 50.5319148936
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
-2016-09-06 08:09:57,815 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 260bd337b5065fdc6013923ae67c65598cb767fe..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e3acff55ea8d1c23616c2f4b9451a165d7cae0be..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 966b9ace7e7d7f46aeedfabfe074fa16a954ce16..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.780952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.780952380952
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 703c8c37d4f6eb2dcc11b345ac0a3609826d044d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080945Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 17, max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6219637db2e50734caceb8ebc37f7ffa716b2e74..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db9d82580b9bc90163d06702ee49a6a0b00faad9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.666666666667
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.666666666667
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3835c91f4ce797233c9371265504fdc97e086376..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.733333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.733333333333
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8e18dec5a6816c3e40047646bfa082a146347a4a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 58aab88f40713e216d7f4883649bd7a437945513..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3d2814493e486beca7c87f1aa7eeb32b86c187b3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index be69d2018f79eb5dfe88f0f5fe772a944d383a60..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080946Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3793474e9f00640ead416897eed947046449744c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6042afd35a162bebf3061bf4ce3d9f90e1637b58..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6aed140aeac12cf425e403f5815864af47233d42..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.738095238095
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ffc5f592a148be22c14addf141a8c6355e3e925a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.714285714286
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.714285714286
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 08543b9c89bece460ec81c2226194bc57bc61112..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dfa42758a768d5a747afee3f0d1cb5ac5a01001f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 12cfab976347385134bf9bd5e181928d19d9563b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.695238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9103
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.695238095238
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 696c1906cf276971b7026ae852c98c8ad3752434..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bf8c8be24835e4c193f58b98e8b8cf9d145817b1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.388888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.388888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9f00b6b28e2a701516fcedd3b20aefc6b2668944..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.685714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.685714285714
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dcea9eb839325b8bf5dc6974ef0aee8043e8bb5d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.728571428571
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.728571428571
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 615c2a9b68019a57c1caafe3343e47081327d6fd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4e9420a43e815dd8f3bbe4495efde5f6f22c88e6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 244785d4235288cf34fe1bc4138f613959a4d00d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 183892fd1a753b6188046ef1853987d75ce606a2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 42aa09b7799001a3c4b813bf81da23fe2393f11d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.728571428571
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.728571428571
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a23a75a30e493291a0d60ea2b690e59d341113b3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.680952380952
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.680952380952
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3227b2366a006067e2f72b0d912638b5f29a3e34..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : perceptron, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4e10be90b26f7e1e7a17e4c584b979fe22fbce62..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8811
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index 85173b5e72ff3aec9eb20144b85ad444e07cdcf4..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index fdb9968d848da41778518e410495e8d920cc545e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-080957Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,215 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 74.2818070447
- -On Test : 53.1707317073
- -On Validation : 68.0898876404Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 11), View1 of shape (300, 6), View2 of shape (300, 19), View3 of shape (300, 12)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:01 0:00:00
- Fold 3 0:00:02 0:00:00
- Fold 4 0:00:03 0:00:00
- Fold 5 0:00:05 0:00:00
- Total 0:00:14 0:00:01
- So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 4
- - On View0 :
- - Mean average Accuracy : 0.186666666667
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.175555555556
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.2
- - Percentage of time chosen : 0.3
- - On View3 :
- - Mean average Accuracy : 0.184444444444
- - Percentage of time chosen : 0.0
- - Fold 1, used 4
- - On View0 :
- - Mean average Accuracy : 0.177094972067
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.180446927374
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.205027932961
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.197206703911
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.190710382514
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.189071038251
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.198360655738
- - Percentage of time chosen : 0.3
- - On View3 :
- - Mean average Accuracy : 0.187978142077
- - Percentage of time chosen : 0.0
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.170718232044
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.173480662983
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.186187845304
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.190055248619
- - Percentage of time chosen : 0.3
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.244680851064
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.238829787234
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.275
- - Percentage of time chosen : 0.3
- - On View3 :
- - Mean average Accuracy : 0.268617021277
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 51.6666666667
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View2
- Fold 2
- Accuracy on train : 53.0726256983
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
- Fold 3
- Accuracy on train : 53.0054644809
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View2
- Fold 4
- Accuracy on train : 51.9337016575
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View3
- Fold 5
- Accuracy on train : 50.5319148936
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View1
- - Iteration 2
- Fold 1
- Accuracy on train : 72.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View2
- Fold 2
- Accuracy on train : 75.9776536313
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View2
- Fold 3
- Accuracy on train : 74.3169398907
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View2
- Fold 4
- Accuracy on train : 69.6132596685
- Accuracy on test : 0.0
- Accuracy on validation : 59.5505617978
- Selected View : View3
- Fold 5
- Accuracy on train : 78.7234042553
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View2
- - Iteration 3
- Fold 1
- Accuracy on train : 72.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View2
- Fold 2
- Accuracy on train : 75.9776536313
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View2
- Fold 3
- Accuracy on train : 74.3169398907
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View2
- Fold 4
- Accuracy on train : 69.6132596685
- Accuracy on test : 0.0
- Accuracy on validation : 59.5505617978
- Selected View : View3
- Fold 5
- Accuracy on train : 78.7234042553
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View2
- - Iteration 4
- Fold 1
- Accuracy on train : 51.6666666667
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
- Fold 2
- Accuracy on train : 53.0726256983
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
- Fold 3
- Accuracy on train : 53.0054644809
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
- Fold 4
- Accuracy on train : 51.9337016575
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
- Fold 5
- Accuracy on train : 78.7234042553
- Accuracy on test : 0.0
- Accuracy on validation : 71.9101123596
- Selected View : View2
- - Iteration 5
- Fold 5
- Accuracy on train : 50.5319148936
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-081124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index ba1338154dfe97c28e15ead94c5056da20a921d1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1932 +0,0 @@
-2016-09-06 08:11:24,040 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:11:24,041 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000133375 Gbytes /!\
-2016-09-06 08:11:29,054 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:11:29,057 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:11:29,106 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,107 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:11:29,107 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:29,107 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,108 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,108 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:29,108 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,108 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,108 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:11:29,108 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:29,109 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,109 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,109 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:29,109 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,163 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:29,164 DEBUG: Start: Training
-2016-09-06 08:11:29,164 DEBUG: Info: Time for Training: 0.0569159984589[s]
-2016-09-06 08:11:29,164 DEBUG: Done: Training
-2016-09-06 08:11:29,164 DEBUG: Start: Predicting
-2016-09-06 08:11:29,167 DEBUG: Done: Predicting
-2016-09-06 08:11:29,167 DEBUG: Start: Getting Results
-2016-09-06 08:11:29,168 DEBUG: Done: Getting Results
-2016-09-06 08:11:29,168 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,168 INFO: Done: Result Analysis
-2016-09-06 08:11:29,187 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:29,187 DEBUG: Start: Training
-2016-09-06 08:11:29,190 DEBUG: Info: Time for Training: 0.0837590694427[s]
-2016-09-06 08:11:29,190 DEBUG: Done: Training
-2016-09-06 08:11:29,190 DEBUG: Start: Predicting
-2016-09-06 08:11:29,193 DEBUG: Done: Predicting
-2016-09-06 08:11:29,193 DEBUG: Start: Getting Results
-2016-09-06 08:11:29,195 DEBUG: Done: Getting Results
-2016-09-06 08:11:29,195 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,195 INFO: Done: Result Analysis
-2016-09-06 08:11:29,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,255 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:11:29,255 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:11:29,255 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:29,255 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:29,256 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,256 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,256 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,256 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,256 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:29,256 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:29,256 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,256 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,308 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:29,308 DEBUG: Start: Training
-2016-09-06 08:11:29,308 DEBUG: Info: Time for Training: 0.0547308921814[s]
-2016-09-06 08:11:29,308 DEBUG: Done: Training
-2016-09-06 08:11:29,309 DEBUG: Start: Predicting
-2016-09-06 08:11:29,313 DEBUG: Done: Predicting
-2016-09-06 08:11:29,313 DEBUG: Start: Getting Results
-2016-09-06 08:11:29,314 DEBUG: Done: Getting Results
-2016-09-06 08:11:29,314 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.671428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.671428571429
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,314 INFO: Done: Result Analysis
-2016-09-06 08:11:29,399 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:29,399 DEBUG: Start: Training
-2016-09-06 08:11:29,408 DEBUG: Info: Time for Training: 0.154690027237[s]
-2016-09-06 08:11:29,408 DEBUG: Done: Training
-2016-09-06 08:11:29,409 DEBUG: Start: Predicting
-2016-09-06 08:11:29,412 DEBUG: Done: Predicting
-2016-09-06 08:11:29,412 DEBUG: Start: Getting Results
-2016-09-06 08:11:29,413 DEBUG: Done: Getting Results
-2016-09-06 08:11:29,413 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.638095238095
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.638095238095
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,413 INFO: Done: Result Analysis
-2016-09-06 08:11:29,506 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,506 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,506 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:11:29,506 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:11:29,506 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:29,506 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:29,507 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,507 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,507 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,507 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,507 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:29,507 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:29,508 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,508 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,587 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:29,587 DEBUG: Start: Training
-2016-09-06 08:11:29,588 DEBUG: Info: Time for Training: 0.0827720165253[s]
-2016-09-06 08:11:29,588 DEBUG: Done: Training
-2016-09-06 08:11:29,588 DEBUG: Start: Predicting
-2016-09-06 08:11:29,592 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:29,592 DEBUG: Start: Training
-2016-09-06 08:11:29,609 DEBUG: Info: Time for Training: 0.10356593132[s]
-2016-09-06 08:11:29,609 DEBUG: Done: Training
-2016-09-06 08:11:29,609 DEBUG: Start: Predicting
-2016-09-06 08:11:29,612 DEBUG: Done: Predicting
-2016-09-06 08:11:29,612 DEBUG: Start: Getting Results
-2016-09-06 08:11:29,613 DEBUG: Done: Predicting
-2016-09-06 08:11:29,613 DEBUG: Start: Getting Results
-2016-09-06 08:11:29,613 DEBUG: Done: Getting Results
-2016-09-06 08:11:29,613 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,614 INFO: Done: Result Analysis
-2016-09-06 08:11:29,615 DEBUG: Done: Getting Results
-2016-09-06 08:11:29,615 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,616 INFO: Done: Result Analysis
-2016-09-06 08:11:29,757 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,757 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:29,757 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:11:29,757 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:11:29,757 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:29,757 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:29,758 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,758 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:11:29,758 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,758 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:11:29,758 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:29,758 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:29,758 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,759 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:29,842 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:29,842 DEBUG: Start: Training
-2016-09-06 08:11:29,849 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:29,850 DEBUG: Start: Training
-2016-09-06 08:11:29,860 DEBUG: Info: Time for Training: 0.103305101395[s]
-2016-09-06 08:11:29,860 DEBUG: Done: Training
-2016-09-06 08:11:29,860 DEBUG: Start: Predicting
-2016-09-06 08:11:29,865 DEBUG: Done: Predicting
-2016-09-06 08:11:29,866 DEBUG: Start: Getting Results
-2016-09-06 08:11:29,867 DEBUG: Done: Getting Results
-2016-09-06 08:11:29,867 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,867 INFO: Done: Result Analysis
-2016-09-06 08:11:29,868 DEBUG: Info: Time for Training: 0.112034082413[s]
-2016-09-06 08:11:29,869 DEBUG: Done: Training
-2016-09-06 08:11:29,869 DEBUG: Start: Predicting
-2016-09-06 08:11:29,872 DEBUG: Done: Predicting
-2016-09-06 08:11:29,872 DEBUG: Start: Getting Results
-2016-09-06 08:11:29,873 DEBUG: Done: Getting Results
-2016-09-06 08:11:29,873 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5059
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:29,873 INFO: Done: Result Analysis
-2016-09-06 08:11:30,010 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,010 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:11:30,010 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:30,010 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,011 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:11:30,011 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,011 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:30,011 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,011 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:30,011 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,012 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,012 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,012 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:30,012 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,061 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:30,061 DEBUG: Start: Training
-2016-09-06 08:11:30,062 DEBUG: Info: Time for Training: 0.0535268783569[s]
-2016-09-06 08:11:30,063 DEBUG: Done: Training
-2016-09-06 08:11:30,063 DEBUG: Start: Predicting
-2016-09-06 08:11:30,065 DEBUG: Done: Predicting
-2016-09-06 08:11:30,065 DEBUG: Start: Getting Results
-2016-09-06 08:11:30,066 DEBUG: Done: Getting Results
-2016-09-06 08:11:30,066 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 29
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,067 INFO: Done: Result Analysis
-2016-09-06 08:11:30,098 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:30,098 DEBUG: Start: Training
-2016-09-06 08:11:30,102 DEBUG: Info: Time for Training: 0.092768907547[s]
-2016-09-06 08:11:30,102 DEBUG: Done: Training
-2016-09-06 08:11:30,102 DEBUG: Start: Predicting
-2016-09-06 08:11:30,105 DEBUG: Done: Predicting
-2016-09-06 08:11:30,105 DEBUG: Start: Getting Results
-2016-09-06 08:11:30,107 DEBUG: Done: Getting Results
-2016-09-06 08:11:30,107 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,107 INFO: Done: Result Analysis
-2016-09-06 08:11:30,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,255 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:11:30,255 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:11:30,255 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:30,255 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:30,256 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,256 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,256 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,256 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,256 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:30,256 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:30,257 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,257 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,306 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:30,306 DEBUG: Start: Training
-2016-09-06 08:11:30,306 DEBUG: Info: Time for Training: 0.0526769161224[s]
-2016-09-06 08:11:30,307 DEBUG: Done: Training
-2016-09-06 08:11:30,307 DEBUG: Start: Predicting
-2016-09-06 08:11:30,311 DEBUG: Done: Predicting
-2016-09-06 08:11:30,311 DEBUG: Start: Getting Results
-2016-09-06 08:11:30,312 DEBUG: Done: Getting Results
-2016-09-06 08:11:30,312 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.680952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 6
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.680952380952
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,312 INFO: Done: Result Analysis
-2016-09-06 08:11:30,395 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:30,396 DEBUG: Start: Training
-2016-09-06 08:11:30,403 DEBUG: Info: Time for Training: 0.149629116058[s]
-2016-09-06 08:11:30,404 DEBUG: Done: Training
-2016-09-06 08:11:30,404 DEBUG: Start: Predicting
-2016-09-06 08:11:30,407 DEBUG: Done: Predicting
-2016-09-06 08:11:30,407 DEBUG: Start: Getting Results
-2016-09-06 08:11:30,408 DEBUG: Done: Getting Results
-2016-09-06 08:11:30,408 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,408 INFO: Done: Result Analysis
-2016-09-06 08:11:30,510 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,510 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,511 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:11:30,511 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:11:30,511 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:30,511 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:30,512 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,512 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,512 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,512 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,513 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:30,513 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:30,513 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,513 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,592 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:30,592 DEBUG: Start: Training
-2016-09-06 08:11:30,593 DEBUG: Info: Time for Training: 0.0842258930206[s]
-2016-09-06 08:11:30,593 DEBUG: Done: Training
-2016-09-06 08:11:30,593 DEBUG: Start: Predicting
-2016-09-06 08:11:30,606 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:30,607 DEBUG: Start: Training
-2016-09-06 08:11:30,609 DEBUG: Done: Predicting
-2016-09-06 08:11:30,609 DEBUG: Start: Getting Results
-2016-09-06 08:11:30,610 DEBUG: Done: Getting Results
-2016-09-06 08:11:30,611 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,611 INFO: Done: Result Analysis
-2016-09-06 08:11:30,624 DEBUG: Info: Time for Training: 0.114921092987[s]
-2016-09-06 08:11:30,624 DEBUG: Done: Training
-2016-09-06 08:11:30,624 DEBUG: Start: Predicting
-2016-09-06 08:11:30,628 DEBUG: Done: Predicting
-2016-09-06 08:11:30,628 DEBUG: Start: Getting Results
-2016-09-06 08:11:30,629 DEBUG: Done: Getting Results
-2016-09-06 08:11:30,629 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.452380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.452380952381
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,629 INFO: Done: Result Analysis
-2016-09-06 08:11:30,755 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,755 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:30,756 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:11:30,756 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:11:30,756 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:30,756 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:30,757 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,757 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 08:11:30,757 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,757 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 08:11:30,757 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:30,757 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:30,758 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,758 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:30,884 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:30,884 DEBUG: Start: Training
-2016-09-06 08:11:30,893 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:30,893 DEBUG: Start: Training
-2016-09-06 08:11:30,912 DEBUG: Info: Time for Training: 0.157926082611[s]
-2016-09-06 08:11:30,913 DEBUG: Done: Training
-2016-09-06 08:11:30,913 DEBUG: Start: Predicting
-2016-09-06 08:11:30,919 DEBUG: Done: Predicting
-2016-09-06 08:11:30,919 DEBUG: Start: Getting Results
-2016-09-06 08:11:30,921 DEBUG: Done: Getting Results
-2016-09-06 08:11:30,921 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,921 INFO: Done: Result Analysis
-2016-09-06 08:11:30,922 DEBUG: Info: Time for Training: 0.167793035507[s]
-2016-09-06 08:11:30,922 DEBUG: Done: Training
-2016-09-06 08:11:30,923 DEBUG: Start: Predicting
-2016-09-06 08:11:30,926 DEBUG: Done: Predicting
-2016-09-06 08:11:30,926 DEBUG: Start: Getting Results
-2016-09-06 08:11:30,927 DEBUG: Done: Getting Results
-2016-09-06 08:11:30,927 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:11:30,927 INFO: Done: Result Analysis
-2016-09-06 08:11:31,004 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,004 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,005 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:11:31,005 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:11:31,005 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:31,005 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:31,006 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,006 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,006 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,006 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,006 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:31,006 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:31,006 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,006 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,063 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:31,063 DEBUG: Start: Training
-2016-09-06 08:11:31,064 DEBUG: Info: Time for Training: 0.0602171421051[s]
-2016-09-06 08:11:31,064 DEBUG: Done: Training
-2016-09-06 08:11:31,064 DEBUG: Start: Predicting
-2016-09-06 08:11:31,067 DEBUG: Done: Predicting
-2016-09-06 08:11:31,067 DEBUG: Start: Getting Results
-2016-09-06 08:11:31,068 DEBUG: Done: Getting Results
-2016-09-06 08:11:31,068 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,068 INFO: Done: Result Analysis
-2016-09-06 08:11:31,097 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:31,097 DEBUG: Start: Training
-2016-09-06 08:11:31,101 DEBUG: Info: Time for Training: 0.0974199771881[s]
-2016-09-06 08:11:31,101 DEBUG: Done: Training
-2016-09-06 08:11:31,102 DEBUG: Start: Predicting
-2016-09-06 08:11:31,104 DEBUG: Done: Predicting
-2016-09-06 08:11:31,105 DEBUG: Start: Getting Results
-2016-09-06 08:11:31,106 DEBUG: Done: Getting Results
-2016-09-06 08:11:31,106 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,107 INFO: Done: Result Analysis
-2016-09-06 08:11:31,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,254 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,254 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:11:31,254 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:31,254 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:11:31,254 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:31,255 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,255 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,255 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,255 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,255 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:31,255 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:31,255 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,255 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,306 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:31,306 DEBUG: Start: Training
-2016-09-06 08:11:31,307 DEBUG: Info: Time for Training: 0.0538859367371[s]
-2016-09-06 08:11:31,307 DEBUG: Done: Training
-2016-09-06 08:11:31,307 DEBUG: Start: Predicting
-2016-09-06 08:11:31,312 DEBUG: Done: Predicting
-2016-09-06 08:11:31,313 DEBUG: Start: Getting Results
-2016-09-06 08:11:31,314 DEBUG: Done: Getting Results
-2016-09-06 08:11:31,314 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.77619047619
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.77619047619
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,314 INFO: Done: Result Analysis
-2016-09-06 08:11:31,388 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:31,388 DEBUG: Start: Training
-2016-09-06 08:11:31,396 DEBUG: Info: Time for Training: 0.14239692688[s]
-2016-09-06 08:11:31,396 DEBUG: Done: Training
-2016-09-06 08:11:31,396 DEBUG: Start: Predicting
-2016-09-06 08:11:31,399 DEBUG: Done: Predicting
-2016-09-06 08:11:31,399 DEBUG: Start: Getting Results
-2016-09-06 08:11:31,400 DEBUG: Done: Getting Results
-2016-09-06 08:11:31,400 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.633333333333
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.633333333333
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,400 INFO: Done: Result Analysis
-2016-09-06 08:11:31,503 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,503 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:11:31,504 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:31,504 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,504 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,505 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:31,504 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,505 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,505 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:11:31,505 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:31,506 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,506 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,506 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:31,506 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,586 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:31,587 DEBUG: Start: Training
-2016-09-06 08:11:31,588 DEBUG: Info: Time for Training: 0.0840480327606[s]
-2016-09-06 08:11:31,588 DEBUG: Done: Training
-2016-09-06 08:11:31,588 DEBUG: Start: Predicting
-2016-09-06 08:11:31,592 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:31,592 DEBUG: Start: Training
-2016-09-06 08:11:31,609 DEBUG: Done: Predicting
-2016-09-06 08:11:31,609 DEBUG: Start: Getting Results
-2016-09-06 08:11:31,610 DEBUG: Done: Getting Results
-2016-09-06 08:11:31,610 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,610 INFO: Done: Result Analysis
-2016-09-06 08:11:31,613 DEBUG: Info: Time for Training: 0.110257148743[s]
-2016-09-06 08:11:31,613 DEBUG: Done: Training
-2016-09-06 08:11:31,613 DEBUG: Start: Predicting
-2016-09-06 08:11:31,616 DEBUG: Done: Predicting
-2016-09-06 08:11:31,616 DEBUG: Start: Getting Results
-2016-09-06 08:11:31,617 DEBUG: Done: Getting Results
-2016-09-06 08:11:31,618 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,618 INFO: Done: Result Analysis
-2016-09-06 08:11:31,750 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,751 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:11:31,751 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:31,751 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:31,751 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:11:31,751 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:31,752 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,752 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:11:31,752 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,752 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:11:31,752 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:31,752 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:31,753 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,753 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:31,845 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:31,845 DEBUG: Start: Training
-2016-09-06 08:11:31,848 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:31,848 DEBUG: Start: Training
-2016-09-06 08:11:31,862 DEBUG: Info: Time for Training: 0.112237930298[s]
-2016-09-06 08:11:31,862 DEBUG: Done: Training
-2016-09-06 08:11:31,862 DEBUG: Start: Predicting
-2016-09-06 08:11:31,866 DEBUG: Info: Time for Training: 0.116894960403[s]
-2016-09-06 08:11:31,867 DEBUG: Done: Training
-2016-09-06 08:11:31,867 DEBUG: Start: Predicting
-2016-09-06 08:11:31,868 DEBUG: Done: Predicting
-2016-09-06 08:11:31,869 DEBUG: Start: Getting Results
-2016-09-06 08:11:31,870 DEBUG: Done: Getting Results
-2016-09-06 08:11:31,870 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,870 INFO: Done: Result Analysis
-2016-09-06 08:11:31,870 DEBUG: Done: Predicting
-2016-09-06 08:11:31,871 DEBUG: Start: Getting Results
-2016-09-06 08:11:31,872 DEBUG: Done: Getting Results
-2016-09-06 08:11:31,872 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:11:31,872 INFO: Done: Result Analysis
-2016-09-06 08:11:32,003 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,003 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,004 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:11:32,004 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:11:32,004 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:32,004 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:32,004 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,004 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,005 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,005 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,005 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:32,005 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:32,005 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,005 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,062 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:32,062 DEBUG: Start: Training
-2016-09-06 08:11:32,063 DEBUG: Info: Time for Training: 0.059504032135[s]
-2016-09-06 08:11:32,063 DEBUG: Done: Training
-2016-09-06 08:11:32,063 DEBUG: Start: Predicting
-2016-09-06 08:11:32,065 DEBUG: Done: Predicting
-2016-09-06 08:11:32,065 DEBUG: Start: Getting Results
-2016-09-06 08:11:32,066 DEBUG: Done: Getting Results
-2016-09-06 08:11:32,066 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.628571428571
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,067 INFO: Done: Result Analysis
-2016-09-06 08:11:32,095 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:32,096 DEBUG: Start: Training
-2016-09-06 08:11:32,100 DEBUG: Info: Time for Training: 0.0969760417938[s]
-2016-09-06 08:11:32,100 DEBUG: Done: Training
-2016-09-06 08:11:32,100 DEBUG: Start: Predicting
-2016-09-06 08:11:32,103 DEBUG: Done: Predicting
-2016-09-06 08:11:32,103 DEBUG: Start: Getting Results
-2016-09-06 08:11:32,105 DEBUG: Done: Getting Results
-2016-09-06 08:11:32,105 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,105 INFO: Done: Result Analysis
-2016-09-06 08:11:32,255 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,256 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,256 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:11:32,256 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:11:32,256 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:32,256 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:32,257 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,257 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,257 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,257 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,257 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:32,257 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:32,257 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,257 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,308 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:32,308 DEBUG: Start: Training
-2016-09-06 08:11:32,309 DEBUG: Info: Time for Training: 0.0539190769196[s]
-2016-09-06 08:11:32,309 DEBUG: Done: Training
-2016-09-06 08:11:32,309 DEBUG: Start: Predicting
-2016-09-06 08:11:32,315 DEBUG: Done: Predicting
-2016-09-06 08:11:32,315 DEBUG: Start: Getting Results
-2016-09-06 08:11:32,316 DEBUG: Done: Getting Results
-2016-09-06 08:11:32,316 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 6
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.738095238095
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,317 INFO: Done: Result Analysis
-2016-09-06 08:11:32,394 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:32,395 DEBUG: Start: Training
-2016-09-06 08:11:32,402 DEBUG: Info: Time for Training: 0.147303104401[s]
-2016-09-06 08:11:32,403 DEBUG: Done: Training
-2016-09-06 08:11:32,403 DEBUG: Start: Predicting
-2016-09-06 08:11:32,406 DEBUG: Done: Predicting
-2016-09-06 08:11:32,406 DEBUG: Start: Getting Results
-2016-09-06 08:11:32,407 DEBUG: Done: Getting Results
-2016-09-06 08:11:32,407 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.714285714286
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.714285714286
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,407 INFO: Done: Result Analysis
-2016-09-06 08:11:32,500 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,500 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:11:32,500 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:32,501 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:11:32,502 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,502 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:11:32,502 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,502 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:11:32,502 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:32,502 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,503 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:11:32,503 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:11:32,503 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:11:32,503 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:11:32,585 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:32,585 DEBUG: Start: Training
-2016-09-06 08:11:32,586 DEBUG: Info: Time for Training: 0.0862069129944[s]
-2016-09-06 08:11:32,586 DEBUG: Done: Training
-2016-09-06 08:11:32,587 DEBUG: Start: Predicting
-2016-09-06 08:11:32,591 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:11:32,592 DEBUG: Start: Training
-2016-09-06 08:11:32,601 DEBUG: Done: Predicting
-2016-09-06 08:11:32,601 DEBUG: Start: Getting Results
-2016-09-06 08:11:32,602 DEBUG: Done: Getting Results
-2016-09-06 08:11:32,602 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.652380952381
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,603 INFO: Done: Result Analysis
-2016-09-06 08:11:32,613 DEBUG: Info: Time for Training: 0.113891839981[s]
-2016-09-06 08:11:32,614 DEBUG: Done: Training
-2016-09-06 08:11:32,614 DEBUG: Start: Predicting
-2016-09-06 08:11:32,617 DEBUG: Done: Predicting
-2016-09-06 08:11:32,617 DEBUG: Start: Getting Results
-2016-09-06 08:11:32,618 DEBUG: Done: Getting Results
-2016-09-06 08:11:32,619 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.471428571429
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:11:32,619 INFO: Done: Result Analysis
-2016-09-06 08:11:32,891 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:32,892 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:11:32,892 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:32,892 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:32,892 INFO: Info: Shape of View0 :(300, 7)
-2016-09-06 08:11:32,893 INFO: Info: Shape of View0 :(300, 7)
-2016-09-06 08:11:32,893 INFO: Info: Shape of View1 :(300, 5)
-2016-09-06 08:11:32,894 INFO: Info: Shape of View1 :(300, 5)
-2016-09-06 08:11:32,894 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:11:32,895 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:11:32,895 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:11:32,895 INFO: Done: Read Database Files
-2016-09-06 08:11:32,895 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:11:32,895 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:11:32,896 INFO: Done: Read Database Files
-2016-09-06 08:11:32,896 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:11:32,901 INFO: Done: Determine validation split
-2016-09-06 08:11:32,901 INFO: Start: Determine 5 folds
-2016-09-06 08:11:32,903 INFO: Done: Determine validation split
-2016-09-06 08:11:32,903 INFO: Start: Determine 5 folds
-2016-09-06 08:11:32,910 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:11:32,911 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:11:32,911 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:11:32,911 INFO: Done: Determine folds
-2016-09-06 08:11:32,911 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:11:32,911 INFO: Start: Classification
-2016-09-06 08:11:32,911 INFO: Start: Fold number 1
-2016-09-06 08:11:32,913 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:11:32,914 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:11:32,914 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:11:32,914 INFO: Done: Determine folds
-2016-09-06 08:11:32,914 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:11:32,914 INFO: Start: Classification
-2016-09-06 08:11:32,914 INFO: Start: Fold number 1
-2016-09-06 08:11:32,953 DEBUG: Start: Iteration 1
-2016-09-06 08:11:32,960 DEBUG: View 0 : 0.535519125683
-2016-09-06 08:11:32,969 DEBUG: View 1 : 0.551912568306
-2016-09-06 08:11:32,971 INFO: Start: Classification
-2016-09-06 08:11:32,976 DEBUG: View 2 : 0.535519125683
-2016-09-06 08:11:32,984 DEBUG: View 3 : 0.513661202186
-2016-09-06 08:11:33,023 DEBUG: Best view : View0
-2016-09-06 08:11:33,027 INFO: Done: Fold number 1
-2016-09-06 08:11:33,027 INFO: Start: Fold number 2
-2016-09-06 08:11:33,081 INFO: Start: Classification
-2016-09-06 08:11:33,110 INFO: Done: Fold number 2
-2016-09-06 08:11:33,110 INFO: Start: Fold number 3
-2016-09-06 08:11:33,116 DEBUG: Start: Iteration 2
-2016-09-06 08:11:33,124 DEBUG: View 0 : 0.661202185792
-2016-09-06 08:11:33,132 DEBUG: View 1 : 0.590163934426
-2016-09-06 08:11:33,141 DEBUG: View 2 : 0.754098360656
-2016-09-06 08:11:33,151 DEBUG: View 3 : 0.743169398907
-2016-09-06 08:11:33,163 INFO: Start: Classification
-2016-09-06 08:11:33,191 INFO: Done: Fold number 3
-2016-09-06 08:11:33,191 INFO: Start: Fold number 4
-2016-09-06 08:11:33,199 DEBUG: Best view : View2
-2016-09-06 08:11:33,245 INFO: Start: Classification
-2016-09-06 08:11:33,273 INFO: Done: Fold number 4
-2016-09-06 08:11:33,273 INFO: Start: Fold number 5
-2016-09-06 08:11:33,328 INFO: Start: Classification
-2016-09-06 08:11:33,357 INFO: Done: Fold number 5
-2016-09-06 08:11:33,357 INFO: Done: Classification
-2016-09-06 08:11:33,357 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:11:33,357 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:11:33,361 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 70.9440472969
- -On Test : 56.6666666667
- -On Validation : 66.2222222222
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - Random Forest with num_esimators : 3, max_depth : 2
- - SVM Linear with C : 5501
- - Random Forest with num_esimators : 3, max_depth : 2
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:11:33,362 INFO: Done: Result Analysis
-2016-09-06 08:11:33,379 DEBUG: Start: Iteration 3
-2016-09-06 08:11:33,393 DEBUG: View 0 : 0.661202185792
-2016-09-06 08:11:33,400 DEBUG: View 1 : 0.590163934426
-2016-09-06 08:11:33,407 DEBUG: View 2 : 0.754098360656
-2016-09-06 08:11:33,415 DEBUG: View 3 : 0.743169398907
-2016-09-06 08:11:33,456 DEBUG: Best view : View2
-2016-09-06 08:11:33,673 INFO: Start: Classification
-2016-09-06 08:11:34,028 INFO: Done: Fold number 1
-2016-09-06 08:11:34,028 INFO: Start: Fold number 2
-2016-09-06 08:11:34,057 DEBUG: Start: Iteration 1
-2016-09-06 08:11:34,064 DEBUG: View 0 : 0.579545454545
-2016-09-06 08:11:34,071 DEBUG: View 1 : 0.471590909091
-2016-09-06 08:11:34,078 DEBUG: View 2 : 0.4375
-2016-09-06 08:11:34,085 DEBUG: View 3 : 0.471590909091
-2016-09-06 08:11:34,115 DEBUG: Best view : View2
-2016-09-06 08:11:34,193 DEBUG: Start: Iteration 2
-2016-09-06 08:11:34,200 DEBUG: View 0 : 0.664772727273
-2016-09-06 08:11:34,207 DEBUG: View 1 : 0.670454545455
-2016-09-06 08:11:34,213 DEBUG: View 2 : 0.642045454545
-2016-09-06 08:11:34,221 DEBUG: View 3 : 0.784090909091
-2016-09-06 08:11:34,258 DEBUG: Best view : View3
-2016-09-06 08:11:34,401 DEBUG: Start: Iteration 3
-2016-09-06 08:11:34,408 DEBUG: View 0 : 0.664772727273
-2016-09-06 08:11:34,415 DEBUG: View 1 : 0.670454545455
-2016-09-06 08:11:34,422 DEBUG: View 2 : 0.642045454545
-2016-09-06 08:11:34,430 DEBUG: View 3 : 0.784090909091
-2016-09-06 08:11:34,469 DEBUG: Best view : View3
-2016-09-06 08:11:34,679 DEBUG: Start: Iteration 4
-2016-09-06 08:11:34,685 DEBUG: View 0 : 0.653409090909
-2016-09-06 08:11:34,692 DEBUG: View 1 : 0.613636363636
-2016-09-06 08:11:34,699 DEBUG: View 2 : 0.676136363636
-2016-09-06 08:11:34,707 DEBUG: View 3 : 0.744318181818
-2016-09-06 08:11:34,748 DEBUG: Best view : View3
-2016-09-06 08:11:35,024 INFO: Start: Classification
-2016-09-06 08:11:35,486 INFO: Done: Fold number 2
-2016-09-06 08:11:35,486 INFO: Start: Fold number 3
-2016-09-06 08:11:35,515 DEBUG: Start: Iteration 1
-2016-09-06 08:11:35,522 DEBUG: View 0 : 0.522727272727
-2016-09-06 08:11:35,529 DEBUG: View 1 : 0.460227272727
-2016-09-06 08:11:35,536 DEBUG: View 2 : 0.5
-2016-09-06 08:11:35,543 DEBUG: View 3 : 0.477272727273
-2016-09-06 08:11:35,573 DEBUG: Best view : View0
-2016-09-06 08:11:35,651 DEBUG: Start: Iteration 2
-2016-09-06 08:11:35,657 DEBUG: View 0 : 0.698863636364
-2016-09-06 08:11:35,664 DEBUG: View 1 : 0.619318181818
-2016-09-06 08:11:35,671 DEBUG: View 2 : 0.693181818182
-2016-09-06 08:11:35,678 DEBUG: View 3 : 0.721590909091
-2016-09-06 08:11:35,715 DEBUG: Best view : View3
-2016-09-06 08:11:35,859 DEBUG: Start: Iteration 3
-2016-09-06 08:11:35,865 DEBUG: View 0 : 0.698863636364
-2016-09-06 08:11:35,872 DEBUG: View 1 : 0.619318181818
-2016-09-06 08:11:35,879 DEBUG: View 2 : 0.676136363636
-2016-09-06 08:11:35,887 DEBUG: View 3 : 0.721590909091
-2016-09-06 08:11:35,926 DEBUG: Best view : View2
-2016-09-06 08:11:36,134 INFO: Start: Classification
-2016-09-06 08:11:36,479 INFO: Done: Fold number 3
-2016-09-06 08:11:36,479 INFO: Start: Fold number 4
-2016-09-06 08:11:36,508 DEBUG: Start: Iteration 1
-2016-09-06 08:11:36,515 DEBUG: View 0 : 0.525139664804
-2016-09-06 08:11:36,522 DEBUG: View 1 : 0.541899441341
-2016-09-06 08:11:36,528 DEBUG: View 2 : 0.536312849162
-2016-09-06 08:11:36,536 DEBUG: View 3 : 0.474860335196
-2016-09-06 08:11:36,567 DEBUG: Best view : View3
-2016-09-06 08:11:36,645 DEBUG: Start: Iteration 2
-2016-09-06 08:11:36,652 DEBUG: View 0 : 0.703910614525
-2016-09-06 08:11:36,659 DEBUG: View 1 : 0.586592178771
-2016-09-06 08:11:36,666 DEBUG: View 2 : 0.72625698324
-2016-09-06 08:11:36,674 DEBUG: View 3 : 0.709497206704
-2016-09-06 08:11:36,711 DEBUG: Best view : View2
-2016-09-06 08:11:36,856 DEBUG: Start: Iteration 3
-2016-09-06 08:11:36,863 DEBUG: View 0 : 0.687150837989
-2016-09-06 08:11:36,870 DEBUG: View 1 : 0.586592178771
-2016-09-06 08:11:36,877 DEBUG: View 2 : 0.72625698324
-2016-09-06 08:11:36,884 DEBUG: View 3 : 0.709497206704
-2016-09-06 08:11:36,924 DEBUG: Best view : View0
-2016-09-06 08:11:37,136 INFO: Start: Classification
-2016-09-06 08:11:37,486 INFO: Done: Fold number 4
-2016-09-06 08:11:37,486 INFO: Start: Fold number 5
-2016-09-06 08:11:37,516 DEBUG: Start: Iteration 1
-2016-09-06 08:11:37,523 DEBUG: View 0 : 0.524590163934
-2016-09-06 08:11:37,529 DEBUG: View 1 : 0.524590163934
-2016-09-06 08:11:37,536 DEBUG: View 2 : 0.524590163934
-2016-09-06 08:11:37,542 DEBUG: View 3 : 0.524590163934
-2016-09-06 08:11:37,574 DEBUG: Best view : View0
-2016-09-06 08:11:37,653 DEBUG: Start: Iteration 2
-2016-09-06 08:11:37,660 DEBUG: View 0 : 0.693989071038
-2016-09-06 08:11:37,667 DEBUG: View 1 : 0.677595628415
-2016-09-06 08:11:37,674 DEBUG: View 2 : 0.68306010929
-2016-09-06 08:11:37,682 DEBUG: View 3 : 0.72131147541
-2016-09-06 08:11:37,720 DEBUG: Best view : View3
-2016-09-06 08:11:37,868 DEBUG: Start: Iteration 3
-2016-09-06 08:11:37,875 DEBUG: View 0 : 0.693989071038
-2016-09-06 08:11:37,882 DEBUG: View 1 : 0.677595628415
-2016-09-06 08:11:37,890 DEBUG: View 2 : 0.68306010929
-2016-09-06 08:11:37,897 DEBUG: View 3 : 0.72131147541
-2016-09-06 08:11:37,938 DEBUG: Best view : View3
-2016-09-06 08:11:38,155 INFO: Start: Classification
-2016-09-06 08:11:38,509 INFO: Done: Fold number 5
-2016-09-06 08:11:38,509 INFO: Done: Classification
-2016-09-06 08:11:38,509 INFO: Info: Time for Classification: 5[s]
-2016-09-06 08:11:38,509 INFO: Start: Result Analysis for Mumbo
-2016-09-06 08:11:40,533 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 72.4557589356
- -On Test : 49.0476190476
- -On Validation : 62.6666666667Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 7), View1 of shape (300, 5), View2 of shape (300, 17), View3 of shape (300, 20)
- -5 folds
- - Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:04 0:00:00
- Fold 5 0:00:05 0:00:00
- Total 0:00:15 0:00:01
- So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 4
- - On View0 :
- - Mean average Accuracy : 0.185792349727
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.173224043716
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.204371584699
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.2
- - Percentage of time chosen : 0.0
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.25625
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.242613636364
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.239772727273
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.278409090909
- - Percentage of time chosen : 0.3
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.192045454545
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.169886363636
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.186931818182
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.192045454545
- - Percentage of time chosen : 0.1
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.191620111732
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.171508379888
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.198882681564
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.18938547486
- - Percentage of time chosen : 0.1
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.191256830601
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.187978142077
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.189071038251
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.196721311475
- - Percentage of time chosen : 0.2
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 52.4590163934
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 52.8409090909
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View2
- Fold 3
- Accuracy on train : 53.4090909091
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 51.9553072626
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View3
- Fold 5
- Accuracy on train : 52.4590163934
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 75.4098360656
- Accuracy on test : 0.0
- Accuracy on validation : 60.0
- Selected View : View2
- Fold 2
- Accuracy on train : 78.4090909091
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View3
- Fold 3
- Accuracy on train : 72.1590909091
- Accuracy on test : 0.0
- Accuracy on validation : 70.0
- Selected View : View3
- Fold 4
- Accuracy on train : 72.625698324
- Accuracy on test : 0.0
- Accuracy on validation : 62.2222222222
- Selected View : View2
- Fold 5
- Accuracy on train : 72.131147541
- Accuracy on test : 0.0
- Accuracy on validation : 66.6666666667
- Selected View : View3
- - Iteration 3
- Fold 1
- Accuracy on train : 75.4098360656
- Accuracy on test : 0.0
- Accuracy on validation : 60.0
- Selected View : View2
- Fold 2
- Accuracy on train : 78.4090909091
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View3
- Fold 3
- Accuracy on train : 67.6136363636
- Accuracy on test : 0.0
- Accuracy on validation : 56.6666666667
- Selected View : View2
- Fold 4
- Accuracy on train : 68.7150837989
- Accuracy on test : 0.0
- Accuracy on validation : 62.2222222222
- Selected View : View0
- Fold 5
- Accuracy on train : 72.131147541
- Accuracy on test : 0.0
- Accuracy on validation : 66.6666666667
- Selected View : View3
- - Iteration 4
- Fold 1
- Accuracy on train : 52.4590163934
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 78.4090909091
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View3
- Fold 3
- Accuracy on train : 53.4090909091
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 51.9553072626
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 5
- Accuracy on train : 52.4590163934
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- - Iteration 5
- Fold 2
- Accuracy on train : 52.8409090909
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
-2016-09-06 08:11:40,716 INFO: Done: Result Analysis
-2016-09-06 08:11:40,858 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:40,858 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:40,858 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:40,859 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:40,859 INFO: Info: Shape of View0 :(300, 7)
-2016-09-06 08:11:40,859 INFO: Info: Shape of View0 :(300, 7)
-2016-09-06 08:11:40,860 INFO: Info: Shape of View1 :(300, 5)
-2016-09-06 08:11:40,860 INFO: Info: Shape of View1 :(300, 5)
-2016-09-06 08:11:40,860 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:11:40,860 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:11:40,861 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:11:40,861 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:11:40,861 INFO: Done: Read Database Files
-2016-09-06 08:11:40,861 INFO: Done: Read Database Files
-2016-09-06 08:11:40,861 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:11:40,861 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:11:40,867 INFO: Done: Determine validation split
-2016-09-06 08:11:40,867 INFO: Start: Determine 5 folds
-2016-09-06 08:11:40,867 INFO: Done: Determine validation split
-2016-09-06 08:11:40,867 INFO: Start: Determine 5 folds
-2016-09-06 08:11:40,877 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:11:40,877 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:11:40,877 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:11:40,877 INFO: Done: Determine folds
-2016-09-06 08:11:40,877 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:11:40,877 INFO: Start: Classification
-2016-09-06 08:11:40,877 INFO: Start: Fold number 1
-2016-09-06 08:11:40,878 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:11:40,878 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:11:40,878 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:11:40,878 INFO: Done: Determine folds
-2016-09-06 08:11:40,878 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:11:40,878 INFO: Start: Classification
-2016-09-06 08:11:40,878 INFO: Start: Fold number 1
-2016-09-06 08:11:40,937 INFO: Start: Classification
-2016-09-06 08:11:40,964 INFO: Start: Classification
-2016-09-06 08:11:41,000 INFO: Done: Fold number 1
-2016-09-06 08:11:41,000 INFO: Start: Fold number 2
-2016-09-06 08:11:41,029 INFO: Done: Fold number 1
-2016-09-06 08:11:41,030 INFO: Start: Fold number 2
-2016-09-06 08:11:41,075 INFO: Start: Classification
-2016-09-06 08:11:41,084 INFO: Start: Classification
-2016-09-06 08:11:41,107 INFO: Done: Fold number 2
-2016-09-06 08:11:41,107 INFO: Start: Fold number 3
-2016-09-06 08:11:41,159 INFO: Done: Fold number 2
-2016-09-06 08:11:41,159 INFO: Start: Fold number 3
-2016-09-06 08:11:41,180 INFO: Start: Classification
-2016-09-06 08:11:41,212 INFO: Done: Fold number 3
-2016-09-06 08:11:41,212 INFO: Start: Fold number 4
-2016-09-06 08:11:41,214 INFO: Start: Classification
-2016-09-06 08:11:41,288 INFO: Start: Classification
-2016-09-06 08:11:41,289 INFO: Done: Fold number 3
-2016-09-06 08:11:41,289 INFO: Start: Fold number 4
-2016-09-06 08:11:41,321 INFO: Done: Fold number 4
-2016-09-06 08:11:41,321 INFO: Start: Fold number 5
-2016-09-06 08:11:41,343 INFO: Start: Classification
-2016-09-06 08:11:41,395 INFO: Start: Classification
-2016-09-06 08:11:41,417 INFO: Done: Fold number 4
-2016-09-06 08:11:41,418 INFO: Start: Fold number 5
-2016-09-06 08:11:41,429 INFO: Done: Fold number 5
-2016-09-06 08:11:41,429 INFO: Done: Classification
-2016-09-06 08:11:41,429 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:11:41,429 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:11:41,434 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 71.9436574753
- -On Test : 50.4761904762
- -On Validation : 69.7777777778
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - Random Forest with num_esimators : 3, max_depth : 2
- - SVM Linear with C : 5501
- - Random Forest with num_esimators : 3, max_depth : 2
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:11:41,434 INFO: Done: Result Analysis
-2016-09-06 08:11:41,471 INFO: Start: Classification
-2016-09-06 08:11:41,543 INFO: Done: Fold number 5
-2016-09-06 08:11:41,544 INFO: Done: Classification
-2016-09-06 08:11:41,544 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:11:41,544 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:11:41,548 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 67.9066961467
- -On Test : 56.1904761905
- -On Validation : 61.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - Random Forest with num_esimators : 3, max_depth : 2
- - SVM Linear with C : 5501
- - Random Forest with num_esimators : 3, max_depth : 2
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:11:41,548 INFO: Done: Result Analysis
-2016-09-06 08:11:41,611 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:41,611 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:41,612 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:41,612 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:41,613 INFO: Info: Shape of View0 :(300, 7)
-2016-09-06 08:11:41,613 INFO: Info: Shape of View0 :(300, 7)
-2016-09-06 08:11:41,613 INFO: Info: Shape of View1 :(300, 5)
-2016-09-06 08:11:41,613 INFO: Info: Shape of View1 :(300, 5)
-2016-09-06 08:11:41,614 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:11:41,614 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:11:41,615 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:11:41,615 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:11:41,615 INFO: Done: Read Database Files
-2016-09-06 08:11:41,615 INFO: Done: Read Database Files
-2016-09-06 08:11:41,616 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:11:41,616 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:11:41,624 INFO: Done: Determine validation split
-2016-09-06 08:11:41,624 INFO: Done: Determine validation split
-2016-09-06 08:11:41,624 INFO: Start: Determine 5 folds
-2016-09-06 08:11:41,624 INFO: Start: Determine 5 folds
-2016-09-06 08:11:41,636 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:11:41,636 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:11:41,637 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:11:41,637 INFO: Done: Determine folds
-2016-09-06 08:11:41,637 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:11:41,637 INFO: Start: Classification
-2016-09-06 08:11:41,637 INFO: Start: Fold number 1
-2016-09-06 08:11:41,639 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:11:41,639 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:11:41,639 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:11:41,639 INFO: Done: Determine folds
-2016-09-06 08:11:41,639 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:11:41,639 INFO: Start: Classification
-2016-09-06 08:11:41,640 INFO: Start: Fold number 1
-2016-09-06 08:11:41,681 INFO: Start: Classification
-2016-09-06 08:11:41,722 INFO: Done: Fold number 1
-2016-09-06 08:11:41,722 INFO: Start: Fold number 2
-2016-09-06 08:11:41,732 INFO: Start: Classification
-2016-09-06 08:11:41,760 INFO: Start: Classification
-2016-09-06 08:11:41,792 INFO: Done: Fold number 2
-2016-09-06 08:11:41,792 INFO: Start: Fold number 3
-2016-09-06 08:11:41,793 INFO: Done: Fold number 1
-2016-09-06 08:11:41,793 INFO: Start: Fold number 2
-2016-09-06 08:11:41,815 INFO: Start: Classification
-2016-09-06 08:11:41,840 INFO: Done: Fold number 3
-2016-09-06 08:11:41,840 INFO: Start: Fold number 4
-2016-09-06 08:11:41,849 INFO: Start: Classification
-2016-09-06 08:11:41,865 INFO: Start: Classification
-2016-09-06 08:11:41,883 INFO: Done: Fold number 2
-2016-09-06 08:11:41,883 INFO: Start: Fold number 3
-2016-09-06 08:11:41,890 INFO: Done: Fold number 4
-2016-09-06 08:11:41,890 INFO: Start: Fold number 5
-2016-09-06 08:11:41,912 INFO: Start: Classification
-2016-09-06 08:11:41,937 INFO: Done: Fold number 5
-2016-09-06 08:11:41,937 INFO: Done: Classification
-2016-09-06 08:11:41,937 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:11:41,937 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:11:41,938 INFO: Start: Classification
-2016-09-06 08:11:41,942 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 49.5238095238
- -On Validation : 84.8888888889
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:11:41,942 INFO: Done: Result Analysis
-2016-09-06 08:11:41,972 INFO: Done: Fold number 3
-2016-09-06 08:11:41,972 INFO: Start: Fold number 4
-2016-09-06 08:11:42,024 INFO: Start: Classification
-2016-09-06 08:11:42,057 INFO: Done: Fold number 4
-2016-09-06 08:11:42,058 INFO: Start: Fold number 5
-2016-09-06 08:11:42,111 INFO: Start: Classification
-2016-09-06 08:11:42,144 INFO: Done: Fold number 5
-2016-09-06 08:11:42,144 INFO: Done: Classification
-2016-09-06 08:11:42,144 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:11:42,144 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:11:42,149 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 10.3999960613
- -On Test : 9.04761904762
- -On Validation : 9.55555555556
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - Random Forest with num_esimators : 3, max_depth : 2
- - SVM Linear with C : 5501
- - Random Forest with num_esimators : 3, max_depth : 2
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:11:42,149 INFO: Done: Result Analysis
-2016-09-06 08:11:42,262 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:42,262 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:11:42,262 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:42,262 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:11:42,263 INFO: Info: Shape of View0 :(300, 7)
-2016-09-06 08:11:42,263 INFO: Info: Shape of View0 :(300, 7)
-2016-09-06 08:11:42,263 INFO: Info: Shape of View1 :(300, 5)
-2016-09-06 08:11:42,263 INFO: Info: Shape of View1 :(300, 5)
-2016-09-06 08:11:42,264 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:11:42,264 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:11:42,264 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:11:42,264 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:11:42,264 INFO: Done: Read Database Files
-2016-09-06 08:11:42,264 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:11:42,264 INFO: Done: Read Database Files
-2016-09-06 08:11:42,264 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:11:42,270 INFO: Done: Determine validation split
-2016-09-06 08:11:42,270 INFO: Done: Determine validation split
-2016-09-06 08:11:42,270 INFO: Start: Determine 5 folds
-2016-09-06 08:11:42,270 INFO: Start: Determine 5 folds
-2016-09-06 08:11:42,281 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:11:42,281 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:11:42,281 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:11:42,281 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:11:42,282 INFO: Done: Determine folds
-2016-09-06 08:11:42,282 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:11:42,282 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:11:42,282 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:11:42,282 INFO: Start: Classification
-2016-09-06 08:11:42,282 INFO: Done: Determine folds
-2016-09-06 08:11:42,282 INFO: Start: Fold number 1
-2016-09-06 08:11:42,282 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:11:42,282 INFO: Start: Classification
-2016-09-06 08:11:42,282 INFO: Start: Fold number 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c0cb8a00a039615db9bc5902b87cc3edc5c38456..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 841bc5e7e828bd334b2ee331852733411cc96651..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 21d202329a63c54854fa995e30cd9f86c276268f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.671428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.671428571429
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0724566e516d7fcb8537a0fc5619a015c250b0ab..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.638095238095
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.638095238095
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e4a004d2a7436d18aa6dff077aeffcd045aea1d6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ec5fd5cc8b867d2a5e73badd9c596945e6b10aa9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db4896c6fbb93bcfe208929812442a5ec41aefa2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5059
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9abab3addc0259fe5c8616e58bb2e3f8676e0301..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081129Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7a4f3e071e2f9dd4ed2447872183e78b7f9814da..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 023fb03b49dac82b8e9efb65ae8c02b942d8c4ec..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 29
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 22f0d0831a716f8b766ba2f7968a92a6ff99d6f5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.680952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 6
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.680952380952
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9e35d2e7430de1c71a9ba1e48ce3dcb3863b309b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b02bf490ce906dbfbf49ec7f0a6dfbfabf062d15..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cd534e7802a95a9ca1a994b5646ef2f1c84c59e2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.452380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.452380952381
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4703bb45a1e75c69c4a0c3f5f4922a340b4a371c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bb42a14aa972c67bfec4498b805ea6497ba02088..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081130Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 33fd6ac9916fb4227d725df1437898eedd327875..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a010ca9a45e22b09d2551bfa080f8c7638e1ef30..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 26b9fae1632a3d51dcee0939885e07f9b89a9419..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.77619047619
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.77619047619
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fdb9c4cf7ac259459fd28107743584d25d7d02b3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.633333333333
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.633333333333
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 46fdee0441a346e3fcd0d09fccd37e87244f4c29..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7cb6798705b3d77f4387f7e9deec6e92efb24671..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 408a48a633c5fe6bb6ef27a7e016b942cfc2f472..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b06ad467add11896763af5dcf1e87e3a5e3fb915..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081131Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 30ac7fadc79c429e0b9d6fd4385788cd722245c7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6c528ab18419c96e345072b5afd7262827fc114d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.628571428571
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 712fa6e92957e019278ad1f43fae6855dd0585de..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 6
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.738095238095
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 12e0b666f862741454d77322daad05e4bb291366..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.714285714286
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.714285714286
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5a304273469d9bf81907a37b741e39ae4256ef43..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.652380952381
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ea26c2dccdff6e68b93d07c2425d15869cf55b3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081132Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5501
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.471428571429
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081133Results-Fusion-LateFusion-BayesianInference-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081133Results-Fusion-LateFusion-BayesianInference-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index a83cf9eb586d5fb797280d7475ec877d41466c35..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081133Results-Fusion-LateFusion-BayesianInference-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 70.9440472969
- -On Test : 56.6666666667
- -On Validation : 66.2222222222
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - Random Forest with num_esimators : 3, max_depth : 2
- - SVM Linear with C : 5501
- - Random Forest with num_esimators : 3, max_depth : 2
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index ca337129d2391976ad45a56a17bba9bd4e88a666..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index ef6fa379e0f1d2230aa6e84ac4e80061ef9353a4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081140Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,215 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 72.4557589356
- -On Test : 49.0476190476
- -On Validation : 62.6666666667Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 7), View1 of shape (300, 5), View2 of shape (300, 17), View3 of shape (300, 20)
- -5 folds
- - Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:04 0:00:00
- Fold 5 0:00:05 0:00:00
- Total 0:00:15 0:00:01
- So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 4
- - On View0 :
- - Mean average Accuracy : 0.185792349727
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.173224043716
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.204371584699
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.2
- - Percentage of time chosen : 0.0
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.25625
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.242613636364
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.239772727273
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.278409090909
- - Percentage of time chosen : 0.3
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.192045454545
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.169886363636
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.186931818182
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.192045454545
- - Percentage of time chosen : 0.1
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.191620111732
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.171508379888
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.198882681564
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.18938547486
- - Percentage of time chosen : 0.1
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.191256830601
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.187978142077
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.189071038251
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.196721311475
- - Percentage of time chosen : 0.2
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 52.4590163934
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 52.8409090909
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View2
- Fold 3
- Accuracy on train : 53.4090909091
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 51.9553072626
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View3
- Fold 5
- Accuracy on train : 52.4590163934
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 75.4098360656
- Accuracy on test : 0.0
- Accuracy on validation : 60.0
- Selected View : View2
- Fold 2
- Accuracy on train : 78.4090909091
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View3
- Fold 3
- Accuracy on train : 72.1590909091
- Accuracy on test : 0.0
- Accuracy on validation : 70.0
- Selected View : View3
- Fold 4
- Accuracy on train : 72.625698324
- Accuracy on test : 0.0
- Accuracy on validation : 62.2222222222
- Selected View : View2
- Fold 5
- Accuracy on train : 72.131147541
- Accuracy on test : 0.0
- Accuracy on validation : 66.6666666667
- Selected View : View3
- - Iteration 3
- Fold 1
- Accuracy on train : 75.4098360656
- Accuracy on test : 0.0
- Accuracy on validation : 60.0
- Selected View : View2
- Fold 2
- Accuracy on train : 78.4090909091
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View3
- Fold 3
- Accuracy on train : 67.6136363636
- Accuracy on test : 0.0
- Accuracy on validation : 56.6666666667
- Selected View : View2
- Fold 4
- Accuracy on train : 68.7150837989
- Accuracy on test : 0.0
- Accuracy on validation : 62.2222222222
- Selected View : View0
- Fold 5
- Accuracy on train : 72.131147541
- Accuracy on test : 0.0
- Accuracy on validation : 66.6666666667
- Selected View : View3
- - Iteration 4
- Fold 1
- Accuracy on train : 52.4590163934
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 78.4090909091
- Accuracy on test : 0.0
- Accuracy on validation : 67.7777777778
- Selected View : View3
- Fold 3
- Accuracy on train : 53.4090909091
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 51.9553072626
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 5
- Accuracy on train : 52.4590163934
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- - Iteration 5
- Fold 2
- Accuracy on train : 52.8409090909
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 793ed7da591e2b79d35f655325f4e90400482789..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 49.5238095238
- -On Validation : 84.8888888889
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-MajorityVoting-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-MajorityVoting-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 1c144111f5f706c98ace6741924c4bd236469869..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-MajorityVoting-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 67.9066961467
- -On Test : 56.1904761905
- -On Validation : 61.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - Random Forest with num_esimators : 3, max_depth : 2
- - SVM Linear with C : 5501
- - Random Forest with num_esimators : 3, max_depth : 2
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-SVMForLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-SVMForLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 4253761dbf1db87933c1f3a2d65eea47f19f6e2c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081141Results-Fusion-LateFusion-SVMForLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 71.9436574753
- -On Test : 50.4761904762
- -On Validation : 69.7777777778
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - Random Forest with num_esimators : 3, max_depth : 2
- - SVM Linear with C : 5501
- - Random Forest with num_esimators : 3, max_depth : 2
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081142Results-Fusion-LateFusion-WeightedLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081142Results-Fusion-LateFusion-WeightedLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 73489cf0dbe868cf488edaebfd132cc039fe477b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081142Results-Fusion-LateFusion-WeightedLinear-RandomForest-SVMLinear-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 10.3999960613
- -On Test : 9.04761904762
- -On Validation : 9.55555555556
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - Random Forest with num_esimators : 3, max_depth : 2
- - SVM Linear with C : 5501
- - Random Forest with num_esimators : 3, max_depth : 2
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081346-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-081346-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index c46e481b1696fd2c7144ee6ece3f6d44ae712400..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081346-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1922 +0,0 @@
-2016-09-06 08:13:46,019 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:13:46,019 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000152125 Gbytes /!\
-2016-09-06 08:13:51,034 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:13:51,037 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:13:51,094 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,094 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,095 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:13:51,095 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:13:51,095 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:51,095 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:51,095 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,096 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,096 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,096 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,096 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:51,096 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:51,096 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,096 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,160 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:51,161 DEBUG: Start: Training
-2016-09-06 08:13:51,163 DEBUG: Info: Time for Training: 0.0696048736572[s]
-2016-09-06 08:13:51,163 DEBUG: Done: Training
-2016-09-06 08:13:51,163 DEBUG: Start: Predicting
-2016-09-06 08:13:51,166 DEBUG: Done: Predicting
-2016-09-06 08:13:51,166 DEBUG: Start: Getting Results
-2016-09-06 08:13:51,167 DEBUG: Done: Getting Results
-2016-09-06 08:13:51,167 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,167 INFO: Done: Result Analysis
-2016-09-06 08:13:51,187 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:51,187 DEBUG: Start: Training
-2016-09-06 08:13:51,191 DEBUG: Info: Time for Training: 0.0979900360107[s]
-2016-09-06 08:13:51,191 DEBUG: Done: Training
-2016-09-06 08:13:51,192 DEBUG: Start: Predicting
-2016-09-06 08:13:51,194 DEBUG: Done: Predicting
-2016-09-06 08:13:51,195 DEBUG: Start: Getting Results
-2016-09-06 08:13:51,196 DEBUG: Done: Getting Results
-2016-09-06 08:13:51,196 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,196 INFO: Done: Result Analysis
-2016-09-06 08:13:51,340 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,341 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:13:51,341 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:51,342 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,342 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:13:51,342 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,342 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:51,342 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,342 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:51,343 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,343 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,343 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,343 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:51,343 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,394 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:51,394 DEBUG: Start: Training
-2016-09-06 08:13:51,395 DEBUG: Info: Time for Training: 0.0554051399231[s]
-2016-09-06 08:13:51,395 DEBUG: Done: Training
-2016-09-06 08:13:51,395 DEBUG: Start: Predicting
-2016-09-06 08:13:51,402 DEBUG: Done: Predicting
-2016-09-06 08:13:51,402 DEBUG: Start: Getting Results
-2016-09-06 08:13:51,404 DEBUG: Done: Getting Results
-2016-09-06 08:13:51,404 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 32
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,404 INFO: Done: Result Analysis
-2016-09-06 08:13:51,645 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:51,645 DEBUG: Start: Training
-2016-09-06 08:13:51,677 DEBUG: Info: Time for Training: 0.336097955704[s]
-2016-09-06 08:13:51,677 DEBUG: Done: Training
-2016-09-06 08:13:51,677 DEBUG: Start: Predicting
-2016-09-06 08:13:51,682 DEBUG: Done: Predicting
-2016-09-06 08:13:51,682 DEBUG: Start: Getting Results
-2016-09-06 08:13:51,683 DEBUG: Done: Getting Results
-2016-09-06 08:13:51,683 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 11, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,683 INFO: Done: Result Analysis
-2016-09-06 08:13:51,788 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,788 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:51,788 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:13:51,788 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:13:51,789 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:51,789 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:51,789 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,789 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:51,789 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,789 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:51,790 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:51,790 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:51,790 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,790 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:51,872 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:51,872 DEBUG: Start: Training
-2016-09-06 08:13:51,873 DEBUG: Info: Time for Training: 0.0851731300354[s]
-2016-09-06 08:13:51,873 DEBUG: Done: Training
-2016-09-06 08:13:51,873 DEBUG: Start: Predicting
-2016-09-06 08:13:51,877 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:51,877 DEBUG: Start: Training
-2016-09-06 08:13:51,891 DEBUG: Done: Predicting
-2016-09-06 08:13:51,891 DEBUG: Start: Getting Results
-2016-09-06 08:13:51,893 DEBUG: Done: Getting Results
-2016-09-06 08:13:51,893 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,893 INFO: Done: Result Analysis
-2016-09-06 08:13:51,907 DEBUG: Info: Time for Training: 0.119738101959[s]
-2016-09-06 08:13:51,907 DEBUG: Done: Training
-2016-09-06 08:13:51,908 DEBUG: Start: Predicting
-2016-09-06 08:13:51,911 DEBUG: Done: Predicting
-2016-09-06 08:13:51,911 DEBUG: Start: Getting Results
-2016-09-06 08:13:51,912 DEBUG: Done: Getting Results
-2016-09-06 08:13:51,912 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:13:51,913 INFO: Done: Result Analysis
-2016-09-06 08:13:52,037 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,037 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:13:52,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:13:52,037 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:52,037 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:52,038 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:52,038 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:13:52,038 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:52,038 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:13:52,038 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:52,038 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:52,038 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,038 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,121 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:52,121 DEBUG: Start: Training
-2016-09-06 08:13:52,134 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:52,134 DEBUG: Start: Training
-2016-09-06 08:13:52,139 DEBUG: Info: Time for Training: 0.102441072464[s]
-2016-09-06 08:13:52,139 DEBUG: Done: Training
-2016-09-06 08:13:52,139 DEBUG: Start: Predicting
-2016-09-06 08:13:52,145 DEBUG: Done: Predicting
-2016-09-06 08:13:52,145 DEBUG: Start: Getting Results
-2016-09-06 08:13:52,146 DEBUG: Done: Getting Results
-2016-09-06 08:13:52,146 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,146 INFO: Done: Result Analysis
-2016-09-06 08:13:52,154 DEBUG: Info: Time for Training: 0.117784976959[s]
-2016-09-06 08:13:52,154 DEBUG: Done: Training
-2016-09-06 08:13:52,154 DEBUG: Start: Predicting
-2016-09-06 08:13:52,159 DEBUG: Done: Predicting
-2016-09-06 08:13:52,159 DEBUG: Start: Getting Results
-2016-09-06 08:13:52,160 DEBUG: Done: Getting Results
-2016-09-06 08:13:52,160 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5781
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,160 INFO: Done: Result Analysis
-2016-09-06 08:13:52,291 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,291 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:13:52,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:13:52,292 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:52,292 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:52,293 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,293 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,293 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:52,293 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,294 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,294 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,294 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:52,294 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,359 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:52,359 DEBUG: Start: Training
-2016-09-06 08:13:52,361 DEBUG: Info: Time for Training: 0.070493221283[s]
-2016-09-06 08:13:52,361 DEBUG: Done: Training
-2016-09-06 08:13:52,361 DEBUG: Start: Predicting
-2016-09-06 08:13:52,365 DEBUG: Done: Predicting
-2016-09-06 08:13:52,365 DEBUG: Start: Getting Results
-2016-09-06 08:13:52,366 DEBUG: Done: Getting Results
-2016-09-06 08:13:52,366 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,366 INFO: Done: Result Analysis
-2016-09-06 08:13:52,391 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:52,391 DEBUG: Start: Training
-2016-09-06 08:13:52,395 DEBUG: Info: Time for Training: 0.10508108139[s]
-2016-09-06 08:13:52,395 DEBUG: Done: Training
-2016-09-06 08:13:52,395 DEBUG: Start: Predicting
-2016-09-06 08:13:52,399 DEBUG: Done: Predicting
-2016-09-06 08:13:52,399 DEBUG: Start: Getting Results
-2016-09-06 08:13:52,401 DEBUG: Done: Getting Results
-2016-09-06 08:13:52,402 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,402 INFO: Done: Result Analysis
-2016-09-06 08:13:52,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:13:52,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:13:52,536 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:52,536 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:52,537 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,537 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,537 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,537 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,537 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:52,537 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:52,537 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,537 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,594 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:52,594 DEBUG: Start: Training
-2016-09-06 08:13:52,595 DEBUG: Info: Time for Training: 0.0595271587372[s]
-2016-09-06 08:13:52,595 DEBUG: Done: Training
-2016-09-06 08:13:52,595 DEBUG: Start: Predicting
-2016-09-06 08:13:52,602 DEBUG: Done: Predicting
-2016-09-06 08:13:52,602 DEBUG: Start: Getting Results
-2016-09-06 08:13:52,603 DEBUG: Done: Getting Results
-2016-09-06 08:13:52,603 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,603 INFO: Done: Result Analysis
-2016-09-06 08:13:52,812 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:52,812 DEBUG: Start: Training
-2016-09-06 08:13:52,842 DEBUG: Info: Time for Training: 0.306573152542[s]
-2016-09-06 08:13:52,842 DEBUG: Done: Training
-2016-09-06 08:13:52,842 DEBUG: Start: Predicting
-2016-09-06 08:13:52,847 DEBUG: Done: Predicting
-2016-09-06 08:13:52,847 DEBUG: Start: Getting Results
-2016-09-06 08:13:52,848 DEBUG: Done: Getting Results
-2016-09-06 08:13:52,848 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 11, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:13:52,848 INFO: Done: Result Analysis
-2016-09-06 08:13:52,988 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,988 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:52,988 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:13:52,988 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:13:52,989 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:52,989 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:52,989 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,989 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:52,989 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,989 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:52,990 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:52,990 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:52,990 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:52,990 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,065 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:53,065 DEBUG: Start: Training
-2016-09-06 08:13:53,066 DEBUG: Info: Time for Training: 0.0787451267242[s]
-2016-09-06 08:13:53,066 DEBUG: Done: Training
-2016-09-06 08:13:53,066 DEBUG: Start: Predicting
-2016-09-06 08:13:53,077 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:53,077 DEBUG: Start: Training
-2016-09-06 08:13:53,083 DEBUG: Done: Predicting
-2016-09-06 08:13:53,083 DEBUG: Start: Getting Results
-2016-09-06 08:13:53,085 DEBUG: Done: Getting Results
-2016-09-06 08:13:53,085 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,085 INFO: Done: Result Analysis
-2016-09-06 08:13:53,104 DEBUG: Info: Time for Training: 0.116367816925[s]
-2016-09-06 08:13:53,104 DEBUG: Done: Training
-2016-09-06 08:13:53,104 DEBUG: Start: Predicting
-2016-09-06 08:13:53,108 DEBUG: Done: Predicting
-2016-09-06 08:13:53,108 DEBUG: Start: Getting Results
-2016-09-06 08:13:53,109 DEBUG: Done: Getting Results
-2016-09-06 08:13:53,109 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,109 INFO: Done: Result Analysis
-2016-09-06 08:13:53,238 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,238 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,238 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:13:53,238 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:13:53,238 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:53,238 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:53,239 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:53,239 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:13:53,239 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:53,239 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:13:53,239 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:53,239 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:53,240 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,240 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,358 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:53,359 DEBUG: Start: Training
-2016-09-06 08:13:53,373 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:53,373 DEBUG: Start: Training
-2016-09-06 08:13:53,382 DEBUG: Info: Time for Training: 0.144605875015[s]
-2016-09-06 08:13:53,382 DEBUG: Done: Training
-2016-09-06 08:13:53,382 DEBUG: Start: Predicting
-2016-09-06 08:13:53,390 DEBUG: Done: Predicting
-2016-09-06 08:13:53,390 DEBUG: Start: Getting Results
-2016-09-06 08:13:53,391 DEBUG: Done: Getting Results
-2016-09-06 08:13:53,391 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,392 INFO: Done: Result Analysis
-2016-09-06 08:13:53,394 DEBUG: Info: Time for Training: 0.156768798828[s]
-2016-09-06 08:13:53,394 DEBUG: Done: Training
-2016-09-06 08:13:53,394 DEBUG: Start: Predicting
-2016-09-06 08:13:53,398 DEBUG: Done: Predicting
-2016-09-06 08:13:53,398 DEBUG: Start: Getting Results
-2016-09-06 08:13:53,399 DEBUG: Done: Getting Results
-2016-09-06 08:13:53,399 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,400 INFO: Done: Result Analysis
-2016-09-06 08:13:53,484 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,484 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,484 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:13:53,484 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:13:53,484 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:53,484 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:53,485 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:53,485 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:53,485 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:53,485 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:53,485 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:53,485 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,485 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:53,486 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,543 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:53,543 DEBUG: Start: Training
-2016-09-06 08:13:53,545 DEBUG: Info: Time for Training: 0.0612480640411[s]
-2016-09-06 08:13:53,545 DEBUG: Done: Training
-2016-09-06 08:13:53,545 DEBUG: Start: Predicting
-2016-09-06 08:13:53,547 DEBUG: Done: Predicting
-2016-09-06 08:13:53,548 DEBUG: Start: Getting Results
-2016-09-06 08:13:53,549 DEBUG: Done: Getting Results
-2016-09-06 08:13:53,549 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,549 INFO: Done: Result Analysis
-2016-09-06 08:13:53,570 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:53,570 DEBUG: Start: Training
-2016-09-06 08:13:53,574 DEBUG: Info: Time for Training: 0.0900461673737[s]
-2016-09-06 08:13:53,574 DEBUG: Done: Training
-2016-09-06 08:13:53,574 DEBUG: Start: Predicting
-2016-09-06 08:13:53,577 DEBUG: Done: Predicting
-2016-09-06 08:13:53,577 DEBUG: Start: Getting Results
-2016-09-06 08:13:53,578 DEBUG: Done: Getting Results
-2016-09-06 08:13:53,578 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,579 INFO: Done: Result Analysis
-2016-09-06 08:13:53,634 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,634 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:13:53,634 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:53,634 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:53,634 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:13:53,634 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:53,635 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:53,635 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:53,635 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:53,635 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:53,635 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:53,635 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:53,635 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,635 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:53,688 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:53,688 DEBUG: Start: Training
-2016-09-06 08:13:53,689 DEBUG: Info: Time for Training: 0.0553061962128[s]
-2016-09-06 08:13:53,689 DEBUG: Done: Training
-2016-09-06 08:13:53,689 DEBUG: Start: Predicting
-2016-09-06 08:13:53,696 DEBUG: Done: Predicting
-2016-09-06 08:13:53,696 DEBUG: Start: Getting Results
-2016-09-06 08:13:53,697 DEBUG: Done: Getting Results
-2016-09-06 08:13:53,697 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,697 INFO: Done: Result Analysis
-2016-09-06 08:13:53,904 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:53,904 DEBUG: Start: Training
-2016-09-06 08:13:53,932 DEBUG: Info: Time for Training: 0.298815011978[s]
-2016-09-06 08:13:53,932 DEBUG: Done: Training
-2016-09-06 08:13:53,933 DEBUG: Start: Predicting
-2016-09-06 08:13:53,937 DEBUG: Done: Predicting
-2016-09-06 08:13:53,937 DEBUG: Start: Getting Results
-2016-09-06 08:13:53,938 DEBUG: Done: Getting Results
-2016-09-06 08:13:53,938 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 11, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:13:53,939 INFO: Done: Result Analysis
-2016-09-06 08:13:54,083 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,084 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:13:54,084 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:54,084 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,084 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:13:54,085 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:54,085 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:54,085 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:54,085 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:54,085 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,086 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:54,086 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:54,086 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:54,087 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,169 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:54,170 DEBUG: Start: Training
-2016-09-06 08:13:54,170 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:54,170 DEBUG: Start: Training
-2016-09-06 08:13:54,170 DEBUG: Info: Time for Training: 0.0873100757599[s]
-2016-09-06 08:13:54,171 DEBUG: Done: Training
-2016-09-06 08:13:54,171 DEBUG: Start: Predicting
-2016-09-06 08:13:54,190 DEBUG: Info: Time for Training: 0.107427835464[s]
-2016-09-06 08:13:54,191 DEBUG: Done: Training
-2016-09-06 08:13:54,191 DEBUG: Start: Predicting
-2016-09-06 08:13:54,192 DEBUG: Done: Predicting
-2016-09-06 08:13:54,193 DEBUG: Start: Getting Results
-2016-09-06 08:13:54,195 DEBUG: Done: Getting Results
-2016-09-06 08:13:54,195 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.655555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.655555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,196 INFO: Done: Result Analysis
-2016-09-06 08:13:54,198 DEBUG: Done: Predicting
-2016-09-06 08:13:54,198 DEBUG: Start: Getting Results
-2016-09-06 08:13:54,199 DEBUG: Done: Getting Results
-2016-09-06 08:13:54,199 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.457142857143
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,199 INFO: Done: Result Analysis
-2016-09-06 08:13:54,332 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,332 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,333 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:13:54,333 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:13:54,333 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:54,333 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:54,334 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:54,334 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:13:54,334 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:54,334 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:13:54,334 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:54,334 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:54,334 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,334 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,418 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:54,418 DEBUG: Start: Training
-2016-09-06 08:13:54,423 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:54,423 DEBUG: Start: Training
-2016-09-06 08:13:54,436 DEBUG: Info: Time for Training: 0.103874921799[s]
-2016-09-06 08:13:54,436 DEBUG: Done: Training
-2016-09-06 08:13:54,436 DEBUG: Start: Predicting
-2016-09-06 08:13:54,441 DEBUG: Info: Time for Training: 0.109354972839[s]
-2016-09-06 08:13:54,441 DEBUG: Done: Training
-2016-09-06 08:13:54,442 DEBUG: Start: Predicting
-2016-09-06 08:13:54,442 DEBUG: Done: Predicting
-2016-09-06 08:13:54,442 DEBUG: Start: Getting Results
-2016-09-06 08:13:54,443 DEBUG: Done: Getting Results
-2016-09-06 08:13:54,443 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,443 INFO: Done: Result Analysis
-2016-09-06 08:13:54,445 DEBUG: Done: Predicting
-2016-09-06 08:13:54,445 DEBUG: Start: Getting Results
-2016-09-06 08:13:54,446 DEBUG: Done: Getting Results
-2016-09-06 08:13:54,446 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,446 INFO: Done: Result Analysis
-2016-09-06 08:13:54,584 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,584 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,585 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:13:54,585 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:13:54,585 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:54,585 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:54,586 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:54,586 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:54,586 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:54,586 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:54,586 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:54,586 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:54,586 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,586 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,688 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:54,689 DEBUG: Start: Training
-2016-09-06 08:13:54,694 DEBUG: Info: Time for Training: 0.110232114792[s]
-2016-09-06 08:13:54,694 DEBUG: Done: Training
-2016-09-06 08:13:54,694 DEBUG: Start: Predicting
-2016-09-06 08:13:54,698 DEBUG: Done: Predicting
-2016-09-06 08:13:54,698 DEBUG: Start: Getting Results
-2016-09-06 08:13:54,699 DEBUG: Done: Getting Results
-2016-09-06 08:13:54,699 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,700 INFO: Done: Result Analysis
-2016-09-06 08:13:54,720 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:54,720 DEBUG: Start: Training
-2016-09-06 08:13:54,725 DEBUG: Info: Time for Training: 0.141868114471[s]
-2016-09-06 08:13:54,726 DEBUG: Done: Training
-2016-09-06 08:13:54,726 DEBUG: Start: Predicting
-2016-09-06 08:13:54,729 DEBUG: Done: Predicting
-2016-09-06 08:13:54,729 DEBUG: Start: Getting Results
-2016-09-06 08:13:54,730 DEBUG: Done: Getting Results
-2016-09-06 08:13:54,730 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,731 INFO: Done: Result Analysis
-2016-09-06 08:13:54,836 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,836 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:54,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:13:54,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:13:54,837 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:54,837 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:54,838 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:54,838 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:54,838 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:54,838 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:54,838 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:54,838 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:54,838 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,838 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:54,923 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:54,923 DEBUG: Start: Training
-2016-09-06 08:13:54,924 DEBUG: Info: Time for Training: 0.088819026947[s]
-2016-09-06 08:13:54,924 DEBUG: Done: Training
-2016-09-06 08:13:54,924 DEBUG: Start: Predicting
-2016-09-06 08:13:54,936 DEBUG: Done: Predicting
-2016-09-06 08:13:54,936 DEBUG: Start: Getting Results
-2016-09-06 08:13:54,937 DEBUG: Done: Getting Results
-2016-09-06 08:13:54,937 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:13:54,938 INFO: Done: Result Analysis
-2016-09-06 08:13:55,151 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:55,152 DEBUG: Start: Training
-2016-09-06 08:13:55,181 DEBUG: Info: Time for Training: 0.346240997314[s]
-2016-09-06 08:13:55,181 DEBUG: Done: Training
-2016-09-06 08:13:55,181 DEBUG: Start: Predicting
-2016-09-06 08:13:55,186 DEBUG: Done: Predicting
-2016-09-06 08:13:55,186 DEBUG: Start: Getting Results
-2016-09-06 08:13:55,187 DEBUG: Done: Getting Results
-2016-09-06 08:13:55,187 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 11, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.980952380952
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:13:55,187 INFO: Done: Result Analysis
-2016-09-06 08:13:55,284 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:55,285 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:13:55,285 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:13:55,285 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:13:55,285 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:55,285 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:13:55,286 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:55,286 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 08:13:55,286 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:55,286 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 08:13:55,287 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:55,287 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:13:55,287 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:55,287 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:13:55,363 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:55,363 DEBUG: Start: Training
-2016-09-06 08:13:55,364 DEBUG: Info: Time for Training: 0.0802390575409[s]
-2016-09-06 08:13:55,364 DEBUG: Done: Training
-2016-09-06 08:13:55,364 DEBUG: Start: Predicting
-2016-09-06 08:13:55,376 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:13:55,376 DEBUG: Start: Training
-2016-09-06 08:13:55,377 DEBUG: Done: Predicting
-2016-09-06 08:13:55,377 DEBUG: Start: Getting Results
-2016-09-06 08:13:55,378 DEBUG: Done: Getting Results
-2016-09-06 08:13:55,378 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:13:55,379 INFO: Done: Result Analysis
-2016-09-06 08:13:55,398 DEBUG: Info: Time for Training: 0.113732099533[s]
-2016-09-06 08:13:55,398 DEBUG: Done: Training
-2016-09-06 08:13:55,398 DEBUG: Start: Predicting
-2016-09-06 08:13:55,402 DEBUG: Done: Predicting
-2016-09-06 08:13:55,402 DEBUG: Start: Getting Results
-2016-09-06 08:13:55,403 DEBUG: Done: Getting Results
-2016-09-06 08:13:55,403 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:13:55,403 INFO: Done: Result Analysis
-2016-09-06 08:13:55,682 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:13:55,682 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:13:55,683 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:13:55,683 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:13:55,683 INFO: Info: Shape of View0 :(300, 17)
-2016-09-06 08:13:55,683 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:13:55,683 INFO: Info: Shape of View0 :(300, 17)
-2016-09-06 08:13:55,684 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:13:55,684 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 08:13:55,684 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 08:13:55,684 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:13:55,684 INFO: Done: Read Database Files
-2016-09-06 08:13:55,685 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:13:55,685 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:13:55,685 INFO: Done: Read Database Files
-2016-09-06 08:13:55,685 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:13:55,689 INFO: Done: Determine validation split
-2016-09-06 08:13:55,689 INFO: Start: Determine 5 folds
-2016-09-06 08:13:55,691 INFO: Done: Determine validation split
-2016-09-06 08:13:55,691 INFO: Start: Determine 5 folds
-2016-09-06 08:13:55,697 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:13:55,698 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:13:55,698 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:13:55,698 INFO: Done: Determine folds
-2016-09-06 08:13:55,698 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:13:55,698 INFO: Start: Classification
-2016-09-06 08:13:55,698 INFO: Start: Fold number 1
-2016-09-06 08:13:55,699 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:13:55,699 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:13:55,699 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:13:55,699 INFO: Done: Determine folds
-2016-09-06 08:13:55,699 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:13:55,699 INFO: Start: Classification
-2016-09-06 08:13:55,699 INFO: Start: Fold number 1
-2016-09-06 08:13:55,727 INFO: Start: Classification
-2016-09-06 08:13:55,735 DEBUG: Start: Iteration 1
-2016-09-06 08:13:55,744 DEBUG: View 0 : 0.459016393443
-2016-09-06 08:13:55,751 DEBUG: View 1 : 0.459016393443
-2016-09-06 08:13:55,760 DEBUG: View 2 : 0.459016393443
-2016-09-06 08:13:55,767 DEBUG: View 3 : 0.459016393443
-2016-09-06 08:13:55,767 WARNING: WARNING: All bad for iteration 0
-2016-09-06 08:13:55,777 INFO: Done: Fold number 1
-2016-09-06 08:13:55,777 INFO: Start: Fold number 2
-2016-09-06 08:13:55,804 INFO: Start: Classification
-2016-09-06 08:13:55,805 DEBUG: Best view : View0
-2016-09-06 08:13:55,836 INFO: Done: Fold number 2
-2016-09-06 08:13:55,837 INFO: Start: Fold number 3
-2016-09-06 08:13:55,862 INFO: Start: Classification
-2016-09-06 08:13:55,894 INFO: Done: Fold number 3
-2016-09-06 08:13:55,895 INFO: Start: Fold number 4
-2016-09-06 08:13:55,898 DEBUG: Start: Iteration 2
-2016-09-06 08:13:55,906 DEBUG: View 0 : 0.770491803279
-2016-09-06 08:13:55,915 DEBUG: View 1 : 0.68306010929
-2016-09-06 08:13:55,921 INFO: Start: Classification
-2016-09-06 08:13:55,922 DEBUG: View 2 : 0.715846994536
-2016-09-06 08:13:55,933 DEBUG: View 3 : 0.655737704918
-2016-09-06 08:13:55,953 INFO: Done: Fold number 4
-2016-09-06 08:13:55,954 INFO: Start: Fold number 5
-2016-09-06 08:13:55,977 DEBUG: Best view : View0
-2016-09-06 08:13:55,980 INFO: Start: Classification
-2016-09-06 08:13:56,012 INFO: Done: Fold number 5
-2016-09-06 08:13:56,012 INFO: Done: Classification
-2016-09-06 08:13:56,012 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:13:56,012 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:13:56,017 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 59.2121406739
- -On Test : 52.6829268293
- -On Validation : 57.7528089888
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - K nearest Neighbors with n_neighbors: 43
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:13:56,017 INFO: Done: Result Analysis
-2016-09-06 08:13:56,148 DEBUG: Start: Iteration 3
-2016-09-06 08:13:56,159 DEBUG: View 0 : 0.770491803279
-2016-09-06 08:13:56,166 DEBUG: View 1 : 0.68306010929
-2016-09-06 08:13:56,173 DEBUG: View 2 : 0.715846994536
-2016-09-06 08:13:56,181 DEBUG: View 3 : 0.655737704918
-2016-09-06 08:13:56,222 DEBUG: Best view : View0
-2016-09-06 08:13:56,442 INFO: Start: Classification
-2016-09-06 08:13:56,796 INFO: Done: Fold number 1
-2016-09-06 08:13:56,796 INFO: Start: Fold number 2
-2016-09-06 08:13:56,825 DEBUG: Start: Iteration 1
-2016-09-06 08:13:56,832 DEBUG: View 0 : 0.541899441341
-2016-09-06 08:13:56,839 DEBUG: View 1 : 0.541899441341
-2016-09-06 08:13:56,845 DEBUG: View 2 : 0.541899441341
-2016-09-06 08:13:56,851 DEBUG: View 3 : 0.541899441341
-2016-09-06 08:13:56,883 DEBUG: Best view : View0
-2016-09-06 08:13:56,964 DEBUG: Start: Iteration 2
-2016-09-06 08:13:56,971 DEBUG: View 0 : 0.648044692737
-2016-09-06 08:13:56,978 DEBUG: View 1 : 0.664804469274
-2016-09-06 08:13:56,985 DEBUG: View 2 : 0.715083798883
-2016-09-06 08:13:56,993 DEBUG: View 3 : 0.675977653631
-2016-09-06 08:13:57,030 DEBUG: Best view : View2
-2016-09-06 08:13:57,178 DEBUG: Start: Iteration 3
-2016-09-06 08:13:57,185 DEBUG: View 0 : 0.648044692737
-2016-09-06 08:13:57,192 DEBUG: View 1 : 0.664804469274
-2016-09-06 08:13:57,199 DEBUG: View 2 : 0.715083798883
-2016-09-06 08:13:57,206 DEBUG: View 3 : 0.675977653631
-2016-09-06 08:13:57,247 DEBUG: Best view : View2
-2016-09-06 08:13:57,460 INFO: Start: Classification
-2016-09-06 08:13:57,809 INFO: Done: Fold number 2
-2016-09-06 08:13:57,809 INFO: Start: Fold number 3
-2016-09-06 08:13:57,839 DEBUG: Start: Iteration 1
-2016-09-06 08:13:57,847 DEBUG: View 0 : 0.483870967742
-2016-09-06 08:13:57,854 DEBUG: View 1 : 0.483870967742
-2016-09-06 08:13:57,861 DEBUG: View 2 : 0.537634408602
-2016-09-06 08:13:57,868 DEBUG: View 3 : 0.516129032258
-2016-09-06 08:13:57,901 DEBUG: Best view : View3
-2016-09-06 08:13:57,983 DEBUG: Start: Iteration 2
-2016-09-06 08:13:57,991 DEBUG: View 0 : 0.720430107527
-2016-09-06 08:13:57,998 DEBUG: View 1 : 0.629032258065
-2016-09-06 08:13:58,005 DEBUG: View 2 : 0.704301075269
-2016-09-06 08:13:58,013 DEBUG: View 3 : 0.731182795699
-2016-09-06 08:13:58,052 DEBUG: Best view : View3
-2016-09-06 08:13:58,206 DEBUG: Start: Iteration 3
-2016-09-06 08:13:58,213 DEBUG: View 0 : 0.720430107527
-2016-09-06 08:13:58,221 DEBUG: View 1 : 0.629032258065
-2016-09-06 08:13:58,228 DEBUG: View 2 : 0.693548387097
-2016-09-06 08:13:58,236 DEBUG: View 3 : 0.731182795699
-2016-09-06 08:13:58,278 DEBUG: Best view : View2
-2016-09-06 08:13:58,500 INFO: Start: Classification
-2016-09-06 08:13:58,857 INFO: Done: Fold number 3
-2016-09-06 08:13:58,857 INFO: Start: Fold number 4
-2016-09-06 08:13:58,886 DEBUG: Start: Iteration 1
-2016-09-06 08:13:58,893 DEBUG: View 0 : 0.530726256983
-2016-09-06 08:13:58,899 DEBUG: View 1 : 0.530726256983
-2016-09-06 08:13:58,906 DEBUG: View 2 : 0.530726256983
-2016-09-06 08:13:58,912 DEBUG: View 3 : 0.530726256983
-2016-09-06 08:13:58,944 DEBUG: Best view : View0
-2016-09-06 08:13:59,023 DEBUG: Start: Iteration 2
-2016-09-06 08:13:59,030 DEBUG: View 0 : 0.709497206704
-2016-09-06 08:13:59,037 DEBUG: View 1 : 0.614525139665
-2016-09-06 08:13:59,044 DEBUG: View 2 : 0.659217877095
-2016-09-06 08:13:59,051 DEBUG: View 3 : 0.648044692737
-2016-09-06 08:13:59,089 DEBUG: Best view : View0
-2016-09-06 08:13:59,235 DEBUG: Start: Iteration 3
-2016-09-06 08:13:59,243 DEBUG: View 0 : 0.709497206704
-2016-09-06 08:13:59,250 DEBUG: View 1 : 0.614525139665
-2016-09-06 08:13:59,257 DEBUG: View 2 : 0.659217877095
-2016-09-06 08:13:59,264 DEBUG: View 3 : 0.648044692737
-2016-09-06 08:13:59,305 DEBUG: Best view : View0
-2016-09-06 08:13:59,519 INFO: Start: Classification
-2016-09-06 08:13:59,869 INFO: Done: Fold number 4
-2016-09-06 08:13:59,869 INFO: Start: Fold number 5
-2016-09-06 08:13:59,898 DEBUG: Start: Iteration 1
-2016-09-06 08:13:59,905 DEBUG: View 0 : 0.456043956044
-2016-09-06 08:13:59,911 DEBUG: View 1 : 0.456043956044
-2016-09-06 08:13:59,918 DEBUG: View 2 : 0.456043956044
-2016-09-06 08:13:59,925 DEBUG: View 3 : 0.456043956044
-2016-09-06 08:13:59,925 WARNING: WARNING: All bad for iteration 0
-2016-09-06 08:13:59,957 DEBUG: Best view : View0
-2016-09-06 08:14:00,037 DEBUG: Start: Iteration 2
-2016-09-06 08:14:00,044 DEBUG: View 0 : 0.692307692308
-2016-09-06 08:14:00,052 DEBUG: View 1 : 0.637362637363
-2016-09-06 08:14:00,059 DEBUG: View 2 : 0.675824175824
-2016-09-06 08:14:00,067 DEBUG: View 3 : 0.659340659341
-2016-09-06 08:14:00,105 DEBUG: Best view : View0
-2016-09-06 08:14:00,254 DEBUG: Start: Iteration 3
-2016-09-06 08:14:00,261 DEBUG: View 0 : 0.692307692308
-2016-09-06 08:14:00,269 DEBUG: View 1 : 0.637362637363
-2016-09-06 08:14:00,276 DEBUG: View 2 : 0.675824175824
-2016-09-06 08:14:00,283 DEBUG: View 3 : 0.659340659341
-2016-09-06 08:14:00,324 DEBUG: Best view : View0
-2016-09-06 08:14:00,541 INFO: Start: Classification
-2016-09-06 08:14:00,894 INFO: Done: Fold number 5
-2016-09-06 08:14:00,894 INFO: Done: Classification
-2016-09-06 08:14:00,894 INFO: Info: Time for Classification: 5[s]
-2016-09-06 08:14:00,894 INFO: Start: Result Analysis for Mumbo
-2016-09-06 08:14:02,808 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 71.7284678753
- -On Test : 54.1463414634
- -On Validation : 64.0449438202Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 17), View1 of shape (300, 12), View2 of shape (300, 8), View3 of shape (300, 20)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:01 0:00:00
- Fold 3 0:00:02 0:00:00
- Fold 4 0:00:03 0:00:00
- Fold 5 0:00:04 0:00:00
- Total 0:00:13 0:00:01
- So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 4
- - On View0 :
- - Mean average Accuracy : 0.2
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.182513661202
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.189071038251
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.177049180328
- - Percentage of time chosen : 0.0
- - Fold 1, used 4
- - On View0 :
- - Mean average Accuracy : 0.183798882682
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.187150837989
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.197206703911
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.18938547486
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.19247311828
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.174193548387
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.193548387097
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.197849462366
- - Percentage of time chosen : 0.2
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.194972067039
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.175977653631
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.184916201117
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.182681564246
- - Percentage of time chosen : 0.0
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.184065934066
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.173076923077
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.180769230769
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.177472527473
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 54.0983606557
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 2
- Accuracy on train : 54.1899441341
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 3
- Accuracy on train : 54.3010752688
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View3
- Fold 4
- Accuracy on train : 53.0726256983
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 5
- Accuracy on train : 54.3956043956
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 77.0491803279
- Accuracy on test : 0.0
- Accuracy on validation : 61.797752809
- Selected View : View0
- Fold 2
- Accuracy on train : 71.5083798883
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View2
- Fold 3
- Accuracy on train : 73.1182795699
- Accuracy on test : 0.0
- Accuracy on validation : 62.9213483146
- Selected View : View3
- Fold 4
- Accuracy on train : 70.9497206704
- Accuracy on test : 0.0
- Accuracy on validation : 62.9213483146
- Selected View : View0
- Fold 5
- Accuracy on train : 69.2307692308
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- - Iteration 3
- Fold 1
- Accuracy on train : 77.0491803279
- Accuracy on test : 0.0
- Accuracy on validation : 61.797752809
- Selected View : View0
- Fold 2
- Accuracy on train : 71.5083798883
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View2
- Fold 3
- Accuracy on train : 69.3548387097
- Accuracy on test : 0.0
- Accuracy on validation : 60.6741573034
- Selected View : View2
- Fold 4
- Accuracy on train : 70.9497206704
- Accuracy on test : 0.0
- Accuracy on validation : 64.0449438202
- Selected View : View0
- Fold 5
- Accuracy on train : 69.7802197802
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- - Iteration 4
- Fold 1
- Accuracy on train : 54.0983606557
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 2
- Accuracy on train : 54.1899441341
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 3
- Accuracy on train : 54.3010752688
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 4
- Accuracy on train : 53.0726256983
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 5
- Accuracy on train : 54.3956043956
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
-2016-09-06 08:14:02,984 INFO: Done: Result Analysis
-2016-09-06 08:14:03,053 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:03,053 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:03,054 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:03,054 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:03,055 INFO: Info: Shape of View0 :(300, 17)
-2016-09-06 08:14:03,055 INFO: Info: Shape of View0 :(300, 17)
-2016-09-06 08:14:03,055 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:14:03,056 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:14:03,056 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 08:14:03,057 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 08:14:03,057 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:14:03,057 INFO: Done: Read Database Files
-2016-09-06 08:14:03,057 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:14:03,058 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:14:03,058 INFO: Done: Read Database Files
-2016-09-06 08:14:03,058 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:14:03,066 INFO: Done: Determine validation split
-2016-09-06 08:14:03,066 INFO: Done: Determine validation split
-2016-09-06 08:14:03,066 INFO: Start: Determine 5 folds
-2016-09-06 08:14:03,066 INFO: Start: Determine 5 folds
-2016-09-06 08:14:03,076 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:14:03,076 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:14:03,077 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:14:03,077 INFO: Done: Determine folds
-2016-09-06 08:14:03,077 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:14:03,077 INFO: Start: Classification
-2016-09-06 08:14:03,077 INFO: Start: Fold number 1
-2016-09-06 08:14:03,077 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:14:03,078 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:14:03,078 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:14:03,078 INFO: Done: Determine folds
-2016-09-06 08:14:03,078 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:14:03,078 INFO: Start: Classification
-2016-09-06 08:14:03,078 INFO: Start: Fold number 1
-2016-09-06 08:14:03,125 INFO: Start: Classification
-2016-09-06 08:14:03,155 INFO: Start: Classification
-2016-09-06 08:14:03,192 INFO: Done: Fold number 1
-2016-09-06 08:14:03,193 INFO: Start: Fold number 2
-2016-09-06 08:14:03,209 INFO: Done: Fold number 1
-2016-09-06 08:14:03,209 INFO: Start: Fold number 2
-2016-09-06 08:14:03,236 INFO: Start: Classification
-2016-09-06 08:14:03,242 INFO: Start: Classification
-2016-09-06 08:14:03,280 INFO: Done: Fold number 2
-2016-09-06 08:14:03,280 INFO: Start: Fold number 3
-2016-09-06 08:14:03,314 INFO: Done: Fold number 2
-2016-09-06 08:14:03,314 INFO: Start: Fold number 3
-2016-09-06 08:14:03,329 INFO: Start: Classification
-2016-09-06 08:14:03,340 INFO: Start: Classification
-2016-09-06 08:14:03,366 INFO: Done: Fold number 3
-2016-09-06 08:14:03,366 INFO: Start: Fold number 4
-2016-09-06 08:14:03,415 INFO: Start: Classification
-2016-09-06 08:14:03,419 INFO: Done: Fold number 3
-2016-09-06 08:14:03,419 INFO: Start: Fold number 4
-2016-09-06 08:14:03,445 INFO: Start: Classification
-2016-09-06 08:14:03,451 INFO: Done: Fold number 4
-2016-09-06 08:14:03,451 INFO: Start: Fold number 5
-2016-09-06 08:14:03,500 INFO: Start: Classification
-2016-09-06 08:14:03,523 INFO: Done: Fold number 4
-2016-09-06 08:14:03,523 INFO: Start: Fold number 5
-2016-09-06 08:14:03,537 INFO: Done: Fold number 5
-2016-09-06 08:14:03,538 INFO: Done: Classification
-2016-09-06 08:14:03,538 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:14:03,538 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:14:03,542 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 60.1962236658
- -On Test : 55.6097560976
- -On Validation : 59.3258426966
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - K nearest Neighbors with n_neighbors: 43
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:14:03,542 INFO: Done: Result Analysis
-2016-09-06 08:14:03,550 INFO: Start: Classification
-2016-09-06 08:14:03,626 INFO: Done: Fold number 5
-2016-09-06 08:14:03,626 INFO: Done: Classification
-2016-09-06 08:14:03,626 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:14:03,626 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:14:03,630 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 54.0717294888
- -On Test : 53.6585365854
- -On Validation : 53.9325842697
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - K nearest Neighbors with n_neighbors: 43
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:14:03,630 INFO: Done: Result Analysis
-2016-09-06 08:14:03,701 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:03,701 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:03,701 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:03,701 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:03,701 INFO: Info: Shape of View0 :(300, 17)
-2016-09-06 08:14:03,701 INFO: Info: Shape of View0 :(300, 17)
-2016-09-06 08:14:03,702 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:14:03,702 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:14:03,702 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 08:14:03,702 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 08:14:03,703 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:14:03,703 INFO: Done: Read Database Files
-2016-09-06 08:14:03,703 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:14:03,703 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:14:03,703 INFO: Done: Read Database Files
-2016-09-06 08:14:03,703 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:14:03,707 INFO: Done: Determine validation split
-2016-09-06 08:14:03,708 INFO: Start: Determine 5 folds
-2016-09-06 08:14:03,708 INFO: Done: Determine validation split
-2016-09-06 08:14:03,708 INFO: Start: Determine 5 folds
-2016-09-06 08:14:03,715 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:14:03,716 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:14:03,716 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:14:03,716 INFO: Done: Determine folds
-2016-09-06 08:14:03,716 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:14:03,716 INFO: Start: Classification
-2016-09-06 08:14:03,716 INFO: Start: Fold number 1
-2016-09-06 08:14:03,716 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:14:03,717 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:14:03,717 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:14:03,717 INFO: Done: Determine folds
-2016-09-06 08:14:03,717 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:14:03,717 INFO: Start: Classification
-2016-09-06 08:14:03,717 INFO: Start: Fold number 1
-2016-09-06 08:14:03,742 INFO: Start: Classification
-2016-09-06 08:14:03,747 INFO: Start: Classification
-2016-09-06 08:14:03,767 INFO: Done: Fold number 1
-2016-09-06 08:14:03,768 INFO: Start: Fold number 2
-2016-09-06 08:14:03,792 INFO: Start: Classification
-2016-09-06 08:14:03,800 INFO: Done: Fold number 1
-2016-09-06 08:14:03,800 INFO: Start: Fold number 2
-2016-09-06 08:14:03,817 INFO: Done: Fold number 2
-2016-09-06 08:14:03,817 INFO: Start: Fold number 3
-2016-09-06 08:14:03,828 INFO: Start: Classification
-2016-09-06 08:14:03,841 INFO: Start: Classification
-2016-09-06 08:14:03,866 INFO: Done: Fold number 3
-2016-09-06 08:14:03,866 INFO: Start: Fold number 4
-2016-09-06 08:14:03,870 INFO: Done: Fold number 2
-2016-09-06 08:14:03,870 INFO: Start: Fold number 3
-2016-09-06 08:14:03,890 INFO: Start: Classification
-2016-09-06 08:14:03,901 INFO: Start: Classification
-2016-09-06 08:14:03,915 INFO: Done: Fold number 4
-2016-09-06 08:14:03,915 INFO: Start: Fold number 5
-2016-09-06 08:14:03,938 INFO: Start: Classification
-2016-09-06 08:14:03,942 INFO: Done: Fold number 3
-2016-09-06 08:14:03,942 INFO: Start: Fold number 4
-2016-09-06 08:14:03,964 INFO: Done: Fold number 5
-2016-09-06 08:14:03,964 INFO: Done: Classification
-2016-09-06 08:14:03,964 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:14:03,965 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:14:03,969 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 47.3170731707
- -On Validation : 83.8202247191
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:14:03,970 INFO: Done: Result Analysis
-2016-09-06 08:14:03,970 INFO: Start: Classification
-2016-09-06 08:14:04,005 INFO: Done: Fold number 4
-2016-09-06 08:14:04,006 INFO: Start: Fold number 5
-2016-09-06 08:14:04,030 INFO: Start: Classification
-2016-09-06 08:14:04,066 INFO: Done: Fold number 5
-2016-09-06 08:14:04,066 INFO: Done: Classification
-2016-09-06 08:14:04,066 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:14:04,066 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:14:04,071 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 18.5734395421
- -On Test : 16.5853658537
- -On Validation : 18.4269662921
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - K nearest Neighbors with n_neighbors: 43
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:14:04,071 INFO: Done: Result Analysis
-2016-09-06 08:14:04,151 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:04,151 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:04,151 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:14:04,152 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:14:04,152 INFO: Info: Shape of View0 :(300, 17)
-2016-09-06 08:14:04,152 INFO: Info: Shape of View0 :(300, 17)
-2016-09-06 08:14:04,153 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:14:04,153 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:14:04,153 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 08:14:04,153 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 08:14:04,154 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:14:04,154 INFO: Done: Read Database Files
-2016-09-06 08:14:04,154 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:14:04,154 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 08:14:04,154 INFO: Done: Read Database Files
-2016-09-06 08:14:04,154 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:14:04,158 INFO: Done: Determine validation split
-2016-09-06 08:14:04,158 INFO: Start: Determine 5 folds
-2016-09-06 08:14:04,159 INFO: Done: Determine validation split
-2016-09-06 08:14:04,159 INFO: Start: Determine 5 folds
-2016-09-06 08:14:04,164 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:14:04,164 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:14:04,164 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:14:04,164 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:14:04,164 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:14:04,164 INFO: Done: Determine folds
-2016-09-06 08:14:04,164 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:14:04,165 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:14:04,165 INFO: Done: Determine folds
-2016-09-06 08:14:04,165 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:14:04,165 INFO: Start: Classification
-2016-09-06 08:14:04,165 INFO: Start: Classification
-2016-09-06 08:14:04,165 INFO: Start: Fold number 1
-2016-09-06 08:14:04,165 INFO: Start: Fold number 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cfed7f4ecb2a35dbc33ddcfb6dff2c89d7317411..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6c7c657d6b2d83b6e9bfe78fe7de0da7152081e1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8b525565503e1880b6df2daa291875c0143c0abe..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 32
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index df07d8064dbea636b9ec678be46bf2ba1a29f5c8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 11, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 591967513087fd99fc8f323932b16f83a3ba5253..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ee8523e8ac1983f299d6d0e968060fc171ffda8d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2929de7bf7bc4348b23512837a0f682f71345a32..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 67ffaac575c2263b68f25fb4431f5366cd9acb24..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2e84281950a75085cdce5704072ba35a4196b5d7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b1177ee714046320772d614da1f0ff18f38ab7c9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 11, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ab54ec5a76da93a23db4d5f047942aeae6f54ae2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5781
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 187a9c3ef43bf3dc2e1a3b38c27b17eddf092da6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081352Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 76bbebd81114a745cf644771e97fe7150ffc995f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ae9879950c35ee11e5ce2957e2a4d71b4f214ff3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 15b2572e7b6d969b4b4c2ecca91ba74e18c3e64d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fc4349949cd6a81b7e264c5dadf9afd9e7278fa7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 11, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b0df0939d583de5c56b5c89faa5955896a040033..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 63a4473eb98b0d7eda7b88299df365dfb0c5fc3b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f4d2a567c482d00241a141b32ae63df95453b034..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9ac332364e552597d9d93a766d600b9b81e0cab8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081353Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 243793e9b560c5fe0bd966310c8ab3eba2f663b1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3861a3746b22c260041b14d03efef34df5d882f9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index aaa776bf90e8547580e044e6ce0082afe833274b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e03e18e0e663151321609c99f1a5081331471719..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.655555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.655555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5c76abd8b24b1ea1414d44faa0439604f44ee5c2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.457142857143
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b21c5aaefaf4471429fd551cc71a8eafd64db80b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8b5628539a4f0f734b04d5ddcb08cb45e624921e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081354Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 553d09276e22a73d1dc37e331aafe91fe6587188..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 11, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.980952380952
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7686d0da2309afcf16ecb157937ef8558a8faaca..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9a50b87fc31373c1fa9bae0dc880004d539bd6d9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081355Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5883
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081356Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081356Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 33353e5d0a13bfbbe2f59291fe5962a25e8a6291..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081356Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 59.2121406739
- -On Test : 52.6829268293
- -On Validation : 57.7528089888
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - K nearest Neighbors with n_neighbors: 43
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index 69b3a9e0a65138aafedff9e20307d6cf4762e4a1..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 4cc4e759c8d96b9a25af53f7aec3427fc28d41bd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081402Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,209 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 71.7284678753
- -On Test : 54.1463414634
- -On Validation : 64.0449438202Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 17), View1 of shape (300, 12), View2 of shape (300, 8), View3 of shape (300, 20)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:01 0:00:00
- Fold 3 0:00:02 0:00:00
- Fold 4 0:00:03 0:00:00
- Fold 5 0:00:04 0:00:00
- Total 0:00:13 0:00:01
- So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 4
- - On View0 :
- - Mean average Accuracy : 0.2
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.182513661202
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.189071038251
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.177049180328
- - Percentage of time chosen : 0.0
- - Fold 1, used 4
- - On View0 :
- - Mean average Accuracy : 0.183798882682
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.187150837989
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.197206703911
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.18938547486
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.19247311828
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.174193548387
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.193548387097
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.197849462366
- - Percentage of time chosen : 0.2
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.194972067039
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.175977653631
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.184916201117
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.182681564246
- - Percentage of time chosen : 0.0
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.184065934066
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.173076923077
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.180769230769
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.177472527473
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 54.0983606557
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 2
- Accuracy on train : 54.1899441341
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 3
- Accuracy on train : 54.3010752688
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View3
- Fold 4
- Accuracy on train : 53.0726256983
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 5
- Accuracy on train : 54.3956043956
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 77.0491803279
- Accuracy on test : 0.0
- Accuracy on validation : 61.797752809
- Selected View : View0
- Fold 2
- Accuracy on train : 71.5083798883
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View2
- Fold 3
- Accuracy on train : 73.1182795699
- Accuracy on test : 0.0
- Accuracy on validation : 62.9213483146
- Selected View : View3
- Fold 4
- Accuracy on train : 70.9497206704
- Accuracy on test : 0.0
- Accuracy on validation : 62.9213483146
- Selected View : View0
- Fold 5
- Accuracy on train : 69.2307692308
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- - Iteration 3
- Fold 1
- Accuracy on train : 77.0491803279
- Accuracy on test : 0.0
- Accuracy on validation : 61.797752809
- Selected View : View0
- Fold 2
- Accuracy on train : 71.5083798883
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View2
- Fold 3
- Accuracy on train : 69.3548387097
- Accuracy on test : 0.0
- Accuracy on validation : 60.6741573034
- Selected View : View2
- Fold 4
- Accuracy on train : 70.9497206704
- Accuracy on test : 0.0
- Accuracy on validation : 64.0449438202
- Selected View : View0
- Fold 5
- Accuracy on train : 69.7802197802
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- - Iteration 4
- Fold 1
- Accuracy on train : 54.0983606557
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 2
- Accuracy on train : 54.1899441341
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 3
- Accuracy on train : 54.3010752688
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 4
- Accuracy on train : 53.0726256983
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 5
- Accuracy on train : 54.3956043956
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index b4b3727baa8dddfdd3b93f1a6b33efaf3eb5c4ff..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 47.3170731707
- -On Validation : 83.8202247191
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3b8c97f45640d1e4e835a2a26034ca64895fb984..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 54.0717294888
- -On Test : 53.6585365854
- -On Validation : 53.9325842697
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - K nearest Neighbors with n_neighbors: 43
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 42ae0b9c286b64e7b7b1bc4f99c5df40706f2028..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081403Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 60.1962236658
- -On Test : 55.6097560976
- -On Validation : 59.3258426966
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - K nearest Neighbors with n_neighbors: 43
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081404Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081404Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 71d4c5b3e180645996f382a314aa26de7083cb31..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081404Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 18.5734395421
- -On Test : 16.5853658537
- -On Validation : 18.4269662921
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - K nearest Neighbors with n_neighbors: 43
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081613-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-081613-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 0eb327521f60bbf439b68da8b8593c4b80c1437b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081613-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1977 +0,0 @@
-2016-09-06 08:16:13,564 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:16:13,564 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00013571875 Gbytes /!\
-2016-09-06 08:16:18,576 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:16:18,578 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:16:18,632 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:18,632 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:18,632 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:16:18,632 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:16:18,632 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:18,632 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:18,633 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:18,633 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:18,633 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:18,633 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:18,633 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:18,633 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:18,634 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:18,634 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:18,695 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:18,695 DEBUG: Start: Training
-2016-09-06 08:16:18,697 DEBUG: Info: Time for Training: 0.0656869411469[s]
-2016-09-06 08:16:18,697 DEBUG: Done: Training
-2016-09-06 08:16:18,697 DEBUG: Start: Predicting
-2016-09-06 08:16:18,700 DEBUG: Done: Predicting
-2016-09-06 08:16:18,700 DEBUG: Start: Getting Results
-2016-09-06 08:16:18,701 DEBUG: Done: Getting Results
-2016-09-06 08:16:18,701 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:16:18,701 INFO: Done: Result Analysis
-2016-09-06 08:16:18,732 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:18,732 DEBUG: Start: Training
-2016-09-06 08:16:18,736 DEBUG: Info: Time for Training: 0.104928970337[s]
-2016-09-06 08:16:18,736 DEBUG: Done: Training
-2016-09-06 08:16:18,736 DEBUG: Start: Predicting
-2016-09-06 08:16:18,739 DEBUG: Done: Predicting
-2016-09-06 08:16:18,739 DEBUG: Start: Getting Results
-2016-09-06 08:16:18,741 DEBUG: Done: Getting Results
-2016-09-06 08:16:18,741 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:16:18,741 INFO: Done: Result Analysis
-2016-09-06 08:16:18,884 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:18,884 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:18,884 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:16:18,884 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:16:18,884 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:18,884 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:18,885 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:18,885 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:18,885 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:18,885 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:18,886 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:18,886 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:18,886 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:18,886 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:18,971 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:18,971 DEBUG: Start: Training
-2016-09-06 08:16:18,972 DEBUG: Info: Time for Training: 0.0888061523438[s]
-2016-09-06 08:16:18,972 DEBUG: Done: Training
-2016-09-06 08:16:18,972 DEBUG: Start: Predicting
-2016-09-06 08:16:18,981 DEBUG: Done: Predicting
-2016-09-06 08:16:18,981 DEBUG: Start: Getting Results
-2016-09-06 08:16:18,983 DEBUG: Done: Getting Results
-2016-09-06 08:16:18,983 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.628571428571
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:16:18,983 INFO: Done: Result Analysis
-2016-09-06 08:16:19,664 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:19,664 DEBUG: Start: Training
-2016-09-06 08:16:19,737 DEBUG: Info: Time for Training: 0.853468894958[s]
-2016-09-06 08:16:19,737 DEBUG: Done: Training
-2016-09-06 08:16:19,737 DEBUG: Start: Predicting
-2016-09-06 08:16:19,745 DEBUG: Done: Predicting
-2016-09-06 08:16:19,745 DEBUG: Start: Getting Results
-2016-09-06 08:16:19,746 DEBUG: Done: Getting Results
-2016-09-06 08:16:19,746 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 28, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:16:19,746 INFO: Done: Result Analysis
-2016-09-06 08:16:19,839 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:19,839 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:19,840 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:16:19,840 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:16:19,840 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:19,840 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:19,841 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:19,841 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:19,841 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:19,841 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:19,841 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:19,841 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:19,841 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:19,841 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:19,925 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:19,925 DEBUG: Start: Training
-2016-09-06 08:16:19,926 DEBUG: Info: Time for Training: 0.0877830982208[s]
-2016-09-06 08:16:19,926 DEBUG: Done: Training
-2016-09-06 08:16:19,926 DEBUG: Start: Predicting
-2016-09-06 08:16:19,929 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:19,929 DEBUG: Start: Training
-2016-09-06 08:16:19,941 DEBUG: Done: Predicting
-2016-09-06 08:16:19,942 DEBUG: Start: Getting Results
-2016-09-06 08:16:19,943 DEBUG: Done: Getting Results
-2016-09-06 08:16:19,943 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:19,943 INFO: Done: Result Analysis
-2016-09-06 08:16:19,952 DEBUG: Info: Time for Training: 0.113765001297[s]
-2016-09-06 08:16:19,952 DEBUG: Done: Training
-2016-09-06 08:16:19,952 DEBUG: Start: Predicting
-2016-09-06 08:16:19,956 DEBUG: Done: Predicting
-2016-09-06 08:16:19,956 DEBUG: Start: Getting Results
-2016-09-06 08:16:19,957 DEBUG: Done: Getting Results
-2016-09-06 08:16:19,957 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.666666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.666666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:16:19,957 INFO: Done: Result Analysis
-2016-09-06 08:16:20,087 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,088 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:16:20,088 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:20,089 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,089 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:20,089 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:20,089 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:16:20,089 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:20,090 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:20,090 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,091 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 08:16:20,091 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 08:16:20,092 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:20,092 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,180 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:20,180 DEBUG: Start: Training
-2016-09-06 08:16:20,183 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:20,183 DEBUG: Start: Training
-2016-09-06 08:16:20,200 DEBUG: Info: Time for Training: 0.113780021667[s]
-2016-09-06 08:16:20,200 DEBUG: Done: Training
-2016-09-06 08:16:20,200 DEBUG: Start: Predicting
-2016-09-06 08:16:20,202 DEBUG: Info: Time for Training: 0.114318132401[s]
-2016-09-06 08:16:20,202 DEBUG: Done: Training
-2016-09-06 08:16:20,202 DEBUG: Start: Predicting
-2016-09-06 08:16:20,204 DEBUG: Done: Predicting
-2016-09-06 08:16:20,205 DEBUG: Start: Getting Results
-2016-09-06 08:16:20,206 DEBUG: Done: Getting Results
-2016-09-06 08:16:20,206 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:16:20,206 INFO: Done: Result Analysis
-2016-09-06 08:16:20,208 DEBUG: Done: Predicting
-2016-09-06 08:16:20,209 DEBUG: Start: Getting Results
-2016-09-06 08:16:20,210 DEBUG: Done: Getting Results
-2016-09-06 08:16:20,210 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:20,211 INFO: Done: Result Analysis
-2016-09-06 08:16:20,333 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,333 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:16:20,334 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:20,334 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:20,334 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:20,334 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,335 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:20,335 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:16:20,335 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:20,335 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,335 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:20,335 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:20,335 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:20,336 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,393 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:20,393 DEBUG: Start: Training
-2016-09-06 08:16:20,394 DEBUG: Info: Time for Training: 0.0608160495758[s]
-2016-09-06 08:16:20,395 DEBUG: Done: Training
-2016-09-06 08:16:20,395 DEBUG: Start: Predicting
-2016-09-06 08:16:20,397 DEBUG: Done: Predicting
-2016-09-06 08:16:20,397 DEBUG: Start: Getting Results
-2016-09-06 08:16:20,398 DEBUG: Done: Getting Results
-2016-09-06 08:16:20,398 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:16:20,399 INFO: Done: Result Analysis
-2016-09-06 08:16:20,418 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:20,419 DEBUG: Start: Training
-2016-09-06 08:16:20,423 DEBUG: Info: Time for Training: 0.0899488925934[s]
-2016-09-06 08:16:20,423 DEBUG: Done: Training
-2016-09-06 08:16:20,423 DEBUG: Start: Predicting
-2016-09-06 08:16:20,426 DEBUG: Done: Predicting
-2016-09-06 08:16:20,426 DEBUG: Start: Getting Results
-2016-09-06 08:16:20,427 DEBUG: Done: Getting Results
-2016-09-06 08:16:20,427 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:16:20,428 INFO: Done: Result Analysis
-2016-09-06 08:16:20,583 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,583 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:20,584 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:16:20,584 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:16:20,584 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:20,584 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:20,585 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:20,585 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:20,585 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:20,585 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:20,585 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:20,585 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:20,585 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,585 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:20,670 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:20,670 DEBUG: Start: Training
-2016-09-06 08:16:20,671 DEBUG: Info: Time for Training: 0.0880181789398[s]
-2016-09-06 08:16:20,671 DEBUG: Done: Training
-2016-09-06 08:16:20,671 DEBUG: Start: Predicting
-2016-09-06 08:16:20,681 DEBUG: Done: Predicting
-2016-09-06 08:16:20,681 DEBUG: Start: Getting Results
-2016-09-06 08:16:20,682 DEBUG: Done: Getting Results
-2016-09-06 08:16:20,683 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:16:20,683 INFO: Done: Result Analysis
-2016-09-06 08:16:21,366 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:21,366 DEBUG: Start: Training
-2016-09-06 08:16:21,439 DEBUG: Info: Time for Training: 0.856797218323[s]
-2016-09-06 08:16:21,440 DEBUG: Done: Training
-2016-09-06 08:16:21,440 DEBUG: Start: Predicting
-2016-09-06 08:16:21,448 DEBUG: Done: Predicting
-2016-09-06 08:16:21,448 DEBUG: Start: Getting Results
-2016-09-06 08:16:21,449 DEBUG: Done: Getting Results
-2016-09-06 08:16:21,449 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 28, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:16:21,449 INFO: Done: Result Analysis
-2016-09-06 08:16:21,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:21,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:21,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:16:21,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:16:21,536 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:21,536 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:21,537 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:21,537 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:21,537 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:21,537 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:21,537 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:21,537 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:21,537 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:21,537 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:21,614 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:21,614 DEBUG: Start: Training
-2016-09-06 08:16:21,615 DEBUG: Info: Time for Training: 0.0793581008911[s]
-2016-09-06 08:16:21,615 DEBUG: Done: Training
-2016-09-06 08:16:21,615 DEBUG: Start: Predicting
-2016-09-06 08:16:21,624 DEBUG: Done: Predicting
-2016-09-06 08:16:21,625 DEBUG: Start: Getting Results
-2016-09-06 08:16:21,626 DEBUG: Done: Getting Results
-2016-09-06 08:16:21,626 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:21,626 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:21,626 DEBUG: Start: Training
-2016-09-06 08:16:21,626 INFO: Done: Result Analysis
-2016-09-06 08:16:21,644 DEBUG: Info: Time for Training: 0.108808994293[s]
-2016-09-06 08:16:21,644 DEBUG: Done: Training
-2016-09-06 08:16:21,644 DEBUG: Start: Predicting
-2016-09-06 08:16:21,648 DEBUG: Done: Predicting
-2016-09-06 08:16:21,648 DEBUG: Start: Getting Results
-2016-09-06 08:16:21,649 DEBUG: Done: Getting Results
-2016-09-06 08:16:21,649 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:16:21,649 INFO: Done: Result Analysis
-2016-09-06 08:16:21,786 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:21,786 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:21,787 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:16:21,787 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:16:21,787 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:21,787 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:21,788 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:21,788 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 08:16:21,788 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:21,788 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 08:16:21,788 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:21,788 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:21,788 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:21,788 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:21,907 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:21,907 DEBUG: Start: Training
-2016-09-06 08:16:21,919 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:21,919 DEBUG: Start: Training
-2016-09-06 08:16:21,931 DEBUG: Info: Time for Training: 0.145541191101[s]
-2016-09-06 08:16:21,931 DEBUG: Done: Training
-2016-09-06 08:16:21,932 DEBUG: Start: Predicting
-2016-09-06 08:16:21,939 DEBUG: Done: Predicting
-2016-09-06 08:16:21,939 DEBUG: Start: Getting Results
-2016-09-06 08:16:21,940 DEBUG: Done: Getting Results
-2016-09-06 08:16:21,940 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:16:21,941 INFO: Done: Result Analysis
-2016-09-06 08:16:21,946 DEBUG: Info: Time for Training: 0.159810066223[s]
-2016-09-06 08:16:21,946 DEBUG: Done: Training
-2016-09-06 08:16:21,946 DEBUG: Start: Predicting
-2016-09-06 08:16:21,949 DEBUG: Done: Predicting
-2016-09-06 08:16:21,950 DEBUG: Start: Getting Results
-2016-09-06 08:16:21,951 DEBUG: Done: Getting Results
-2016-09-06 08:16:21,951 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3904
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:16:21,951 INFO: Done: Result Analysis
-2016-09-06 08:16:22,039 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:22,039 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:22,039 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:16:22,039 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:16:22,039 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:22,039 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:22,040 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:22,040 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:22,040 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:22,040 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:22,040 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:22,040 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:22,041 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:22,041 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:22,131 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:22,131 DEBUG: Start: Training
-2016-09-06 08:16:22,134 DEBUG: Info: Time for Training: 0.0960428714752[s]
-2016-09-06 08:16:22,134 DEBUG: Done: Training
-2016-09-06 08:16:22,134 DEBUG: Start: Predicting
-2016-09-06 08:16:22,137 DEBUG: Done: Predicting
-2016-09-06 08:16:22,138 DEBUG: Start: Getting Results
-2016-09-06 08:16:22,139 DEBUG: Done: Getting Results
-2016-09-06 08:16:22,139 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:22,139 INFO: Done: Result Analysis
-2016-09-06 08:16:22,160 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:22,160 DEBUG: Start: Training
-2016-09-06 08:16:22,164 DEBUG: Info: Time for Training: 0.126334905624[s]
-2016-09-06 08:16:22,164 DEBUG: Done: Training
-2016-09-06 08:16:22,164 DEBUG: Start: Predicting
-2016-09-06 08:16:22,167 DEBUG: Done: Predicting
-2016-09-06 08:16:22,167 DEBUG: Start: Getting Results
-2016-09-06 08:16:22,169 DEBUG: Done: Getting Results
-2016-09-06 08:16:22,169 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:22,169 INFO: Done: Result Analysis
-2016-09-06 08:16:22,283 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:22,283 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:22,283 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:16:22,283 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:16:22,283 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:22,283 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:22,284 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:22,284 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:22,284 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:22,284 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:22,284 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:22,284 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:22,284 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:22,284 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:22,339 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:22,339 DEBUG: Start: Training
-2016-09-06 08:16:22,340 DEBUG: Info: Time for Training: 0.0575180053711[s]
-2016-09-06 08:16:22,340 DEBUG: Done: Training
-2016-09-06 08:16:22,340 DEBUG: Start: Predicting
-2016-09-06 08:16:22,346 DEBUG: Done: Predicting
-2016-09-06 08:16:22,346 DEBUG: Start: Getting Results
-2016-09-06 08:16:22,347 DEBUG: Done: Getting Results
-2016-09-06 08:16:22,347 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:16:22,348 INFO: Done: Result Analysis
-2016-09-06 08:16:23,030 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:23,031 DEBUG: Start: Training
-2016-09-06 08:16:23,104 DEBUG: Info: Time for Training: 0.82169508934[s]
-2016-09-06 08:16:23,104 DEBUG: Done: Training
-2016-09-06 08:16:23,104 DEBUG: Start: Predicting
-2016-09-06 08:16:23,112 DEBUG: Done: Predicting
-2016-09-06 08:16:23,112 DEBUG: Start: Getting Results
-2016-09-06 08:16:23,113 DEBUG: Done: Getting Results
-2016-09-06 08:16:23,113 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 28, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,113 INFO: Done: Result Analysis
-2016-09-06 08:16:23,231 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,231 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,232 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:16:23,232 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:16:23,232 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:23,232 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:23,232 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:23,232 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:23,233 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:23,233 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:23,233 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:23,233 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:23,233 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,233 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,309 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:23,309 DEBUG: Start: Training
-2016-09-06 08:16:23,310 DEBUG: Info: Time for Training: 0.0788888931274[s]
-2016-09-06 08:16:23,310 DEBUG: Done: Training
-2016-09-06 08:16:23,310 DEBUG: Start: Predicting
-2016-09-06 08:16:23,317 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:23,318 DEBUG: Start: Training
-2016-09-06 08:16:23,336 DEBUG: Info: Time for Training: 0.10564994812[s]
-2016-09-06 08:16:23,337 DEBUG: Done: Training
-2016-09-06 08:16:23,337 DEBUG: Start: Predicting
-2016-09-06 08:16:23,337 DEBUG: Done: Predicting
-2016-09-06 08:16:23,337 DEBUG: Start: Getting Results
-2016-09-06 08:16:23,339 DEBUG: Done: Getting Results
-2016-09-06 08:16:23,339 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,339 INFO: Done: Result Analysis
-2016-09-06 08:16:23,340 DEBUG: Done: Predicting
-2016-09-06 08:16:23,340 DEBUG: Start: Getting Results
-2016-09-06 08:16:23,341 DEBUG: Done: Getting Results
-2016-09-06 08:16:23,341 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,342 INFO: Done: Result Analysis
-2016-09-06 08:16:23,491 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,491 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,491 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:16:23,491 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:16:23,491 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:23,491 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:23,492 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:23,492 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:16:23,492 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:23,492 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:16:23,493 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:23,493 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:23,493 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,493 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,613 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:23,613 DEBUG: Start: Training
-2016-09-06 08:16:23,618 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:23,618 DEBUG: Start: Training
-2016-09-06 08:16:23,633 DEBUG: Info: Time for Training: 0.142898797989[s]
-2016-09-06 08:16:23,633 DEBUG: Done: Training
-2016-09-06 08:16:23,633 DEBUG: Start: Predicting
-2016-09-06 08:16:23,636 DEBUG: Info: Time for Training: 0.146584033966[s]
-2016-09-06 08:16:23,637 DEBUG: Done: Training
-2016-09-06 08:16:23,637 DEBUG: Start: Predicting
-2016-09-06 08:16:23,639 DEBUG: Done: Predicting
-2016-09-06 08:16:23,639 DEBUG: Start: Getting Results
-2016-09-06 08:16:23,640 DEBUG: Done: Getting Results
-2016-09-06 08:16:23,640 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,640 DEBUG: Done: Predicting
-2016-09-06 08:16:23,641 DEBUG: Start: Getting Results
-2016-09-06 08:16:23,641 INFO: Done: Result Analysis
-2016-09-06 08:16:23,642 DEBUG: Done: Getting Results
-2016-09-06 08:16:23,642 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,642 INFO: Done: Result Analysis
-2016-09-06 08:16:23,739 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,739 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,739 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:16:23,739 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:16:23,740 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:23,740 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:23,741 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:23,741 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:23,741 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:23,741 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:23,741 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:23,741 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:23,741 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,741 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,805 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:23,806 DEBUG: Start: Training
-2016-09-06 08:16:23,808 DEBUG: Info: Time for Training: 0.0693278312683[s]
-2016-09-06 08:16:23,808 DEBUG: Done: Training
-2016-09-06 08:16:23,808 DEBUG: Start: Predicting
-2016-09-06 08:16:23,810 DEBUG: Done: Predicting
-2016-09-06 08:16:23,810 DEBUG: Start: Getting Results
-2016-09-06 08:16:23,811 DEBUG: Done: Getting Results
-2016-09-06 08:16:23,811 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,812 INFO: Done: Result Analysis
-2016-09-06 08:16:23,831 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:23,831 DEBUG: Start: Training
-2016-09-06 08:16:23,835 DEBUG: Info: Time for Training: 0.0968770980835[s]
-2016-09-06 08:16:23,835 DEBUG: Done: Training
-2016-09-06 08:16:23,835 DEBUG: Start: Predicting
-2016-09-06 08:16:23,838 DEBUG: Done: Predicting
-2016-09-06 08:16:23,838 DEBUG: Start: Getting Results
-2016-09-06 08:16:23,840 DEBUG: Done: Getting Results
-2016-09-06 08:16:23,840 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:16:23,840 INFO: Done: Result Analysis
-2016-09-06 08:16:23,986 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,986 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:23,986 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:16:23,986 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:16:23,987 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:23,987 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:23,987 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:23,988 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:23,988 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:23,988 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:23,988 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:23,988 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:23,988 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:23,988 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:24,042 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:24,043 DEBUG: Start: Training
-2016-09-06 08:16:24,043 DEBUG: Info: Time for Training: 0.0575971603394[s]
-2016-09-06 08:16:24,043 DEBUG: Done: Training
-2016-09-06 08:16:24,043 DEBUG: Start: Predicting
-2016-09-06 08:16:24,051 DEBUG: Done: Predicting
-2016-09-06 08:16:24,051 DEBUG: Start: Getting Results
-2016-09-06 08:16:24,052 DEBUG: Done: Getting Results
-2016-09-06 08:16:24,052 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:16:24,052 INFO: Done: Result Analysis
-2016-09-06 08:16:24,711 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:24,711 DEBUG: Start: Training
-2016-09-06 08:16:24,761 DEBUG: Info: Time for Training: 0.775108098984[s]
-2016-09-06 08:16:24,761 DEBUG: Done: Training
-2016-09-06 08:16:24,761 DEBUG: Start: Predicting
-2016-09-06 08:16:24,767 DEBUG: Done: Predicting
-2016-09-06 08:16:24,767 DEBUG: Start: Getting Results
-2016-09-06 08:16:24,769 DEBUG: Done: Getting Results
-2016-09-06 08:16:24,769 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 19, max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:16:24,769 INFO: Done: Result Analysis
-2016-09-06 08:16:24,838 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:24,838 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:16:24,838 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:16:24,838 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:16:24,839 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:24,839 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:16:24,840 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:24,840 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 08:16:24,840 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:24,840 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 08:16:24,840 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:24,840 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:16:24,840 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:24,840 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:16:24,916 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:24,916 DEBUG: Start: Training
-2016-09-06 08:16:24,917 DEBUG: Info: Time for Training: 0.0791480541229[s]
-2016-09-06 08:16:24,917 DEBUG: Done: Training
-2016-09-06 08:16:24,917 DEBUG: Start: Predicting
-2016-09-06 08:16:24,924 DEBUG: Done: Predicting
-2016-09-06 08:16:24,924 DEBUG: Start: Getting Results
-2016-09-06 08:16:24,925 DEBUG: Done: Getting Results
-2016-09-06 08:16:24,926 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:16:24,926 INFO: Done: Result Analysis
-2016-09-06 08:16:24,928 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:16:24,928 DEBUG: Start: Training
-2016-09-06 08:16:24,948 DEBUG: Info: Time for Training: 0.11057305336[s]
-2016-09-06 08:16:24,948 DEBUG: Done: Training
-2016-09-06 08:16:24,948 DEBUG: Start: Predicting
-2016-09-06 08:16:24,952 DEBUG: Done: Predicting
-2016-09-06 08:16:24,952 DEBUG: Start: Getting Results
-2016-09-06 08:16:24,953 DEBUG: Done: Getting Results
-2016-09-06 08:16:24,953 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:16:24,953 INFO: Done: Result Analysis
-2016-09-06 08:16:25,237 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:25,238 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:16:25,239 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:25,239 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:25,239 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:16:25,240 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:16:25,240 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:16:25,241 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:16:25,241 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 08:16:25,242 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 08:16:25,242 INFO: Info: Shape of View3 :(300, 15)
-2016-09-06 08:16:25,242 INFO: Done: Read Database Files
-2016-09-06 08:16:25,242 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:16:25,243 INFO: Info: Shape of View3 :(300, 15)
-2016-09-06 08:16:25,243 INFO: Done: Read Database Files
-2016-09-06 08:16:25,243 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:16:25,248 INFO: Done: Determine validation split
-2016-09-06 08:16:25,248 INFO: Start: Determine 5 folds
-2016-09-06 08:16:25,249 INFO: Done: Determine validation split
-2016-09-06 08:16:25,249 INFO: Start: Determine 5 folds
-2016-09-06 08:16:25,262 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:16:25,263 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:16:25,263 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:16:25,263 INFO: Done: Determine folds
-2016-09-06 08:16:25,263 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:16:25,263 INFO: Start: Classification
-2016-09-06 08:16:25,263 INFO: Start: Fold number 1
-2016-09-06 08:16:25,266 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:16:25,266 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:16:25,267 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:16:25,267 INFO: Done: Determine folds
-2016-09-06 08:16:25,267 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:16:25,267 INFO: Start: Classification
-2016-09-06 08:16:25,267 INFO: Start: Fold number 1
-2016-09-06 08:16:25,304 DEBUG: Start: Iteration 1
-2016-09-06 08:16:25,312 DEBUG: View 0 : 0.576086956522
-2016-09-06 08:16:25,322 DEBUG: View 1 : 0.467391304348
-2016-09-06 08:16:25,329 INFO: Start: Classification
-2016-09-06 08:16:25,329 DEBUG: View 2 : 0.505434782609
-2016-09-06 08:16:25,339 DEBUG: View 3 : 0.538043478261
-2016-09-06 08:16:25,378 INFO: Done: Fold number 1
-2016-09-06 08:16:25,378 INFO: Start: Fold number 2
-2016-09-06 08:16:25,379 DEBUG: Best view : View1
-2016-09-06 08:16:25,433 INFO: Start: Classification
-2016-09-06 08:16:25,464 DEBUG: Start: Iteration 2
-2016-09-06 08:16:25,464 INFO: Done: Fold number 2
-2016-09-06 08:16:25,465 INFO: Start: Fold number 3
-2016-09-06 08:16:25,473 DEBUG: View 0 : 0.673913043478
-2016-09-06 08:16:25,480 DEBUG: View 1 : 0.690217391304
-2016-09-06 08:16:25,487 DEBUG: View 2 : 0.701086956522
-2016-09-06 08:16:25,496 DEBUG: View 3 : 0.70652173913
-2016-09-06 08:16:25,522 INFO: Start: Classification
-2016-09-06 08:16:25,536 DEBUG: Best view : View3
-2016-09-06 08:16:25,559 INFO: Done: Fold number 3
-2016-09-06 08:16:25,559 INFO: Start: Fold number 4
-2016-09-06 08:16:25,619 INFO: Start: Classification
-2016-09-06 08:16:25,654 INFO: Done: Fold number 4
-2016-09-06 08:16:25,654 INFO: Start: Fold number 5
-2016-09-06 08:16:25,711 DEBUG: Start: Iteration 3
-2016-09-06 08:16:25,719 DEBUG: View 0 : 0.673913043478
-2016-09-06 08:16:25,719 INFO: Start: Classification
-2016-09-06 08:16:25,726 DEBUG: View 1 : 0.690217391304
-2016-09-06 08:16:25,734 DEBUG: View 2 : 0.701086956522
-2016-09-06 08:16:25,741 DEBUG: View 3 : 0.70652173913
-2016-09-06 08:16:25,750 INFO: Done: Fold number 5
-2016-09-06 08:16:25,750 INFO: Done: Classification
-2016-09-06 08:16:25,750 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:16:25,750 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:16:25,755 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 55.6119923167
- -On Test : 57.1428571429
- -On Validation : 57.0786516854
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - SVM Linear with C : 4160
- - SVM Linear with C : 4160
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:16:25,755 INFO: Done: Result Analysis
-2016-09-06 08:16:25,784 DEBUG: Best view : View3
-2016-09-06 08:16:26,004 DEBUG: Start: Iteration 4
-2016-09-06 08:16:26,011 DEBUG: View 0 : 0.630434782609
-2016-09-06 08:16:26,018 DEBUG: View 1 : 0.619565217391
-2016-09-06 08:16:26,026 DEBUG: View 2 : 0.684782608696
-2016-09-06 08:16:26,033 DEBUG: View 3 : 0.673913043478
-2016-09-06 08:16:26,077 DEBUG: Best view : View3
-2016-09-06 08:16:26,365 INFO: Start: Classification
-2016-09-06 08:16:26,840 INFO: Done: Fold number 1
-2016-09-06 08:16:26,841 INFO: Start: Fold number 2
-2016-09-06 08:16:26,870 DEBUG: Start: Iteration 1
-2016-09-06 08:16:26,877 DEBUG: View 0 : 0.533707865169
-2016-09-06 08:16:26,884 DEBUG: View 1 : 0.556179775281
-2016-09-06 08:16:26,891 DEBUG: View 2 : 0.421348314607
-2016-09-06 08:16:26,898 DEBUG: View 3 : 0.578651685393
-2016-09-06 08:16:26,929 DEBUG: Best view : View0
-2016-09-06 08:16:27,008 DEBUG: Start: Iteration 2
-2016-09-06 08:16:27,015 DEBUG: View 0 : 0.707865168539
-2016-09-06 08:16:27,022 DEBUG: View 1 : 0.691011235955
-2016-09-06 08:16:27,029 DEBUG: View 2 : 0.691011235955
-2016-09-06 08:16:27,036 DEBUG: View 3 : 0.651685393258
-2016-09-06 08:16:27,074 DEBUG: Best view : View0
-2016-09-06 08:16:27,219 DEBUG: Start: Iteration 3
-2016-09-06 08:16:27,226 DEBUG: View 0 : 0.707865168539
-2016-09-06 08:16:27,234 DEBUG: View 1 : 0.685393258427
-2016-09-06 08:16:27,240 DEBUG: View 2 : 0.691011235955
-2016-09-06 08:16:27,248 DEBUG: View 3 : 0.651685393258
-2016-09-06 08:16:27,288 DEBUG: Best view : View1
-2016-09-06 08:16:27,501 DEBUG: Start: Iteration 4
-2016-09-06 08:16:27,509 DEBUG: View 0 : 0.674157303371
-2016-09-06 08:16:27,516 DEBUG: View 1 : 0.685393258427
-2016-09-06 08:16:27,523 DEBUG: View 2 : 0.634831460674
-2016-09-06 08:16:27,530 DEBUG: View 3 : 0.679775280899
-2016-09-06 08:16:27,573 DEBUG: Best view : View1
-2016-09-06 08:16:27,854 INFO: Start: Classification
-2016-09-06 08:16:28,319 INFO: Done: Fold number 2
-2016-09-06 08:16:28,319 INFO: Start: Fold number 3
-2016-09-06 08:16:28,348 DEBUG: Start: Iteration 1
-2016-09-06 08:16:28,355 DEBUG: View 0 : 0.438202247191
-2016-09-06 08:16:28,361 DEBUG: View 1 : 0.438202247191
-2016-09-06 08:16:28,367 DEBUG: View 2 : 0.438202247191
-2016-09-06 08:16:28,374 DEBUG: View 3 : 0.438202247191
-2016-09-06 08:16:28,374 WARNING: WARNING: All bad for iteration 0
-2016-09-06 08:16:28,406 DEBUG: Best view : View0
-2016-09-06 08:16:28,483 DEBUG: Start: Iteration 2
-2016-09-06 08:16:28,491 DEBUG: View 0 : 0.662921348315
-2016-09-06 08:16:28,498 DEBUG: View 1 : 0.691011235955
-2016-09-06 08:16:28,505 DEBUG: View 2 : 0.64606741573
-2016-09-06 08:16:28,512 DEBUG: View 3 : 0.668539325843
-2016-09-06 08:16:28,549 DEBUG: Best view : View1
-2016-09-06 08:16:28,696 DEBUG: Start: Iteration 3
-2016-09-06 08:16:28,704 DEBUG: View 0 : 0.662921348315
-2016-09-06 08:16:28,710 DEBUG: View 1 : 0.691011235955
-2016-09-06 08:16:28,718 DEBUG: View 2 : 0.64606741573
-2016-09-06 08:16:28,725 DEBUG: View 3 : 0.668539325843
-2016-09-06 08:16:28,765 DEBUG: Best view : View1
-2016-09-06 08:16:28,979 DEBUG: Start: Iteration 4
-2016-09-06 08:16:28,986 DEBUG: View 0 : 0.668539325843
-2016-09-06 08:16:28,993 DEBUG: View 1 : 0.674157303371
-2016-09-06 08:16:29,000 DEBUG: View 2 : 0.634831460674
-2016-09-06 08:16:29,007 DEBUG: View 3 : 0.691011235955
-2016-09-06 08:16:29,050 DEBUG: Best view : View0
-2016-09-06 08:16:29,329 INFO: Start: Classification
-2016-09-06 08:16:29,793 INFO: Done: Fold number 3
-2016-09-06 08:16:29,794 INFO: Start: Fold number 4
-2016-09-06 08:16:29,823 DEBUG: Start: Iteration 1
-2016-09-06 08:16:29,830 DEBUG: View 0 : 0.556756756757
-2016-09-06 08:16:29,837 DEBUG: View 1 : 0.556756756757
-2016-09-06 08:16:29,843 DEBUG: View 2 : 0.556756756757
-2016-09-06 08:16:29,850 DEBUG: View 3 : 0.556756756757
-2016-09-06 08:16:29,883 DEBUG: Best view : View0
-2016-09-06 08:16:29,964 DEBUG: Start: Iteration 2
-2016-09-06 08:16:29,972 DEBUG: View 0 : 0.718918918919
-2016-09-06 08:16:29,979 DEBUG: View 1 : 0.664864864865
-2016-09-06 08:16:29,986 DEBUG: View 2 : 0.664864864865
-2016-09-06 08:16:29,994 DEBUG: View 3 : 0.632432432432
-2016-09-06 08:16:30,033 DEBUG: Best view : View0
-2016-09-06 08:16:30,184 DEBUG: Start: Iteration 3
-2016-09-06 08:16:30,192 DEBUG: View 0 : 0.718918918919
-2016-09-06 08:16:30,199 DEBUG: View 1 : 0.664864864865
-2016-09-06 08:16:30,206 DEBUG: View 2 : 0.664864864865
-2016-09-06 08:16:30,213 DEBUG: View 3 : 0.632432432432
-2016-09-06 08:16:30,254 DEBUG: Best view : View0
-2016-09-06 08:16:30,475 DEBUG: Start: Iteration 4
-2016-09-06 08:16:30,482 DEBUG: View 0 : 0.637837837838
-2016-09-06 08:16:30,489 DEBUG: View 1 : 0.697297297297
-2016-09-06 08:16:30,497 DEBUG: View 2 : 0.67027027027
-2016-09-06 08:16:30,504 DEBUG: View 3 : 0.6
-2016-09-06 08:16:30,548 DEBUG: Best view : View1
-2016-09-06 08:16:30,839 INFO: Start: Classification
-2016-09-06 08:16:31,314 INFO: Done: Fold number 4
-2016-09-06 08:16:31,314 INFO: Start: Fold number 5
-2016-09-06 08:16:31,343 DEBUG: Start: Iteration 1
-2016-09-06 08:16:31,349 DEBUG: View 0 : 0.559322033898
-2016-09-06 08:16:31,356 DEBUG: View 1 : 0.559322033898
-2016-09-06 08:16:31,363 DEBUG: View 2 : 0.559322033898
-2016-09-06 08:16:31,369 DEBUG: View 3 : 0.559322033898
-2016-09-06 08:16:31,401 DEBUG: Best view : View0
-2016-09-06 08:16:31,478 DEBUG: Start: Iteration 2
-2016-09-06 08:16:31,485 DEBUG: View 0 : 0.71186440678
-2016-09-06 08:16:31,492 DEBUG: View 1 : 0.677966101695
-2016-09-06 08:16:31,499 DEBUG: View 2 : 0.689265536723
-2016-09-06 08:16:31,507 DEBUG: View 3 : 0.649717514124
-2016-09-06 08:16:31,544 DEBUG: Best view : View0
-2016-09-06 08:16:31,689 DEBUG: Start: Iteration 3
-2016-09-06 08:16:31,696 DEBUG: View 0 : 0.71186440678
-2016-09-06 08:16:31,703 DEBUG: View 1 : 0.677966101695
-2016-09-06 08:16:31,710 DEBUG: View 2 : 0.689265536723
-2016-09-06 08:16:31,717 DEBUG: View 3 : 0.649717514124
-2016-09-06 08:16:31,757 DEBUG: Best view : View0
-2016-09-06 08:16:31,967 DEBUG: Start: Iteration 4
-2016-09-06 08:16:31,974 DEBUG: View 0 : 0.638418079096
-2016-09-06 08:16:31,981 DEBUG: View 1 : 0.610169491525
-2016-09-06 08:16:31,988 DEBUG: View 2 : 0.683615819209
-2016-09-06 08:16:31,995 DEBUG: View 3 : 0.649717514124
-2016-09-06 08:16:32,037 DEBUG: Best view : View2
-2016-09-06 08:16:32,315 INFO: Start: Classification
-2016-09-06 08:16:32,782 INFO: Done: Fold number 5
-2016-09-06 08:16:32,782 INFO: Done: Classification
-2016-09-06 08:16:32,783 INFO: Info: Time for Classification: 7[s]
-2016-09-06 08:16:32,783 INFO: Start: Result Analysis for Mumbo
-2016-09-06 08:16:35,270 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 70.835988937
- -On Test : 48.5714285714
- -On Validation : 62.0224719101Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 14), View1 of shape (300, 10), View2 of shape (300, 11), View3 of shape (300, 15)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:07 0:00:00
- Total 0:00:20 0:00:02
- So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.255434782609
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.246739130435
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.259239130435
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.2625
- - Percentage of time chosen : 0.3
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.262359550562
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.261797752809
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.243820224719
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.256179775281
- - Percentage of time chosen : 0.0
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.243258426966
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.249438202247
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.236516853933
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.246629213483
- - Percentage of time chosen : 0.0
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.263243243243
- - Percentage of time chosen : 0.9
- - On View1 :
- - Mean average Accuracy : 0.258378378378
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.255675675676
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.242162162162
- - Percentage of time chosen : 0.0
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.262146892655
- - Percentage of time chosen : 0.9
- - On View1 :
- - Mean average Accuracy : 0.252542372881
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.262146892655
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.250847457627
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 44.0217391304
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View1
- Fold 2
- Accuracy on train : 44.3820224719
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 3
- Accuracy on train : 43.8202247191
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 4
- Accuracy on train : 44.3243243243
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 5
- Accuracy on train : 44.0677966102
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 70.652173913
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View3
- Fold 2
- Accuracy on train : 70.7865168539
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View0
- Fold 3
- Accuracy on train : 69.1011235955
- Accuracy on test : 0.0
- Accuracy on validation : 61.797752809
- Selected View : View1
- Fold 4
- Accuracy on train : 71.8918918919
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View0
- Fold 5
- Accuracy on train : 71.186440678
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View0
- - Iteration 3
- Fold 1
- Accuracy on train : 70.652173913
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View3
- Fold 2
- Accuracy on train : 68.5393258427
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View1
- Fold 3
- Accuracy on train : 68.5393258427
- Accuracy on test : 0.0
- Accuracy on validation : 61.797752809
- Selected View : View1
- Fold 4
- Accuracy on train : 71.8918918919
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View0
- Fold 5
- Accuracy on train : 71.186440678
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View0
- - Iteration 4
- Fold 1
- Accuracy on train : 70.652173913
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View3
- Fold 2
- Accuracy on train : 71.3483146067
- Accuracy on test : 0.0
- Accuracy on validation : 52.808988764
- Selected View : View1
- Fold 3
- Accuracy on train : 69.1011235955
- Accuracy on test : 0.0
- Accuracy on validation : 61.797752809
- Selected View : View0
- Fold 4
- Accuracy on train : 71.8918918919
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View1
- Fold 5
- Accuracy on train : 71.186440678
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View2
- - Iteration 5
- Fold 1
- Accuracy on train : 44.0217391304
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 2
- Accuracy on train : 44.3820224719
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 3
- Accuracy on train : 43.8202247191
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 4
- Accuracy on train : 44.3243243243
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 5
- Accuracy on train : 44.0677966102
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
-2016-09-06 08:16:35,456 INFO: Done: Result Analysis
-2016-09-06 08:16:35,608 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:35,608 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:35,608 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:35,609 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:35,609 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:16:35,609 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:16:35,609 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:16:35,610 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:16:35,610 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 08:16:35,610 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 08:16:35,610 INFO: Info: Shape of View3 :(300, 15)
-2016-09-06 08:16:35,611 INFO: Done: Read Database Files
-2016-09-06 08:16:35,611 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:16:35,611 INFO: Info: Shape of View3 :(300, 15)
-2016-09-06 08:16:35,611 INFO: Done: Read Database Files
-2016-09-06 08:16:35,611 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:16:35,616 INFO: Done: Determine validation split
-2016-09-06 08:16:35,616 INFO: Start: Determine 5 folds
-2016-09-06 08:16:35,616 INFO: Done: Determine validation split
-2016-09-06 08:16:35,616 INFO: Start: Determine 5 folds
-2016-09-06 08:16:35,626 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:16:35,626 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:16:35,626 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:16:35,626 INFO: Done: Determine folds
-2016-09-06 08:16:35,626 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:16:35,627 INFO: Start: Classification
-2016-09-06 08:16:35,627 INFO: Start: Fold number 1
-2016-09-06 08:16:35,628 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:16:35,628 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:16:35,628 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:16:35,628 INFO: Done: Determine folds
-2016-09-06 08:16:35,628 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:16:35,629 INFO: Start: Classification
-2016-09-06 08:16:35,629 INFO: Start: Fold number 1
-2016-09-06 08:16:35,695 INFO: Start: Classification
-2016-09-06 08:16:35,731 INFO: Start: Classification
-2016-09-06 08:16:35,762 INFO: Done: Fold number 1
-2016-09-06 08:16:35,762 INFO: Start: Fold number 2
-2016-09-06 08:16:35,791 INFO: Done: Fold number 1
-2016-09-06 08:16:35,791 INFO: Start: Fold number 2
-2016-09-06 08:16:35,835 INFO: Start: Classification
-2016-09-06 08:16:35,851 INFO: Start: Classification
-2016-09-06 08:16:35,865 INFO: Done: Fold number 2
-2016-09-06 08:16:35,865 INFO: Start: Fold number 3
-2016-09-06 08:16:35,934 INFO: Done: Fold number 2
-2016-09-06 08:16:35,934 INFO: Start: Fold number 3
-2016-09-06 08:16:35,939 INFO: Start: Classification
-2016-09-06 08:16:35,971 INFO: Done: Fold number 3
-2016-09-06 08:16:35,972 INFO: Start: Fold number 4
-2016-09-06 08:16:35,994 INFO: Start: Classification
-2016-09-06 08:16:36,047 INFO: Start: Classification
-2016-09-06 08:16:36,078 INFO: Done: Fold number 4
-2016-09-06 08:16:36,078 INFO: Start: Fold number 5
-2016-09-06 08:16:36,078 INFO: Done: Fold number 3
-2016-09-06 08:16:36,078 INFO: Start: Fold number 4
-2016-09-06 08:16:36,140 INFO: Start: Classification
-2016-09-06 08:16:36,153 INFO: Start: Classification
-2016-09-06 08:16:36,183 INFO: Done: Fold number 5
-2016-09-06 08:16:36,183 INFO: Done: Classification
-2016-09-06 08:16:36,183 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:16:36,183 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:16:36,188 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 57.1428571429
- -On Validation : 85.1685393258
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - SVM Linear with C : 4160
- - SVM Linear with C : 4160
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:16:36,188 INFO: Done: Result Analysis
-2016-09-06 08:16:36,225 INFO: Done: Fold number 4
-2016-09-06 08:16:36,225 INFO: Start: Fold number 5
-2016-09-06 08:16:36,281 INFO: Start: Classification
-2016-09-06 08:16:36,361 INFO: Done: Fold number 5
-2016-09-06 08:16:36,361 INFO: Done: Classification
-2016-09-06 08:16:36,361 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:16:36,361 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:16:36,365 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 77.2241829793
- -On Test : 57.619047619
- -On Validation : 68.5393258427
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SVM Linear with C : 4160
- - SVM Linear with C : 4160
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:16:36,366 INFO: Done: Result Analysis
-2016-09-06 08:16:36,455 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:36,456 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:36,456 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:36,456 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:16:36,456 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:36,457 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:16:36,457 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:16:36,457 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 08:16:36,458 INFO: Info: Shape of View3 :(300, 15)
-2016-09-06 08:16:36,458 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:16:36,458 INFO: Done: Read Database Files
-2016-09-06 08:16:36,458 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:16:36,458 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 08:16:36,459 INFO: Info: Shape of View3 :(300, 15)
-2016-09-06 08:16:36,459 INFO: Done: Read Database Files
-2016-09-06 08:16:36,459 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:16:36,462 INFO: Done: Determine validation split
-2016-09-06 08:16:36,462 INFO: Start: Determine 5 folds
-2016-09-06 08:16:36,463 INFO: Done: Determine validation split
-2016-09-06 08:16:36,464 INFO: Start: Determine 5 folds
-2016-09-06 08:16:36,470 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:16:36,470 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:16:36,470 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:16:36,470 INFO: Done: Determine folds
-2016-09-06 08:16:36,470 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:16:36,470 INFO: Start: Classification
-2016-09-06 08:16:36,470 INFO: Start: Fold number 1
-2016-09-06 08:16:36,474 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:16:36,474 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:16:36,474 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:16:36,474 INFO: Done: Determine folds
-2016-09-06 08:16:36,475 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:16:36,475 INFO: Start: Classification
-2016-09-06 08:16:36,475 INFO: Start: Fold number 1
-2016-09-06 08:16:36,495 INFO: Start: Classification
-2016-09-06 08:16:36,520 INFO: Done: Fold number 1
-2016-09-06 08:16:36,521 INFO: Start: Fold number 2
-2016-09-06 08:16:36,539 INFO: Start: Classification
-2016-09-06 08:16:36,544 INFO: Start: Classification
-2016-09-06 08:16:36,569 INFO: Done: Fold number 2
-2016-09-06 08:16:36,570 INFO: Start: Fold number 3
-2016-09-06 08:16:36,610 INFO: Start: Classification
-2016-09-06 08:16:36,611 INFO: Done: Fold number 1
-2016-09-06 08:16:36,611 INFO: Start: Fold number 2
-2016-09-06 08:16:36,649 INFO: Done: Fold number 3
-2016-09-06 08:16:36,649 INFO: Start: Fold number 4
-2016-09-06 08:16:36,680 INFO: Start: Classification
-2016-09-06 08:16:36,688 INFO: Start: Classification
-2016-09-06 08:16:36,705 INFO: Done: Fold number 4
-2016-09-06 08:16:36,705 INFO: Start: Fold number 5
-2016-09-06 08:16:36,729 INFO: Done: Fold number 2
-2016-09-06 08:16:36,729 INFO: Start: Classification
-2016-09-06 08:16:36,730 INFO: Start: Fold number 3
-2016-09-06 08:16:36,755 INFO: Done: Fold number 5
-2016-09-06 08:16:36,755 INFO: Done: Classification
-2016-09-06 08:16:36,755 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:16:36,755 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:16:36,760 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 56.6666666667
- -On Validation : 85.1685393258
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:16:36,760 INFO: Done: Result Analysis
-2016-09-06 08:16:36,792 INFO: Start: Classification
-2016-09-06 08:16:36,833 INFO: Done: Fold number 3
-2016-09-06 08:16:36,833 INFO: Start: Fold number 4
-2016-09-06 08:16:36,888 INFO: Start: Classification
-2016-09-06 08:16:36,923 INFO: Done: Fold number 4
-2016-09-06 08:16:36,923 INFO: Start: Fold number 5
-2016-09-06 08:16:36,976 INFO: Start: Classification
-2016-09-06 08:16:37,010 INFO: Done: Fold number 5
-2016-09-06 08:16:37,010 INFO: Done: Classification
-2016-09-06 08:16:37,010 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:16:37,010 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:16:37,015 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 23.3651324521
- -On Test : 21.4285714286
- -On Validation : 20.0
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - SVM Linear with C : 4160
- - SVM Linear with C : 4160
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:16:37,015 INFO: Done: Result Analysis
-2016-09-06 08:16:37,101 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:37,101 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:16:37,101 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:37,101 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:16:37,102 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:16:37,102 INFO: Info: Shape of View0 :(300, 14)
-2016-09-06 08:16:37,102 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:16:37,102 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 08:16:37,103 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 08:16:37,103 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 08:16:37,103 INFO: Info: Shape of View3 :(300, 15)
-2016-09-06 08:16:37,103 INFO: Info: Shape of View3 :(300, 15)
-2016-09-06 08:16:37,103 INFO: Done: Read Database Files
-2016-09-06 08:16:37,103 INFO: Done: Read Database Files
-2016-09-06 08:16:37,104 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:16:37,104 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:16:37,108 INFO: Done: Determine validation split
-2016-09-06 08:16:37,108 INFO: Done: Determine validation split
-2016-09-06 08:16:37,108 INFO: Start: Determine 5 folds
-2016-09-06 08:16:37,108 INFO: Start: Determine 5 folds
-2016-09-06 08:16:37,115 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:16:37,115 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:16:37,115 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:16:37,115 INFO: Done: Determine folds
-2016-09-06 08:16:37,115 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:16:37,116 INFO: Start: Classification
-2016-09-06 08:16:37,116 INFO: Start: Fold number 1
-2016-09-06 08:16:37,118 INFO: Info: Length of Learning Sets: 169
-2016-09-06 08:16:37,118 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:16:37,118 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:16:37,118 INFO: Done: Determine folds
-2016-09-06 08:16:37,118 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:16:37,119 INFO: Start: Classification
-2016-09-06 08:16:37,119 INFO: Start: Fold number 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index aca2f4e31b74af5aa1b5a26913e5bb894bef4a13..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4985e158d7db4ee41cdc347496356b652f20bc6e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 661fc596a4ea38f38b59eace786721ff0711ace4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081618Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.628571428571
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bd742e241119a46387a177e8e58ba23f99170425..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 28, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7c21013c5cfb3e9553acd692f3b613f9296f95d4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ab82a3f3fa5781bb300faae77b3b35c858e03379..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081619Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.666666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.666666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 646cc11f1bab3d4c46370f674ec527f0b4674a9e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f5f0502c65da4ff3420486f35aae5c6f5a6d5102..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c4674ee3b8b75aee578ef6c6bc4ef48045ec484e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d22eb125e0b572f95af67d24a09e166f5d43f2da..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4887e38c6af1b5e6a7decfb8732a0a38d54d26df..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081620Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a57838cca165f6d77035599e1f3b28c45893fe6f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 28, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7b171c6001c414e6e78ca527894e43d99d3d1ec2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b20b752dcb74edac050c54a1c8cd4ad8c2752964..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fb445cc2a17ffa9cd8970ee2a5ed32b54290bd02..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3904
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7286019a2a8f1117f5285c6c33f1c6ffbe267a1e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081621Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b892e0810730663290ab3ce786640e01e324a89f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 098c6142d06cd8a8e3b704bc6134ace80771d868..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fc8244e3c0e41360dcc945f8304cd432587f93ff..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081622Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8e5be8ed82137438f65f84b0e68b9d1c6d39ddc9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fc62cd0fd9627f8e58f075a169455d774870e14d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fafd56e10af37b5db48c7a5004877cf691a598b5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 28, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 24e1dd84183caf3535371499ead70f86b7327b6b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2314cfafa79862ef6ae6f28674f18419fb6cd372..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a9b1e75d0f0fc6db07dc7d4debb6d0b2865a42db..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0ebf8cd6abbec4b077b5c1711497183512072c05..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081623Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a09329d1a22050d9fd15e997e11e90d5d5d88a63..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 80d8383fb1cce9c3002e53905247f8cf218a753a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 19, max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ffa5b2332ec6d3612b4f7ce376d722e26491d00b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d9839a8d14b252b2d61efd1746d7a4679b405de8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081624Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081625Results-Fusion-LateFusion-BayesianInference-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081625Results-Fusion-LateFusion-BayesianInference-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 93341e7aa6390e15667ed96403a390e1b09c4ee8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081625Results-Fusion-LateFusion-BayesianInference-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 55.6119923167
- -On Test : 57.1428571429
- -On Validation : 57.0786516854
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - SVM Linear with C : 4160
- - SVM Linear with C : 4160
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index fc4f5126fa0032caad548842457d5511636756de..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index b1a0e37287ab424871a2facc4086572d163113ce..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081635Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,235 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 70.835988937
- -On Test : 48.5714285714
- -On Validation : 62.0224719101Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 14), View1 of shape (300, 10), View2 of shape (300, 11), View3 of shape (300, 15)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:07 0:00:00
- Total 0:00:20 0:00:02
- So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.255434782609
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.246739130435
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.259239130435
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.2625
- - Percentage of time chosen : 0.3
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.262359550562
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.261797752809
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.243820224719
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.256179775281
- - Percentage of time chosen : 0.0
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.243258426966
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.249438202247
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.236516853933
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.246629213483
- - Percentage of time chosen : 0.0
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.263243243243
- - Percentage of time chosen : 0.9
- - On View1 :
- - Mean average Accuracy : 0.258378378378
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.255675675676
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.242162162162
- - Percentage of time chosen : 0.0
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.262146892655
- - Percentage of time chosen : 0.9
- - On View1 :
- - Mean average Accuracy : 0.252542372881
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.262146892655
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.250847457627
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 44.0217391304
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View1
- Fold 2
- Accuracy on train : 44.3820224719
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 3
- Accuracy on train : 43.8202247191
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 4
- Accuracy on train : 44.3243243243
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 5
- Accuracy on train : 44.0677966102
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 70.652173913
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View3
- Fold 2
- Accuracy on train : 70.7865168539
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View0
- Fold 3
- Accuracy on train : 69.1011235955
- Accuracy on test : 0.0
- Accuracy on validation : 61.797752809
- Selected View : View1
- Fold 4
- Accuracy on train : 71.8918918919
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View0
- Fold 5
- Accuracy on train : 71.186440678
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View0
- - Iteration 3
- Fold 1
- Accuracy on train : 70.652173913
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View3
- Fold 2
- Accuracy on train : 68.5393258427
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View1
- Fold 3
- Accuracy on train : 68.5393258427
- Accuracy on test : 0.0
- Accuracy on validation : 61.797752809
- Selected View : View1
- Fold 4
- Accuracy on train : 71.8918918919
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View0
- Fold 5
- Accuracy on train : 71.186440678
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View0
- - Iteration 4
- Fold 1
- Accuracy on train : 70.652173913
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View3
- Fold 2
- Accuracy on train : 71.3483146067
- Accuracy on test : 0.0
- Accuracy on validation : 52.808988764
- Selected View : View1
- Fold 3
- Accuracy on train : 69.1011235955
- Accuracy on test : 0.0
- Accuracy on validation : 61.797752809
- Selected View : View0
- Fold 4
- Accuracy on train : 71.8918918919
- Accuracy on test : 0.0
- Accuracy on validation : 69.6629213483
- Selected View : View1
- Fold 5
- Accuracy on train : 71.186440678
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View2
- - Iteration 5
- Fold 1
- Accuracy on train : 44.0217391304
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 2
- Accuracy on train : 44.3820224719
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 3
- Accuracy on train : 43.8202247191
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 4
- Accuracy on train : 44.3243243243
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
- Fold 5
- Accuracy on train : 44.0677966102
- Accuracy on test : 0.0
- Accuracy on validation : 42.6966292135
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 121ae6ee3ba57deed332d6b3a2257e3d9c64a4dd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 56.6666666667
- -On Validation : 85.1685393258
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-MajorityVoting-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-MajorityVoting-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 77d5a0f819a0e5f065eb80fee558c66352ada8f7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-MajorityVoting-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 77.2241829793
- -On Test : 57.619047619
- -On Validation : 68.5393258427
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SVM Linear with C : 4160
- - SVM Linear with C : 4160
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-SVMForLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-SVMForLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index f0611ce5e85b4b9bf856109b7da7bd1b84754524..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081636Results-Fusion-LateFusion-SVMForLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 57.1428571429
- -On Validation : 85.1685393258
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - SVM Linear with C : 4160
- - SVM Linear with C : 4160
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081637Results-Fusion-LateFusion-WeightedLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081637Results-Fusion-LateFusion-WeightedLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index e87cbf61fc4aada2c311bd5b61671d1dd92bda4c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081637Results-Fusion-LateFusion-WeightedLinear-SVMLinear-SVMRBF-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 23.3651324521
- -On Test : 21.4285714286
- -On Validation : 20.0
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - SVM Linear with C : 4160
- - SVM Linear with C : 4160
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081825-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-081825-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 35bb0793719c60aab803c454ebc8b54dc04daf57..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081825-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2242 +0,0 @@
-2016-09-06 08:18:25,832 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:18:25,832 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00014275 Gbytes /!\
-2016-09-06 08:18:30,846 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:18:30,849 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:18:30,899 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:30,899 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:18:30,900 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:30,900 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:30,900 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:18:30,900 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:30,900 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:30,900 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:30,900 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:30,900 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:30,901 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:30,901 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:30,901 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:30,901 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:30,961 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:30,961 DEBUG: Start: Training
-2016-09-06 08:18:30,963 DEBUG: Info: Time for Training: 0.0640490055084[s]
-2016-09-06 08:18:30,963 DEBUG: Done: Training
-2016-09-06 08:18:30,963 DEBUG: Start: Predicting
-2016-09-06 08:18:30,966 DEBUG: Done: Predicting
-2016-09-06 08:18:30,966 DEBUG: Start: Getting Results
-2016-09-06 08:18:30,967 DEBUG: Done: Getting Results
-2016-09-06 08:18:30,967 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:18:30,967 INFO: Done: Result Analysis
-2016-09-06 08:18:30,988 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:30,988 DEBUG: Start: Training
-2016-09-06 08:18:30,992 DEBUG: Info: Time for Training: 0.0932528972626[s]
-2016-09-06 08:18:30,992 DEBUG: Done: Training
-2016-09-06 08:18:30,992 DEBUG: Start: Predicting
-2016-09-06 08:18:30,995 DEBUG: Done: Predicting
-2016-09-06 08:18:30,995 DEBUG: Start: Getting Results
-2016-09-06 08:18:30,997 DEBUG: Done: Getting Results
-2016-09-06 08:18:30,997 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:18:30,997 INFO: Done: Result Analysis
-2016-09-06 08:18:31,158 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,158 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,159 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:18:31,159 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:18:31,159 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:31,159 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:31,160 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,160 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,160 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,160 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,160 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:31,160 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:31,160 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,160 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,244 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:31,244 DEBUG: Start: Training
-2016-09-06 08:18:31,245 DEBUG: Info: Time for Training: 0.0878131389618[s]
-2016-09-06 08:18:31,245 DEBUG: Done: Training
-2016-09-06 08:18:31,245 DEBUG: Start: Predicting
-2016-09-06 08:18:31,255 DEBUG: Done: Predicting
-2016-09-06 08:18:31,255 DEBUG: Start: Getting Results
-2016-09-06 08:18:31,257 DEBUG: Done: Getting Results
-2016-09-06 08:18:31,257 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:18:31,257 INFO: Done: Result Analysis
-2016-09-06 08:18:31,473 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:31,473 DEBUG: Start: Training
-2016-09-06 08:18:31,484 DEBUG: Info: Time for Training: 0.32683300972[s]
-2016-09-06 08:18:31,484 DEBUG: Done: Training
-2016-09-06 08:18:31,484 DEBUG: Start: Predicting
-2016-09-06 08:18:31,487 DEBUG: Done: Predicting
-2016-09-06 08:18:31,487 DEBUG: Start: Getting Results
-2016-09-06 08:18:31,488 DEBUG: Done: Getting Results
-2016-09-06 08:18:31,489 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.919047619048
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:18:31,489 INFO: Done: Result Analysis
-2016-09-06 08:18:31,608 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,608 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,608 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:18:31,608 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:18:31,608 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:31,608 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:31,609 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,609 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,609 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,609 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,609 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:31,609 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:31,609 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,609 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,683 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:31,684 DEBUG: Start: Training
-2016-09-06 08:18:31,684 DEBUG: Info: Time for Training: 0.0768630504608[s]
-2016-09-06 08:18:31,684 DEBUG: Done: Training
-2016-09-06 08:18:31,685 DEBUG: Start: Predicting
-2016-09-06 08:18:31,695 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:31,695 DEBUG: Start: Training
-2016-09-06 08:18:31,707 DEBUG: Done: Predicting
-2016-09-06 08:18:31,707 DEBUG: Start: Getting Results
-2016-09-06 08:18:31,709 DEBUG: Done: Getting Results
-2016-09-06 08:18:31,709 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:18:31,709 INFO: Done: Result Analysis
-2016-09-06 08:18:31,721 DEBUG: Info: Time for Training: 0.11314201355[s]
-2016-09-06 08:18:31,721 DEBUG: Done: Training
-2016-09-06 08:18:31,721 DEBUG: Start: Predicting
-2016-09-06 08:18:31,724 DEBUG: Done: Predicting
-2016-09-06 08:18:31,724 DEBUG: Start: Getting Results
-2016-09-06 08:18:31,725 DEBUG: Done: Getting Results
-2016-09-06 08:18:31,725 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:31,725 INFO: Done: Result Analysis
-2016-09-06 08:18:31,863 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,863 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:31,863 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:18:31,863 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:18:31,864 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:31,864 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:31,865 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,865 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 08:18:31,865 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,865 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 08:18:31,865 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:31,865 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:31,865 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,865 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:31,982 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:31,982 DEBUG: Start: Training
-2016-09-06 08:18:31,993 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:31,993 DEBUG: Start: Training
-2016-09-06 08:18:32,006 DEBUG: Info: Time for Training: 0.143626928329[s]
-2016-09-06 08:18:32,006 DEBUG: Done: Training
-2016-09-06 08:18:32,006 DEBUG: Start: Predicting
-2016-09-06 08:18:32,014 DEBUG: Done: Predicting
-2016-09-06 08:18:32,014 DEBUG: Start: Getting Results
-2016-09-06 08:18:32,015 DEBUG: Done: Getting Results
-2016-09-06 08:18:32,016 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,016 INFO: Done: Result Analysis
-2016-09-06 08:18:32,018 DEBUG: Info: Time for Training: 0.155956029892[s]
-2016-09-06 08:18:32,018 DEBUG: Done: Training
-2016-09-06 08:18:32,018 DEBUG: Start: Predicting
-2016-09-06 08:18:32,022 DEBUG: Done: Predicting
-2016-09-06 08:18:32,022 DEBUG: Start: Getting Results
-2016-09-06 08:18:32,023 DEBUG: Done: Getting Results
-2016-09-06 08:18:32,023 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,023 INFO: Done: Result Analysis
-2016-09-06 08:18:32,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,112 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:18:32,113 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:32,113 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:18:32,113 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:32,113 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,113 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,114 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:32,114 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,114 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,114 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,114 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:32,114 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,181 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:32,181 DEBUG: Start: Training
-2016-09-06 08:18:32,184 DEBUG: Info: Time for Training: 0.0724370479584[s]
-2016-09-06 08:18:32,184 DEBUG: Done: Training
-2016-09-06 08:18:32,184 DEBUG: Start: Predicting
-2016-09-06 08:18:32,187 DEBUG: Done: Predicting
-2016-09-06 08:18:32,187 DEBUG: Start: Getting Results
-2016-09-06 08:18:32,188 DEBUG: Done: Getting Results
-2016-09-06 08:18:32,188 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,188 INFO: Done: Result Analysis
-2016-09-06 08:18:32,202 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:32,202 DEBUG: Start: Training
-2016-09-06 08:18:32,206 DEBUG: Info: Time for Training: 0.0945439338684[s]
-2016-09-06 08:18:32,206 DEBUG: Done: Training
-2016-09-06 08:18:32,206 DEBUG: Start: Predicting
-2016-09-06 08:18:32,209 DEBUG: Done: Predicting
-2016-09-06 08:18:32,209 DEBUG: Start: Getting Results
-2016-09-06 08:18:32,211 DEBUG: Done: Getting Results
-2016-09-06 08:18:32,211 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,211 INFO: Done: Result Analysis
-2016-09-06 08:18:32,363 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,363 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,364 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:18:32,364 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:18:32,364 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:32,364 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:32,365 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,365 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,365 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,365 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,365 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:32,365 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:32,365 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,365 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,420 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:32,421 DEBUG: Start: Training
-2016-09-06 08:18:32,421 DEBUG: Info: Time for Training: 0.0582938194275[s]
-2016-09-06 08:18:32,421 DEBUG: Done: Training
-2016-09-06 08:18:32,421 DEBUG: Start: Predicting
-2016-09-06 08:18:32,428 DEBUG: Done: Predicting
-2016-09-06 08:18:32,429 DEBUG: Start: Getting Results
-2016-09-06 08:18:32,430 DEBUG: Done: Getting Results
-2016-09-06 08:18:32,430 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 36
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,430 INFO: Done: Result Analysis
-2016-09-06 08:18:32,646 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:32,646 DEBUG: Start: Training
-2016-09-06 08:18:32,657 DEBUG: Info: Time for Training: 0.294118881226[s]
-2016-09-06 08:18:32,657 DEBUG: Done: Training
-2016-09-06 08:18:32,657 DEBUG: Start: Predicting
-2016-09-06 08:18:32,660 DEBUG: Done: Predicting
-2016-09-06 08:18:32,660 DEBUG: Start: Getting Results
-2016-09-06 08:18:32,661 DEBUG: Done: Getting Results
-2016-09-06 08:18:32,661 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,662 INFO: Done: Result Analysis
-2016-09-06 08:18:32,818 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,818 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:32,819 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:18:32,819 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:18:32,819 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:32,819 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:32,819 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,819 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:32,820 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,820 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:32,820 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:32,820 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:32,820 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,820 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:32,905 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:32,905 DEBUG: Start: Training
-2016-09-06 08:18:32,906 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:32,906 DEBUG: Start: Training
-2016-09-06 08:18:32,907 DEBUG: Info: Time for Training: 0.089103937149[s]
-2016-09-06 08:18:32,907 DEBUG: Done: Training
-2016-09-06 08:18:32,907 DEBUG: Start: Predicting
-2016-09-06 08:18:32,924 DEBUG: Info: Time for Training: 0.106693983078[s]
-2016-09-06 08:18:32,924 DEBUG: Done: Training
-2016-09-06 08:18:32,925 DEBUG: Start: Predicting
-2016-09-06 08:18:32,925 DEBUG: Done: Predicting
-2016-09-06 08:18:32,925 DEBUG: Start: Getting Results
-2016-09-06 08:18:32,927 DEBUG: Done: Getting Results
-2016-09-06 08:18:32,927 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,927 INFO: Done: Result Analysis
-2016-09-06 08:18:32,928 DEBUG: Done: Predicting
-2016-09-06 08:18:32,928 DEBUG: Start: Getting Results
-2016-09-06 08:18:32,929 DEBUG: Done: Getting Results
-2016-09-06 08:18:32,929 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:18:32,929 INFO: Done: Result Analysis
-2016-09-06 08:18:33,067 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,067 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,067 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:18:33,067 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:18:33,067 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:33,067 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:33,068 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:33,068 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 08:18:33,068 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:33,068 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 08:18:33,068 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:33,068 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:33,068 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,068 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,157 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:33,157 DEBUG: Start: Training
-2016-09-06 08:18:33,159 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:33,160 DEBUG: Start: Training
-2016-09-06 08:18:33,174 DEBUG: Info: Time for Training: 0.10836482048[s]
-2016-09-06 08:18:33,174 DEBUG: Done: Training
-2016-09-06 08:18:33,174 DEBUG: Start: Predicting
-2016-09-06 08:18:33,176 DEBUG: Info: Time for Training: 0.109962940216[s]
-2016-09-06 08:18:33,176 DEBUG: Done: Training
-2016-09-06 08:18:33,176 DEBUG: Start: Predicting
-2016-09-06 08:18:33,180 DEBUG: Done: Predicting
-2016-09-06 08:18:33,180 DEBUG: Done: Predicting
-2016-09-06 08:18:33,180 DEBUG: Start: Getting Results
-2016-09-06 08:18:33,180 DEBUG: Start: Getting Results
-2016-09-06 08:18:33,181 DEBUG: Done: Getting Results
-2016-09-06 08:18:33,181 DEBUG: Done: Getting Results
-2016-09-06 08:18:33,181 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,181 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,181 INFO: Done: Result Analysis
-2016-09-06 08:18:33,181 INFO: Done: Result Analysis
-2016-09-06 08:18:33,314 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,314 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,315 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:18:33,315 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:33,315 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:18:33,315 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:33,316 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:33,316 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:33,316 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:33,316 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:33,316 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:33,316 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:33,316 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,316 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,380 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:33,380 DEBUG: Start: Training
-2016-09-06 08:18:33,383 DEBUG: Info: Time for Training: 0.0689311027527[s]
-2016-09-06 08:18:33,383 DEBUG: Done: Training
-2016-09-06 08:18:33,383 DEBUG: Start: Predicting
-2016-09-06 08:18:33,385 DEBUG: Done: Predicting
-2016-09-06 08:18:33,385 DEBUG: Start: Getting Results
-2016-09-06 08:18:33,386 DEBUG: Done: Getting Results
-2016-09-06 08:18:33,386 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,387 INFO: Done: Result Analysis
-2016-09-06 08:18:33,406 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:33,406 DEBUG: Start: Training
-2016-09-06 08:18:33,410 DEBUG: Info: Time for Training: 0.0963981151581[s]
-2016-09-06 08:18:33,410 DEBUG: Done: Training
-2016-09-06 08:18:33,410 DEBUG: Start: Predicting
-2016-09-06 08:18:33,413 DEBUG: Done: Predicting
-2016-09-06 08:18:33,413 DEBUG: Start: Getting Results
-2016-09-06 08:18:33,415 DEBUG: Done: Getting Results
-2016-09-06 08:18:33,415 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,415 INFO: Done: Result Analysis
-2016-09-06 08:18:33,566 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,566 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:33,567 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:18:33,567 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:18:33,567 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:33,567 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:33,568 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:33,568 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:33,568 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:33,568 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:33,568 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:33,568 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:33,568 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,568 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:33,621 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:33,622 DEBUG: Start: Training
-2016-09-06 08:18:33,622 DEBUG: Info: Time for Training: 0.0565719604492[s]
-2016-09-06 08:18:33,622 DEBUG: Done: Training
-2016-09-06 08:18:33,622 DEBUG: Start: Predicting
-2016-09-06 08:18:33,630 DEBUG: Done: Predicting
-2016-09-06 08:18:33,630 DEBUG: Start: Getting Results
-2016-09-06 08:18:33,631 DEBUG: Done: Getting Results
-2016-09-06 08:18:33,631 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 36
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,631 INFO: Done: Result Analysis
-2016-09-06 08:18:33,865 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:33,865 DEBUG: Start: Training
-2016-09-06 08:18:33,896 DEBUG: Info: Time for Training: 0.330600976944[s]
-2016-09-06 08:18:33,896 DEBUG: Done: Training
-2016-09-06 08:18:33,896 DEBUG: Start: Predicting
-2016-09-06 08:18:33,901 DEBUG: Done: Predicting
-2016-09-06 08:18:33,901 DEBUG: Start: Getting Results
-2016-09-06 08:18:33,902 DEBUG: Done: Getting Results
-2016-09-06 08:18:33,902 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.942857142857
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 12, max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.942857142857
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:33,902 INFO: Done: Result Analysis
-2016-09-06 08:18:34,022 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,022 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,022 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:18:34,022 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:18:34,023 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:34,023 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:34,024 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:34,024 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:34,024 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:34,024 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:34,024 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:34,024 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:34,024 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,024 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,140 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:34,141 DEBUG: Start: Training
-2016-09-06 08:18:34,142 DEBUG: Info: Time for Training: 0.120800018311[s]
-2016-09-06 08:18:34,142 DEBUG: Done: Training
-2016-09-06 08:18:34,142 DEBUG: Start: Predicting
-2016-09-06 08:18:34,153 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:34,153 DEBUG: Start: Training
-2016-09-06 08:18:34,154 DEBUG: Done: Predicting
-2016-09-06 08:18:34,154 DEBUG: Start: Getting Results
-2016-09-06 08:18:34,156 DEBUG: Done: Getting Results
-2016-09-06 08:18:34,156 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,156 INFO: Done: Result Analysis
-2016-09-06 08:18:34,178 DEBUG: Info: Time for Training: 0.157228946686[s]
-2016-09-06 08:18:34,178 DEBUG: Done: Training
-2016-09-06 08:18:34,178 DEBUG: Start: Predicting
-2016-09-06 08:18:34,182 DEBUG: Done: Predicting
-2016-09-06 08:18:34,182 DEBUG: Start: Getting Results
-2016-09-06 08:18:34,183 DEBUG: Done: Getting Results
-2016-09-06 08:18:34,183 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,183 INFO: Done: Result Analysis
-2016-09-06 08:18:34,272 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,272 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:18:34,272 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,272 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:34,273 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:18:34,273 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:34,273 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:34,273 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:34,273 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 08:18:34,274 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:34,274 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 08:18:34,274 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,274 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:34,274 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,355 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:34,355 DEBUG: Start: Training
-2016-09-06 08:18:34,373 DEBUG: Info: Time for Training: 0.100815057755[s]
-2016-09-06 08:18:34,373 DEBUG: Done: Training
-2016-09-06 08:18:34,373 DEBUG: Start: Predicting
-2016-09-06 08:18:34,374 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:34,374 DEBUG: Start: Training
-2016-09-06 08:18:34,378 DEBUG: Done: Predicting
-2016-09-06 08:18:34,379 DEBUG: Start: Getting Results
-2016-09-06 08:18:34,380 DEBUG: Done: Getting Results
-2016-09-06 08:18:34,380 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,380 INFO: Done: Result Analysis
-2016-09-06 08:18:34,392 DEBUG: Info: Time for Training: 0.12112402916[s]
-2016-09-06 08:18:34,393 DEBUG: Done: Training
-2016-09-06 08:18:34,393 DEBUG: Start: Predicting
-2016-09-06 08:18:34,397 DEBUG: Done: Predicting
-2016-09-06 08:18:34,397 DEBUG: Start: Getting Results
-2016-09-06 08:18:34,398 DEBUG: Done: Getting Results
-2016-09-06 08:18:34,398 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,399 INFO: Done: Result Analysis
-2016-09-06 08:18:34,526 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,527 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:18:34,527 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:34,528 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:34,528 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:34,528 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:34,528 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,528 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,528 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:18:34,529 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:34,529 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:34,530 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:34,530 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:34,530 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,591 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:34,591 DEBUG: Start: Training
-2016-09-06 08:18:34,593 DEBUG: Info: Time for Training: 0.0652930736542[s]
-2016-09-06 08:18:34,593 DEBUG: Done: Training
-2016-09-06 08:18:34,593 DEBUG: Start: Predicting
-2016-09-06 08:18:34,595 DEBUG: Done: Predicting
-2016-09-06 08:18:34,596 DEBUG: Start: Getting Results
-2016-09-06 08:18:34,597 DEBUG: Done: Getting Results
-2016-09-06 08:18:34,597 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,597 INFO: Done: Result Analysis
-2016-09-06 08:18:34,614 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:34,614 DEBUG: Start: Training
-2016-09-06 08:18:34,618 DEBUG: Info: Time for Training: 0.0917420387268[s]
-2016-09-06 08:18:34,618 DEBUG: Done: Training
-2016-09-06 08:18:34,618 DEBUG: Start: Predicting
-2016-09-06 08:18:34,621 DEBUG: Done: Predicting
-2016-09-06 08:18:34,621 DEBUG: Start: Getting Results
-2016-09-06 08:18:34,622 DEBUG: Done: Getting Results
-2016-09-06 08:18:34,622 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,623 INFO: Done: Result Analysis
-2016-09-06 08:18:34,780 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,780 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:34,781 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:18:34,781 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:18:34,781 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:34,781 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:34,782 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:34,782 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:34,782 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:34,782 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:34,782 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:34,782 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:34,783 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,783 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:34,863 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:34,864 DEBUG: Start: Training
-2016-09-06 08:18:34,864 DEBUG: Info: Time for Training: 0.0847318172455[s]
-2016-09-06 08:18:34,864 DEBUG: Done: Training
-2016-09-06 08:18:34,864 DEBUG: Start: Predicting
-2016-09-06 08:18:34,871 DEBUG: Done: Predicting
-2016-09-06 08:18:34,872 DEBUG: Start: Getting Results
-2016-09-06 08:18:34,873 DEBUG: Done: Getting Results
-2016-09-06 08:18:34,873 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:18:34,873 INFO: Done: Result Analysis
-2016-09-06 08:18:35,108 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:35,109 DEBUG: Start: Training
-2016-09-06 08:18:35,140 DEBUG: Info: Time for Training: 0.360179901123[s]
-2016-09-06 08:18:35,140 DEBUG: Done: Training
-2016-09-06 08:18:35,140 DEBUG: Start: Predicting
-2016-09-06 08:18:35,145 DEBUG: Done: Predicting
-2016-09-06 08:18:35,145 DEBUG: Start: Getting Results
-2016-09-06 08:18:35,146 DEBUG: Done: Getting Results
-2016-09-06 08:18:35,146 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 12, max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:18:35,146 INFO: Done: Result Analysis
-2016-09-06 08:18:35,224 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:35,224 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:18:35,224 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:18:35,224 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:35,224 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:18:35,224 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:18:35,225 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:35,225 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:35,225 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:35,225 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:35,225 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 08:18:35,225 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 08:18:35,225 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:18:35,226 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:18:35,305 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:35,306 DEBUG: Start: Training
-2016-09-06 08:18:35,306 DEBUG: Info: Time for Training: 0.0829739570618[s]
-2016-09-06 08:18:35,306 DEBUG: Done: Training
-2016-09-06 08:18:35,307 DEBUG: Start: Predicting
-2016-09-06 08:18:35,308 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:18:35,308 DEBUG: Start: Training
-2016-09-06 08:18:35,317 DEBUG: Done: Predicting
-2016-09-06 08:18:35,317 DEBUG: Start: Getting Results
-2016-09-06 08:18:35,319 DEBUG: Done: Getting Results
-2016-09-06 08:18:35,319 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:18:35,319 INFO: Done: Result Analysis
-2016-09-06 08:18:35,334 DEBUG: Info: Time for Training: 0.110707998276[s]
-2016-09-06 08:18:35,334 DEBUG: Done: Training
-2016-09-06 08:18:35,334 DEBUG: Start: Predicting
-2016-09-06 08:18:35,338 DEBUG: Done: Predicting
-2016-09-06 08:18:35,338 DEBUG: Start: Getting Results
-2016-09-06 08:18:35,339 DEBUG: Done: Getting Results
-2016-09-06 08:18:35,339 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:18:35,339 INFO: Done: Result Analysis
-2016-09-06 08:18:35,619 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:35,620 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:18:35,620 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:35,620 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:35,620 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:35,620 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:35,621 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:35,621 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:35,621 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:35,621 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:35,622 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:35,622 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:35,622 INFO: Done: Read Database Files
-2016-09-06 08:18:35,622 INFO: Done: Read Database Files
-2016-09-06 08:18:35,622 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:35,622 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:35,626 INFO: Done: Determine validation split
-2016-09-06 08:18:35,626 INFO: Done: Determine validation split
-2016-09-06 08:18:35,626 INFO: Start: Determine 5 folds
-2016-09-06 08:18:35,626 INFO: Start: Determine 5 folds
-2016-09-06 08:18:35,632 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:35,632 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:35,632 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:35,632 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:35,632 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:35,632 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:35,632 INFO: Done: Determine folds
-2016-09-06 08:18:35,632 INFO: Done: Determine folds
-2016-09-06 08:18:35,632 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:18:35,632 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:18:35,633 INFO: Start: Classification
-2016-09-06 08:18:35,633 INFO: Start: Classification
-2016-09-06 08:18:35,633 INFO: Start: Fold number 1
-2016-09-06 08:18:35,633 INFO: Start: Fold number 1
-2016-09-06 08:18:35,664 DEBUG: Start: Iteration 1
-2016-09-06 08:18:35,672 DEBUG: View 0 : 0.502793296089
-2016-09-06 08:18:35,679 DEBUG: View 1 : 0.575418994413
-2016-09-06 08:18:35,686 DEBUG: View 2 : 0.446927374302
-2016-09-06 08:18:35,694 DEBUG: View 3 : 0.558659217877
-2016-09-06 08:18:35,697 INFO: Start: Classification
-2016-09-06 08:18:35,727 DEBUG: Best view : View2
-2016-09-06 08:18:35,755 INFO: Done: Fold number 1
-2016-09-06 08:18:35,756 INFO: Start: Fold number 2
-2016-09-06 08:18:35,814 INFO: Start: Classification
-2016-09-06 08:18:35,815 DEBUG: Start: Iteration 2
-2016-09-06 08:18:35,822 DEBUG: View 0 : 0.743016759777
-2016-09-06 08:18:35,829 DEBUG: View 1 : 0.698324022346
-2016-09-06 08:18:35,837 DEBUG: View 2 : 0.620111731844
-2016-09-06 08:18:35,845 DEBUG: View 3 : 0.63687150838
-2016-09-06 08:18:35,851 INFO: Done: Fold number 2
-2016-09-06 08:18:35,851 INFO: Start: Fold number 3
-2016-09-06 08:18:35,884 DEBUG: Best view : View0
-2016-09-06 08:18:35,909 INFO: Start: Classification
-2016-09-06 08:18:35,945 INFO: Done: Fold number 3
-2016-09-06 08:18:35,945 INFO: Start: Fold number 4
-2016-09-06 08:18:36,004 INFO: Start: Classification
-2016-09-06 08:18:36,039 INFO: Done: Fold number 4
-2016-09-06 08:18:36,039 INFO: Start: Fold number 5
-2016-09-06 08:18:36,044 DEBUG: Start: Iteration 3
-2016-09-06 08:18:36,056 DEBUG: View 0 : 0.743016759777
-2016-09-06 08:18:36,069 DEBUG: View 1 : 0.698324022346
-2016-09-06 08:18:36,081 DEBUG: View 2 : 0.620111731844
-2016-09-06 08:18:36,091 DEBUG: View 3 : 0.63687150838
-2016-09-06 08:18:36,119 INFO: Start: Classification
-2016-09-06 08:18:36,133 DEBUG: Best view : View0
-2016-09-06 08:18:36,154 INFO: Done: Fold number 5
-2016-09-06 08:18:36,154 INFO: Done: Classification
-2016-09-06 08:18:36,155 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:18:36,155 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:18:36,159 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 96.0592946781
- -On Test : 48.7804878049
- -On Validation : 80.6741573034
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 41
- - Decision Tree with max_depth : 9
- - Random Forest with num_esimators : 12, max_depth : 5
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:36,160 INFO: Done: Result Analysis
-2016-09-06 08:18:36,350 DEBUG: Start: Iteration 4
-2016-09-06 08:18:36,358 DEBUG: View 0 : 0.653631284916
-2016-09-06 08:18:36,365 DEBUG: View 1 : 0.731843575419
-2016-09-06 08:18:36,372 DEBUG: View 2 : 0.569832402235
-2016-09-06 08:18:36,379 DEBUG: View 3 : 0.664804469274
-2016-09-06 08:18:36,422 DEBUG: Best view : View1
-2016-09-06 08:18:36,702 INFO: Start: Classification
-2016-09-06 08:18:37,164 INFO: Done: Fold number 1
-2016-09-06 08:18:37,164 INFO: Start: Fold number 2
-2016-09-06 08:18:37,194 DEBUG: Start: Iteration 1
-2016-09-06 08:18:37,200 DEBUG: View 0 : 0.5
-2016-09-06 08:18:37,207 DEBUG: View 1 : 0.522222222222
-2016-09-06 08:18:37,214 DEBUG: View 2 : 0.533333333333
-2016-09-06 08:18:37,220 DEBUG: View 3 : 0.477777777778
-2016-09-06 08:18:37,251 DEBUG: Best view : View2
-2016-09-06 08:18:37,330 DEBUG: Start: Iteration 2
-2016-09-06 08:18:37,337 DEBUG: View 0 : 0.705555555556
-2016-09-06 08:18:37,344 DEBUG: View 1 : 0.65
-2016-09-06 08:18:37,351 DEBUG: View 2 : 0.738888888889
-2016-09-06 08:18:37,358 DEBUG: View 3 : 0.705555555556
-2016-09-06 08:18:37,396 DEBUG: Best view : View2
-2016-09-06 08:18:37,543 DEBUG: Start: Iteration 3
-2016-09-06 08:18:37,550 DEBUG: View 0 : 0.705555555556
-2016-09-06 08:18:37,557 DEBUG: View 1 : 0.65
-2016-09-06 08:18:37,564 DEBUG: View 2 : 0.738888888889
-2016-09-06 08:18:37,572 DEBUG: View 3 : 0.705555555556
-2016-09-06 08:18:37,612 DEBUG: Best view : View2
-2016-09-06 08:18:37,827 DEBUG: Start: Iteration 4
-2016-09-06 08:18:37,834 DEBUG: View 0 : 0.7
-2016-09-06 08:18:37,841 DEBUG: View 1 : 0.655555555556
-2016-09-06 08:18:37,848 DEBUG: View 2 : 0.683333333333
-2016-09-06 08:18:37,855 DEBUG: View 3 : 0.688888888889
-2016-09-06 08:18:37,898 DEBUG: Best view : View3
-2016-09-06 08:18:38,178 INFO: Start: Classification
-2016-09-06 08:18:38,641 INFO: Done: Fold number 2
-2016-09-06 08:18:38,642 INFO: Start: Fold number 3
-2016-09-06 08:18:38,672 DEBUG: Start: Iteration 1
-2016-09-06 08:18:38,678 DEBUG: View 0 : 0.48087431694
-2016-09-06 08:18:38,685 DEBUG: View 1 : 0.453551912568
-2016-09-06 08:18:38,692 DEBUG: View 2 : 0.546448087432
-2016-09-06 08:18:38,699 DEBUG: View 3 : 0.469945355191
-2016-09-06 08:18:38,731 DEBUG: Best view : View3
-2016-09-06 08:18:38,811 DEBUG: Start: Iteration 2
-2016-09-06 08:18:38,818 DEBUG: View 0 : 0.748633879781
-2016-09-06 08:18:38,826 DEBUG: View 1 : 0.737704918033
-2016-09-06 08:18:38,833 DEBUG: View 2 : 0.715846994536
-2016-09-06 08:18:38,840 DEBUG: View 3 : 0.644808743169
-2016-09-06 08:18:38,879 DEBUG: Best view : View0
-2016-09-06 08:18:39,031 DEBUG: Start: Iteration 3
-2016-09-06 08:18:39,038 DEBUG: View 0 : 0.748633879781
-2016-09-06 08:18:39,046 DEBUG: View 1 : 0.737704918033
-2016-09-06 08:18:39,053 DEBUG: View 2 : 0.715846994536
-2016-09-06 08:18:39,060 DEBUG: View 3 : 0.644808743169
-2016-09-06 08:18:39,101 DEBUG: Best view : View0
-2016-09-06 08:18:39,320 DEBUG: Start: Iteration 4
-2016-09-06 08:18:39,327 DEBUG: View 0 : 0.715846994536
-2016-09-06 08:18:39,335 DEBUG: View 1 : 0.601092896175
-2016-09-06 08:18:39,342 DEBUG: View 2 : 0.628415300546
-2016-09-06 08:18:39,349 DEBUG: View 3 : 0.677595628415
-2016-09-06 08:18:39,393 DEBUG: Best view : View0
-2016-09-06 08:18:39,677 INFO: Start: Classification
-2016-09-06 08:18:40,150 INFO: Done: Fold number 3
-2016-09-06 08:18:40,150 INFO: Start: Fold number 4
-2016-09-06 08:18:40,179 DEBUG: Start: Iteration 1
-2016-09-06 08:18:40,187 DEBUG: View 0 : 0.565934065934
-2016-09-06 08:18:40,193 DEBUG: View 1 : 0.538461538462
-2016-09-06 08:18:40,200 DEBUG: View 2 : 0.510989010989
-2016-09-06 08:18:40,207 DEBUG: View 3 : 0.494505494505
-2016-09-06 08:18:40,239 DEBUG: Best view : View0
-2016-09-06 08:18:40,319 DEBUG: Start: Iteration 2
-2016-09-06 08:18:40,326 DEBUG: View 0 : 0.725274725275
-2016-09-06 08:18:40,334 DEBUG: View 1 : 0.653846153846
-2016-09-06 08:18:40,341 DEBUG: View 2 : 0.697802197802
-2016-09-06 08:18:40,348 DEBUG: View 3 : 0.71978021978
-2016-09-06 08:18:40,386 DEBUG: Best view : View0
-2016-09-06 08:18:40,534 DEBUG: Start: Iteration 3
-2016-09-06 08:18:40,541 DEBUG: View 0 : 0.725274725275
-2016-09-06 08:18:40,548 DEBUG: View 1 : 0.653846153846
-2016-09-06 08:18:40,556 DEBUG: View 2 : 0.697802197802
-2016-09-06 08:18:40,563 DEBUG: View 3 : 0.71978021978
-2016-09-06 08:18:40,604 DEBUG: Best view : View0
-2016-09-06 08:18:40,820 INFO: Start: Classification
-2016-09-06 08:18:41,170 INFO: Done: Fold number 4
-2016-09-06 08:18:41,170 INFO: Start: Fold number 5
-2016-09-06 08:18:41,200 DEBUG: Start: Iteration 1
-2016-09-06 08:18:41,207 DEBUG: View 0 : 0.521739130435
-2016-09-06 08:18:41,214 DEBUG: View 1 : 0.478260869565
-2016-09-06 08:18:41,221 DEBUG: View 2 : 0.521739130435
-2016-09-06 08:18:41,228 DEBUG: View 3 : 0.467391304348
-2016-09-06 08:18:41,260 DEBUG: Best view : View3
-2016-09-06 08:18:41,340 DEBUG: Start: Iteration 2
-2016-09-06 08:18:41,348 DEBUG: View 0 : 0.673913043478
-2016-09-06 08:18:41,356 DEBUG: View 1 : 0.663043478261
-2016-09-06 08:18:41,363 DEBUG: View 2 : 0.635869565217
-2016-09-06 08:18:41,370 DEBUG: View 3 : 0.684782608696
-2016-09-06 08:18:41,409 DEBUG: Best view : View3
-2016-09-06 08:18:41,559 DEBUG: Start: Iteration 3
-2016-09-06 08:18:41,566 DEBUG: View 0 : 0.673913043478
-2016-09-06 08:18:41,573 DEBUG: View 1 : 0.663043478261
-2016-09-06 08:18:41,581 DEBUG: View 2 : 0.635869565217
-2016-09-06 08:18:41,588 DEBUG: View 3 : 0.684782608696
-2016-09-06 08:18:41,630 DEBUG: Best view : View3
-2016-09-06 08:18:41,848 INFO: Start: Classification
-2016-09-06 08:18:42,201 INFO: Done: Fold number 5
-2016-09-06 08:18:42,201 INFO: Done: Classification
-2016-09-06 08:18:42,202 INFO: Info: Time for Classification: 6[s]
-2016-09-06 08:18:42,202 INFO: Start: Result Analysis for Mumbo
-2016-09-06 08:18:44,442 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 72.594545944
- -On Test : 51.2195121951
- -On Validation : 68.7640449438Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 13), View1 of shape (300, 12), View2 of shape (300, 17), View3 of shape (300, 11)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:06 0:00:00
- Total 0:00:19 0:00:02
- So a total classification time of 0:00:06.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.264245810056
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.270391061453
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.225698324022
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.249720670391
- - Percentage of time chosen : 0.0
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.261111111111
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.247777777778
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.269444444444
- - Percentage of time chosen : 0.3
- - On View3 :
- - Mean average Accuracy : 0.257777777778
- - Percentage of time chosen : 0.1
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.269398907104
- - Percentage of time chosen : 0.9
- - On View1 :
- - Mean average Accuracy : 0.253005464481
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.260655737705
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.243715846995
- - Percentage of time chosen : 0.1
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.201648351648
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.184615384615
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.190659340659
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.193406593407
- - Percentage of time chosen : 0.0
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.186956521739
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.180434782609
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.179347826087
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.183695652174
- - Percentage of time chosen : 0.3
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 49.1620111732
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View2
- Fold 2
- Accuracy on train : 47.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View2
- Fold 3
- Accuracy on train : 49.7267759563
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View3
- Fold 4
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
- Fold 5
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View3
- - Iteration 2
- Fold 1
- Accuracy on train : 74.3016759777
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- Fold 2
- Accuracy on train : 73.8888888889
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View2
- Fold 3
- Accuracy on train : 74.8633879781
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- Fold 4
- Accuracy on train : 72.5274725275
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View0
- Fold 5
- Accuracy on train : 68.4782608696
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View3
- - Iteration 3
- Fold 1
- Accuracy on train : 74.3016759777
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- Fold 2
- Accuracy on train : 73.8888888889
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View2
- Fold 3
- Accuracy on train : 74.8633879781
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- Fold 4
- Accuracy on train : 72.5274725275
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View0
- Fold 5
- Accuracy on train : 67.3913043478
- Accuracy on test : 0.0
- Accuracy on validation : 64.0449438202
- Selected View : View3
- - Iteration 4
- Fold 1
- Accuracy on train : 74.3016759777
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View1
- Fold 2
- Accuracy on train : 73.8888888889
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View3
- Fold 3
- Accuracy on train : 74.8633879781
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- Fold 4
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
- Fold 5
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
- - Iteration 5
- Fold 1
- Accuracy on train : 49.1620111732
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
- Fold 2
- Accuracy on train : 47.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
- Fold 3
- Accuracy on train : 49.7267759563
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
-2016-09-06 08:18:44,625 INFO: Done: Result Analysis
-2016-09-06 08:18:44,692 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:44,692 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:44,693 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:44,693 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:44,694 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:44,694 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:44,695 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:44,695 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:44,695 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:44,695 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:44,695 INFO: Done: Read Database Files
-2016-09-06 08:18:44,695 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:44,696 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:44,696 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:44,696 INFO: Done: Read Database Files
-2016-09-06 08:18:44,697 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:44,699 INFO: Done: Determine validation split
-2016-09-06 08:18:44,700 INFO: Start: Determine 5 folds
-2016-09-06 08:18:44,701 INFO: Done: Determine validation split
-2016-09-06 08:18:44,701 INFO: Start: Determine 5 folds
-2016-09-06 08:18:44,707 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:44,707 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:44,707 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:44,707 INFO: Done: Determine folds
-2016-09-06 08:18:44,708 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:18:44,708 INFO: Start: Classification
-2016-09-06 08:18:44,708 INFO: Start: Fold number 1
-2016-09-06 08:18:44,708 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:44,708 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:44,709 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:44,709 INFO: Done: Determine folds
-2016-09-06 08:18:44,709 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:18:44,709 INFO: Start: Classification
-2016-09-06 08:18:44,709 INFO: Start: Fold number 1
-2016-09-06 08:18:44,771 INFO: Start: Classification
-2016-09-06 08:18:44,797 INFO: Start: Classification
-2016-09-06 08:18:44,835 INFO: Done: Fold number 1
-2016-09-06 08:18:44,835 INFO: Start: Fold number 2
-2016-09-06 08:18:44,855 INFO: Done: Fold number 1
-2016-09-06 08:18:44,855 INFO: Start: Fold number 2
-2016-09-06 08:18:44,914 INFO: Start: Classification
-2016-09-06 08:18:44,916 INFO: Start: Classification
-2016-09-06 08:18:44,952 INFO: Done: Fold number 2
-2016-09-06 08:18:44,952 INFO: Start: Fold number 3
-2016-09-06 08:18:44,997 INFO: Done: Fold number 2
-2016-09-06 08:18:44,997 INFO: Start: Fold number 3
-2016-09-06 08:18:45,032 INFO: Start: Classification
-2016-09-06 08:18:45,057 INFO: Start: Classification
-2016-09-06 08:18:45,069 INFO: Done: Fold number 3
-2016-09-06 08:18:45,070 INFO: Start: Fold number 4
-2016-09-06 08:18:45,140 INFO: Done: Fold number 3
-2016-09-06 08:18:45,140 INFO: Start: Fold number 4
-2016-09-06 08:18:45,150 INFO: Start: Classification
-2016-09-06 08:18:45,187 INFO: Done: Fold number 4
-2016-09-06 08:18:45,187 INFO: Start: Fold number 5
-2016-09-06 08:18:45,200 INFO: Start: Classification
-2016-09-06 08:18:45,267 INFO: Start: Classification
-2016-09-06 08:18:45,283 INFO: Done: Fold number 4
-2016-09-06 08:18:45,283 INFO: Start: Fold number 5
-2016-09-06 08:18:45,304 INFO: Done: Fold number 5
-2016-09-06 08:18:45,304 INFO: Done: Classification
-2016-09-06 08:18:45,304 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:18:45,304 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:18:45,309 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 94.6159403923
- -On Test : 47.8048780488
- -On Validation : 83.1460674157
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 41
- - Decision Tree with max_depth : 9
- - Random Forest with num_esimators : 12, max_depth : 5
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:45,309 INFO: Done: Result Analysis
-2016-09-06 08:18:45,341 INFO: Start: Classification
-2016-09-06 08:18:45,420 INFO: Done: Fold number 5
-2016-09-06 08:18:45,420 INFO: Done: Classification
-2016-09-06 08:18:45,420 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:18:45,420 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:18:45,424 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 86.9895666159
- -On Test : 49.2682926829
- -On Validation : 76.404494382
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 41
- - Decision Tree with max_depth : 9
- - Random Forest with num_esimators : 12, max_depth : 5
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:45,424 INFO: Done: Result Analysis
-2016-09-06 08:18:45,544 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:45,545 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:45,545 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:45,545 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:45,546 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:45,546 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:45,546 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:45,547 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:45,547 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:45,547 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:45,547 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:45,547 INFO: Done: Read Database Files
-2016-09-06 08:18:45,548 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:45,548 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:45,548 INFO: Done: Read Database Files
-2016-09-06 08:18:45,548 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:45,552 INFO: Done: Determine validation split
-2016-09-06 08:18:45,552 INFO: Done: Determine validation split
-2016-09-06 08:18:45,552 INFO: Start: Determine 5 folds
-2016-09-06 08:18:45,552 INFO: Start: Determine 5 folds
-2016-09-06 08:18:45,558 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:45,559 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:45,559 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:45,559 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:45,559 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:45,559 INFO: Done: Determine folds
-2016-09-06 08:18:45,559 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:45,559 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:18:45,559 INFO: Done: Determine folds
-2016-09-06 08:18:45,559 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:18:45,559 INFO: Start: Classification
-2016-09-06 08:18:45,559 INFO: Start: Classification
-2016-09-06 08:18:45,559 INFO: Start: Fold number 1
-2016-09-06 08:18:45,559 INFO: Start: Fold number 1
-2016-09-06 08:18:45,585 INFO: Start: Classification
-2016-09-06 08:18:45,610 INFO: Done: Fold number 1
-2016-09-06 08:18:45,611 INFO: Start: Fold number 2
-2016-09-06 08:18:45,623 INFO: Start: Classification
-2016-09-06 08:18:45,634 INFO: Start: Classification
-2016-09-06 08:18:45,659 INFO: Done: Fold number 2
-2016-09-06 08:18:45,659 INFO: Start: Fold number 3
-2016-09-06 08:18:45,680 INFO: Done: Fold number 1
-2016-09-06 08:18:45,681 INFO: Start: Fold number 2
-2016-09-06 08:18:45,682 INFO: Start: Classification
-2016-09-06 08:18:45,709 INFO: Done: Fold number 3
-2016-09-06 08:18:45,709 INFO: Start: Fold number 4
-2016-09-06 08:18:45,732 INFO: Start: Classification
-2016-09-06 08:18:45,748 INFO: Start: Classification
-2016-09-06 08:18:45,758 INFO: Done: Fold number 4
-2016-09-06 08:18:45,758 INFO: Start: Fold number 5
-2016-09-06 08:18:45,780 INFO: Start: Classification
-2016-09-06 08:18:45,795 INFO: Done: Fold number 2
-2016-09-06 08:18:45,795 INFO: Start: Fold number 3
-2016-09-06 08:18:45,806 INFO: Done: Fold number 5
-2016-09-06 08:18:45,806 INFO: Done: Classification
-2016-09-06 08:18:45,806 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:18:45,806 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:18:45,810 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 49.756097561
- -On Validation : 85.8426966292
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:45,811 INFO: Done: Result Analysis
-2016-09-06 08:18:45,860 INFO: Start: Classification
-2016-09-06 08:18:45,902 INFO: Done: Fold number 3
-2016-09-06 08:18:45,902 INFO: Start: Fold number 4
-2016-09-06 08:18:45,964 INFO: Start: Classification
-2016-09-06 08:18:46,007 INFO: Done: Fold number 4
-2016-09-06 08:18:46,007 INFO: Start: Fold number 5
-2016-09-06 08:18:46,068 INFO: Start: Classification
-2016-09-06 08:18:46,111 INFO: Done: Fold number 5
-2016-09-06 08:18:46,111 INFO: Done: Classification
-2016-09-06 08:18:46,111 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:18:46,111 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:18:46,116 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 49.4786031123
- -On Test : 46.8292682927
- -On Validation : 47.6404494382
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 41
- - Decision Tree with max_depth : 9
- - Random Forest with num_esimators : 12, max_depth : 5
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:46,116 INFO: Done: Result Analysis
-2016-09-06 08:18:46,193 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:46,193 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:46,193 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:46,193 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:46,194 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:46,194 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:46,194 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:46,194 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:46,195 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:46,195 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:46,195 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:46,195 INFO: Done: Read Database Files
-2016-09-06 08:18:46,195 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:46,196 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:46,196 INFO: Done: Read Database Files
-2016-09-06 08:18:46,196 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:46,199 INFO: Done: Determine validation split
-2016-09-06 08:18:46,200 INFO: Start: Determine 5 folds
-2016-09-06 08:18:46,201 INFO: Done: Determine validation split
-2016-09-06 08:18:46,201 INFO: Start: Determine 5 folds
-2016-09-06 08:18:46,206 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:46,206 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:46,206 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:46,206 INFO: Done: Determine folds
-2016-09-06 08:18:46,206 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:18:46,206 INFO: Start: Classification
-2016-09-06 08:18:46,207 INFO: Start: Fold number 1
-2016-09-06 08:18:46,209 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:46,210 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:46,210 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:46,210 INFO: Done: Determine folds
-2016-09-06 08:18:46,210 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:18:46,210 INFO: Start: Classification
-2016-09-06 08:18:46,210 INFO: Start: Fold number 1
-2016-09-06 08:18:46,225 INFO: Start: Classification
-2016-09-06 08:18:46,231 INFO: Start: Classification
-2016-09-06 08:18:46,249 INFO: Done: Fold number 1
-2016-09-06 08:18:46,249 INFO: Start: Fold number 2
-2016-09-06 08:18:46,262 INFO: Done: Fold number 1
-2016-09-06 08:18:46,262 INFO: Start: Fold number 2
-2016-09-06 08:18:46,267 INFO: Start: Classification
-2016-09-06 08:18:46,282 INFO: Start: Classification
-2016-09-06 08:18:46,292 INFO: Done: Fold number 2
-2016-09-06 08:18:46,292 INFO: Start: Fold number 3
-2016-09-06 08:18:46,310 INFO: Start: Classification
-2016-09-06 08:18:46,315 INFO: Done: Fold number 2
-2016-09-06 08:18:46,315 INFO: Start: Fold number 3
-2016-09-06 08:18:46,335 INFO: Done: Fold number 3
-2016-09-06 08:18:46,335 INFO: Start: Fold number 4
-2016-09-06 08:18:46,335 INFO: Start: Classification
-2016-09-06 08:18:46,353 INFO: Start: Classification
-2016-09-06 08:18:46,368 INFO: Done: Fold number 3
-2016-09-06 08:18:46,369 INFO: Start: Fold number 4
-2016-09-06 08:18:46,378 INFO: Done: Fold number 4
-2016-09-06 08:18:46,378 INFO: Start: Fold number 5
-2016-09-06 08:18:46,388 INFO: Start: Classification
-2016-09-06 08:18:46,395 INFO: Start: Classification
-2016-09-06 08:18:46,420 INFO: Done: Fold number 5
-2016-09-06 08:18:46,420 INFO: Done: Classification
-2016-09-06 08:18:46,420 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:18:46,420 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:18:46,420 INFO: Done: Fold number 4
-2016-09-06 08:18:46,420 INFO: Start: Fold number 5
-2016-09-06 08:18:46,425 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 59.5604395604
- -On Test : 52.1951219512
- -On Validation : 61.1235955056
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Decision Tree with max_depth : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:46,425 INFO: Done: Result Analysis
-2016-09-06 08:18:46,438 INFO: Start: Classification
-2016-09-06 08:18:46,468 INFO: Done: Fold number 5
-2016-09-06 08:18:46,468 INFO: Done: Classification
-2016-09-06 08:18:46,468 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:18:46,469 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:18:46,474 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 42.9268292683
- -On Validation : 82.9213483146
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - K nearest Neighbors with n_neighbors: 1.0
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:46,474 INFO: Done: Result Analysis
-2016-09-06 08:18:46,543 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:46,543 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:46,543 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:46,543 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:46,544 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:46,544 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:46,545 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:46,545 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:46,545 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:46,545 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:46,546 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:46,546 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:46,546 INFO: Done: Read Database Files
-2016-09-06 08:18:46,546 INFO: Done: Read Database Files
-2016-09-06 08:18:46,546 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:46,546 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:46,554 INFO: Done: Determine validation split
-2016-09-06 08:18:46,554 INFO: Done: Determine validation split
-2016-09-06 08:18:46,554 INFO: Start: Determine 5 folds
-2016-09-06 08:18:46,554 INFO: Start: Determine 5 folds
-2016-09-06 08:18:46,564 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:46,565 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:46,565 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:46,565 INFO: Done: Determine folds
-2016-09-06 08:18:46,565 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:46,565 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:18:46,565 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:46,565 INFO: Start: Classification
-2016-09-06 08:18:46,565 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:46,565 INFO: Start: Fold number 1
-2016-09-06 08:18:46,565 INFO: Done: Determine folds
-2016-09-06 08:18:46,566 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:18:46,566 INFO: Start: Classification
-2016-09-06 08:18:46,566 INFO: Start: Fold number 1
-2016-09-06 08:18:46,595 INFO: Start: Classification
-2016-09-06 08:18:46,669 INFO: Done: Fold number 1
-2016-09-06 08:18:46,669 INFO: Start: Fold number 2
-2016-09-06 08:18:46,677 INFO: Start: Classification
-2016-09-06 08:18:46,687 INFO: Start: Classification
-2016-09-06 08:18:46,718 INFO: Done: Fold number 1
-2016-09-06 08:18:46,718 INFO: Start: Fold number 2
-2016-09-06 08:18:46,718 INFO: Done: Fold number 2
-2016-09-06 08:18:46,718 INFO: Start: Fold number 3
-2016-09-06 08:18:46,742 INFO: Start: Classification
-2016-09-06 08:18:46,779 INFO: Done: Fold number 3
-2016-09-06 08:18:46,779 INFO: Start: Fold number 4
-2016-09-06 08:18:46,801 INFO: Start: Classification
-2016-09-06 08:18:46,820 INFO: Start: Classification
-2016-09-06 08:18:46,830 INFO: Done: Fold number 4
-2016-09-06 08:18:46,831 INFO: Start: Fold number 5
-2016-09-06 08:18:46,851 INFO: Start: Classification
-2016-09-06 08:18:46,862 INFO: Done: Fold number 2
-2016-09-06 08:18:46,862 INFO: Start: Fold number 3
-2016-09-06 08:18:46,879 INFO: Done: Fold number 5
-2016-09-06 08:18:46,880 INFO: Done: Classification
-2016-09-06 08:18:46,880 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:18:46,880 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:18:46,886 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 71.312383569
- -On Test : 51.2195121951
- -On Validation : 67.191011236
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:46,886 INFO: Done: Result Analysis
-2016-09-06 08:18:46,970 INFO: Start: Classification
-2016-09-06 08:18:47,009 INFO: Done: Fold number 3
-2016-09-06 08:18:47,010 INFO: Start: Fold number 4
-2016-09-06 08:18:47,113 INFO: Start: Classification
-2016-09-06 08:18:47,152 INFO: Done: Fold number 4
-2016-09-06 08:18:47,152 INFO: Start: Fold number 5
-2016-09-06 08:18:47,256 INFO: Start: Classification
-2016-09-06 08:18:47,291 INFO: Done: Fold number 5
-2016-09-06 08:18:47,291 INFO: Done: Classification
-2016-09-06 08:18:47,291 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:18:47,292 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:18:47,296 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 98.2414484051
- -On Test : 47.8048780488
- -On Validation : 84.9438202247
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:47,296 INFO: Done: Result Analysis
-2016-09-06 08:18:47,389 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:18:47,389 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:18:47,390 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 08:18:47,390 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 08:18:47,391 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 08:18:47,392 INFO: Info: Shape of View3 :(300, 11)
-2016-09-06 08:18:47,392 INFO: Done: Read Database Files
-2016-09-06 08:18:47,392 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:18:47,396 INFO: Done: Determine validation split
-2016-09-06 08:18:47,396 INFO: Start: Determine 5 folds
-2016-09-06 08:18:47,402 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:18:47,403 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:18:47,403 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:18:47,403 INFO: Done: Determine folds
-2016-09-06 08:18:47,403 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:18:47,403 INFO: Start: Classification
-2016-09-06 08:18:47,403 INFO: Start: Fold number 1
-2016-09-06 08:18:47,439 INFO: Start: Classification
-2016-09-06 08:18:47,466 INFO: Done: Fold number 1
-2016-09-06 08:18:47,466 INFO: Start: Fold number 2
-2016-09-06 08:18:47,501 INFO: Start: Classification
-2016-09-06 08:18:47,528 INFO: Done: Fold number 2
-2016-09-06 08:18:47,528 INFO: Start: Fold number 3
-2016-09-06 08:18:47,560 INFO: Start: Classification
-2016-09-06 08:18:47,586 INFO: Done: Fold number 3
-2016-09-06 08:18:47,587 INFO: Start: Fold number 4
-2016-09-06 08:18:47,621 INFO: Start: Classification
-2016-09-06 08:18:47,648 INFO: Done: Fold number 4
-2016-09-06 08:18:47,648 INFO: Start: Fold number 5
-2016-09-06 08:18:47,681 INFO: Start: Classification
-2016-09-06 08:18:47,707 INFO: Done: Fold number 5
-2016-09-06 08:18:47,707 INFO: Done: Classification
-2016-09-06 08:18:47,707 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:18:47,708 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:18:47,712 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 74.6749581183
- -On Test : 49.2682926829
- -On Validation : 64.2696629213
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SVM Linear with C : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:18:47,712 INFO: Done: Result Analysis
-2016-09-06 08:18:47,937 DEBUG: Start: Deleting 2 temporary datasets for multiprocessing
-2016-09-06 08:18:47,958 DEBUG: Start: Deleting datasets for multiprocessing
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9371af0727583ede8dcc05c2d551cb9c206ecbfa..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dd41522c7de68ae06ff1993f9a1057f982c8785e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081830Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ea041e80ac92a2ecc613c395e6ef6d7f6270cd2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4047539edc50b7eb9880210e14c32c8589a27b34..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.919047619048
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 41fd5e1db71f9d55723045886a51126c359bae83..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cd402864fe1d2ba740f68a467908bb08ca5c7620..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081831Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0204aa1d318ca91a4660b9165d9a106f75c2b7bb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f0d54f959717f4c2c60a8ce2a9ae877176f77b6c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0b7ed2ef628fb7211e267f8b5ef01b4b22e19317..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 36
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75c6cd722bccb0841c7af739e56bdb9b556cd5a8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 49756f906905dd975b5942eb571312f5c54c51e4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d1ea6aecb11bc1bec49266c1220f93963d881b33..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dfbdb1e85f3e2a080c8ed0ae16562fe06995a060..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 356494897cedc382dbe5b28eb8b7bc3f7ba7cfe5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081832Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 61a36d649d7c3050b402de8065d50d6b0d895861..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1c6372ec689417522df754386a9e17b783361d4c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5dde24413891d21b8a188cc3ee3f23478c388ab5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 36
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7479ea6bbd40681fd84b80d8e7a34e10a34304a6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.942857142857
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 12, max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.942857142857
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a800add9b5d0e16be38352b95b07eb2e59c3ec7c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fcc37ad3bc570d4182431a945ad60ee005c5a695..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081833Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e7f1ec3e242c4a86e3f59ada9875dfee8cd51477..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fcea7365dc9da7c27a598aea11431f5a31bf1d16..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4d603ad258bab09e5b34f5a51852e1e26b0817cc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 98ac7c2f8aede5406d351d10000f9982d511f17b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e20048310c3b79d28a3177c6ae311e10617691ec..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c06c6d191afe945fe460e784dd83a8d25b3f08c5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 11745c30c09d664d9c9803dd03205bbeba49704c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081834Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5da0e45394d301001884b1dfd5a995e15d67fb7c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 12, max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cb0c06805a17f543e906ea048e9ab435b85e96de..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 341b76a7ec968236a7289ad8fbf50cf6602d523e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081835Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1700
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081836Results-Fusion-LateFusion-BayesianInference-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081836Results-Fusion-LateFusion-BayesianInference-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5a72c7698e9055492f5089544d81bfb81641e28b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081836Results-Fusion-LateFusion-BayesianInference-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 96.0592946781
- -On Test : 48.7804878049
- -On Validation : 80.6741573034
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 41
- - Decision Tree with max_depth : 9
- - Random Forest with num_esimators : 12, max_depth : 5
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index 0bf06dc50ed1bf5a6266c8c9a6248778f2c178c3..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index fc73caf00fc485da63dae159a64f6357aa06d2b8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081844Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,225 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 72.594545944
- -On Test : 51.2195121951
- -On Validation : 68.7640449438Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 13), View1 of shape (300, 12), View2 of shape (300, 17), View3 of shape (300, 11)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:06 0:00:00
- Total 0:00:19 0:00:02
- So a total classification time of 0:00:06.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.264245810056
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.270391061453
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.225698324022
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.249720670391
- - Percentage of time chosen : 0.0
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.261111111111
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.247777777778
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.269444444444
- - Percentage of time chosen : 0.3
- - On View3 :
- - Mean average Accuracy : 0.257777777778
- - Percentage of time chosen : 0.1
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.269398907104
- - Percentage of time chosen : 0.9
- - On View1 :
- - Mean average Accuracy : 0.253005464481
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.260655737705
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.243715846995
- - Percentage of time chosen : 0.1
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.201648351648
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.184615384615
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.190659340659
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.193406593407
- - Percentage of time chosen : 0.0
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.186956521739
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.180434782609
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.179347826087
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.183695652174
- - Percentage of time chosen : 0.3
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 49.1620111732
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View2
- Fold 2
- Accuracy on train : 47.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View2
- Fold 3
- Accuracy on train : 49.7267759563
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View3
- Fold 4
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
- Fold 5
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View3
- - Iteration 2
- Fold 1
- Accuracy on train : 74.3016759777
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- Fold 2
- Accuracy on train : 73.8888888889
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View2
- Fold 3
- Accuracy on train : 74.8633879781
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- Fold 4
- Accuracy on train : 72.5274725275
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View0
- Fold 5
- Accuracy on train : 68.4782608696
- Accuracy on test : 0.0
- Accuracy on validation : 65.1685393258
- Selected View : View3
- - Iteration 3
- Fold 1
- Accuracy on train : 74.3016759777
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- Fold 2
- Accuracy on train : 73.8888888889
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View2
- Fold 3
- Accuracy on train : 74.8633879781
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- Fold 4
- Accuracy on train : 72.5274725275
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View0
- Fold 5
- Accuracy on train : 67.3913043478
- Accuracy on test : 0.0
- Accuracy on validation : 64.0449438202
- Selected View : View3
- - Iteration 4
- Fold 1
- Accuracy on train : 74.3016759777
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View1
- Fold 2
- Accuracy on train : 73.8888888889
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View3
- Fold 3
- Accuracy on train : 74.8633879781
- Accuracy on test : 0.0
- Accuracy on validation : 68.5393258427
- Selected View : View0
- Fold 4
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
- Fold 5
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
- - Iteration 5
- Fold 1
- Accuracy on train : 49.1620111732
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
- Fold 2
- Accuracy on train : 47.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
- Fold 3
- Accuracy on train : 49.7267759563
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index db765822794402828adb5866a7adb1cf45e8db64..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 49.756097561
- -On Validation : 85.8426966292
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-MajorityVoting-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-MajorityVoting-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3dcda106026edd8ef46b97bfb3baeea3148694db..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-MajorityVoting-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 86.9895666159
- -On Test : 49.2682926829
- -On Validation : 76.404494382
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 41
- - Decision Tree with max_depth : 9
- - Random Forest with num_esimators : 12, max_depth : 5
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-SVMForLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-SVMForLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 86dffb90376579fec6e9a79d97878b2250231c78..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081845Results-Fusion-LateFusion-SVMForLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 94.6159403923
- -On Test : 47.8048780488
- -On Validation : 83.1460674157
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 41
- - Decision Tree with max_depth : 9
- - Random Forest with num_esimators : 12, max_depth : 5
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 883fdbdcbe0cb8e794ebbdc24c8e6a230d163d83..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 59.5604395604
- -On Test : 52.1951219512
- -On Validation : 61.1235955056
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Decision Tree with max_depth : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index bd75d96fcd0234d15fae4480810f50064bb46c70..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 42.9268292683
- -On Validation : 82.9213483146
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - K nearest Neighbors with n_neighbors: 1.0
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 208bafc5e952a31eebd5d1986e230abd16036f10..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 71.312383569
- -On Test : 51.2195121951
- -On Validation : 67.191011236
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-LateFusion-WeightedLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-LateFusion-WeightedLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 4eda1dc2016f2078620e19f9dbb5bd79c034b826..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081846Results-Fusion-LateFusion-WeightedLinear-KNN-DecisionTree-RandomForest-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 49.4786031123
- -On Test : 46.8292682927
- -On Validation : 47.6404494382
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 41
- - Decision Tree with max_depth : 9
- - Random Forest with num_esimators : 12, max_depth : 5
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 1add7ec9b8d3e515688f405da7d60e9cd8e71ed7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 98.2414484051
- -On Test : 47.8048780488
- -On Validation : 84.9438202247
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3a40e1e0be49268a76c24a0e2a3c1dd93cdc9b7c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081847Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 74.6749581183
- -On Test : 49.2682926829
- -On Validation : 64.2696629213
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SVM Linear with C : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-081940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 3bcfb4558bb1164044e58afc00b61c21b527df44..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081940-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2265 +0,0 @@
-2016-09-06 08:19:40,653 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:19:40,653 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00015446875 Gbytes /!\
-2016-09-06 08:19:45,665 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:19:45,667 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:19:45,719 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:45,719 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:45,719 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:19:45,719 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:19:45,719 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:45,719 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:45,720 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:45,720 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:45,720 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:45,720 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:45,720 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:45,720 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:45,720 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:45,720 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:45,773 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:45,774 DEBUG: Start: Training
-2016-09-06 08:19:45,775 DEBUG: Info: Time for Training: 0.0563549995422[s]
-2016-09-06 08:19:45,775 DEBUG: Done: Training
-2016-09-06 08:19:45,775 DEBUG: Start: Predicting
-2016-09-06 08:19:45,777 DEBUG: Done: Predicting
-2016-09-06 08:19:45,777 DEBUG: Start: Getting Results
-2016-09-06 08:19:45,778 DEBUG: Done: Getting Results
-2016-09-06 08:19:45,779 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.847619047619
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.847619047619
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:19:45,779 INFO: Done: Result Analysis
-2016-09-06 08:19:45,809 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:45,809 DEBUG: Start: Training
-2016-09-06 08:19:45,812 DEBUG: Info: Time for Training: 0.093691110611[s]
-2016-09-06 08:19:45,812 DEBUG: Done: Training
-2016-09-06 08:19:45,812 DEBUG: Start: Predicting
-2016-09-06 08:19:45,816 DEBUG: Done: Predicting
-2016-09-06 08:19:45,816 DEBUG: Start: Getting Results
-2016-09-06 08:19:45,818 DEBUG: Done: Getting Results
-2016-09-06 08:19:45,818 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:19:45,818 INFO: Done: Result Analysis
-2016-09-06 08:19:45,972 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:45,972 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:19:45,972 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:45,973 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:45,973 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:45,973 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:45,974 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:45,974 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:45,974 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:19:45,974 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:45,975 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:45,976 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:45,976 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:45,976 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:46,026 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:46,026 DEBUG: Start: Training
-2016-09-06 08:19:46,027 DEBUG: Info: Time for Training: 0.0553469657898[s]
-2016-09-06 08:19:46,027 DEBUG: Done: Training
-2016-09-06 08:19:46,027 DEBUG: Start: Predicting
-2016-09-06 08:19:46,032 DEBUG: Done: Predicting
-2016-09-06 08:19:46,032 DEBUG: Start: Getting Results
-2016-09-06 08:19:46,033 DEBUG: Done: Getting Results
-2016-09-06 08:19:46,033 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:19:46,033 INFO: Done: Result Analysis
-2016-09-06 08:19:46,585 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:46,585 DEBUG: Start: Training
-2016-09-06 08:19:46,655 DEBUG: Info: Time for Training: 0.682506084442[s]
-2016-09-06 08:19:46,656 DEBUG: Done: Training
-2016-09-06 08:19:46,656 DEBUG: Start: Predicting
-2016-09-06 08:19:46,664 DEBUG: Done: Predicting
-2016-09-06 08:19:46,664 DEBUG: Start: Getting Results
-2016-09-06 08:19:46,665 DEBUG: Done: Getting Results
-2016-09-06 08:19:46,665 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 25, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:19:46,676 INFO: Done: Result Analysis
-2016-09-06 08:19:46,823 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:46,823 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:46,824 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:19:46,824 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:19:46,824 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:46,824 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:46,824 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:46,824 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:46,825 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:46,825 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:46,825 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:46,825 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:46,825 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:46,825 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:46,908 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:46,908 DEBUG: Start: Training
-2016-09-06 08:19:46,909 DEBUG: Info: Time for Training: 0.0860087871552[s]
-2016-09-06 08:19:46,909 DEBUG: Done: Training
-2016-09-06 08:19:46,909 DEBUG: Start: Predicting
-2016-09-06 08:19:46,916 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:46,916 DEBUG: Start: Training
-2016-09-06 08:19:46,929 DEBUG: Done: Predicting
-2016-09-06 08:19:46,929 DEBUG: Start: Getting Results
-2016-09-06 08:19:46,930 DEBUG: Done: Getting Results
-2016-09-06 08:19:46,930 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:19:46,931 INFO: Done: Result Analysis
-2016-09-06 08:19:46,935 DEBUG: Info: Time for Training: 0.112031936646[s]
-2016-09-06 08:19:46,935 DEBUG: Done: Training
-2016-09-06 08:19:46,935 DEBUG: Start: Predicting
-2016-09-06 08:19:46,939 DEBUG: Done: Predicting
-2016-09-06 08:19:46,939 DEBUG: Start: Getting Results
-2016-09-06 08:19:46,940 DEBUG: Done: Getting Results
-2016-09-06 08:19:46,940 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:19:46,941 INFO: Done: Result Analysis
-2016-09-06 08:19:47,070 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,071 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:19:47,071 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:47,071 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,071 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:19:47,071 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:47,071 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:47,071 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:47,072 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:47,072 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,072 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 08:19:47,072 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 08:19:47,073 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:47,073 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,150 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:47,150 DEBUG: Start: Training
-2016-09-06 08:19:47,166 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:47,166 DEBUG: Start: Training
-2016-09-06 08:19:47,167 DEBUG: Info: Time for Training: 0.0974969863892[s]
-2016-09-06 08:19:47,167 DEBUG: Done: Training
-2016-09-06 08:19:47,168 DEBUG: Start: Predicting
-2016-09-06 08:19:47,173 DEBUG: Done: Predicting
-2016-09-06 08:19:47,173 DEBUG: Start: Getting Results
-2016-09-06 08:19:47,174 DEBUG: Done: Getting Results
-2016-09-06 08:19:47,174 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:19:47,174 INFO: Done: Result Analysis
-2016-09-06 08:19:47,186 DEBUG: Info: Time for Training: 0.115417003632[s]
-2016-09-06 08:19:47,186 DEBUG: Done: Training
-2016-09-06 08:19:47,186 DEBUG: Start: Predicting
-2016-09-06 08:19:47,189 DEBUG: Done: Predicting
-2016-09-06 08:19:47,189 DEBUG: Start: Getting Results
-2016-09-06 08:19:47,190 DEBUG: Done: Getting Results
-2016-09-06 08:19:47,191 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:19:47,191 INFO: Done: Result Analysis
-2016-09-06 08:19:47,321 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,321 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,322 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:19:47,322 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:19:47,322 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:47,322 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:47,323 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:47,323 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:47,323 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:47,323 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:47,323 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:47,323 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:47,323 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,323 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,415 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:47,415 DEBUG: Start: Training
-2016-09-06 08:19:47,418 DEBUG: Info: Time for Training: 0.0981721878052[s]
-2016-09-06 08:19:47,418 DEBUG: Done: Training
-2016-09-06 08:19:47,418 DEBUG: Start: Predicting
-2016-09-06 08:19:47,422 DEBUG: Done: Predicting
-2016-09-06 08:19:47,422 DEBUG: Start: Getting Results
-2016-09-06 08:19:47,424 DEBUG: Done: Getting Results
-2016-09-06 08:19:47,424 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.790476190476
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.790476190476
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:19:47,424 INFO: Done: Result Analysis
-2016-09-06 08:19:47,447 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:47,447 DEBUG: Start: Training
-2016-09-06 08:19:47,451 DEBUG: Info: Time for Training: 0.131391048431[s]
-2016-09-06 08:19:47,451 DEBUG: Done: Training
-2016-09-06 08:19:47,452 DEBUG: Start: Predicting
-2016-09-06 08:19:47,454 DEBUG: Done: Predicting
-2016-09-06 08:19:47,455 DEBUG: Start: Getting Results
-2016-09-06 08:19:47,456 DEBUG: Done: Getting Results
-2016-09-06 08:19:47,456 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:19:47,457 INFO: Done: Result Analysis
-2016-09-06 08:19:47,569 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,569 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:47,569 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:19:47,569 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:19:47,569 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:47,569 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:47,571 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:47,571 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:47,571 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:47,571 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:47,571 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:47,571 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:47,571 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,571 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:47,655 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:47,655 DEBUG: Start: Training
-2016-09-06 08:19:47,656 DEBUG: Info: Time for Training: 0.0877361297607[s]
-2016-09-06 08:19:47,656 DEBUG: Done: Training
-2016-09-06 08:19:47,656 DEBUG: Start: Predicting
-2016-09-06 08:19:47,666 DEBUG: Done: Predicting
-2016-09-06 08:19:47,666 DEBUG: Start: Getting Results
-2016-09-06 08:19:47,668 DEBUG: Done: Getting Results
-2016-09-06 08:19:47,668 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 30
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:19:47,668 INFO: Done: Result Analysis
-2016-09-06 08:19:48,180 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:48,180 DEBUG: Start: Training
-2016-09-06 08:19:48,248 DEBUG: Info: Time for Training: 0.679942131042[s]
-2016-09-06 08:19:48,248 DEBUG: Done: Training
-2016-09-06 08:19:48,248 DEBUG: Start: Predicting
-2016-09-06 08:19:48,256 DEBUG: Done: Predicting
-2016-09-06 08:19:48,256 DEBUG: Start: Getting Results
-2016-09-06 08:19:48,257 DEBUG: Done: Getting Results
-2016-09-06 08:19:48,257 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 25, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,257 INFO: Done: Result Analysis
-2016-09-06 08:19:48,317 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,317 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,317 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:19:48,317 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:19:48,318 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:48,318 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:48,319 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,319 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,319 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,319 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,319 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:48,319 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:48,319 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,319 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,435 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:48,435 DEBUG: Start: Training
-2016-09-06 08:19:48,436 DEBUG: Info: Time for Training: 0.119863986969[s]
-2016-09-06 08:19:48,436 DEBUG: Done: Training
-2016-09-06 08:19:48,436 DEBUG: Start: Predicting
-2016-09-06 08:19:48,446 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:48,446 DEBUG: Start: Training
-2016-09-06 08:19:48,450 DEBUG: Done: Predicting
-2016-09-06 08:19:48,450 DEBUG: Start: Getting Results
-2016-09-06 08:19:48,452 DEBUG: Done: Getting Results
-2016-09-06 08:19:48,452 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,452 INFO: Done: Result Analysis
-2016-09-06 08:19:48,473 DEBUG: Info: Time for Training: 0.156780004501[s]
-2016-09-06 08:19:48,473 DEBUG: Done: Training
-2016-09-06 08:19:48,473 DEBUG: Start: Predicting
-2016-09-06 08:19:48,477 DEBUG: Done: Predicting
-2016-09-06 08:19:48,477 DEBUG: Start: Getting Results
-2016-09-06 08:19:48,478 DEBUG: Done: Getting Results
-2016-09-06 08:19:48,478 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,478 INFO: Done: Result Analysis
-2016-09-06 08:19:48,562 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,562 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,563 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:19:48,563 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:19:48,563 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:48,563 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:48,563 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,563 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,563 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,563 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,564 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:48,564 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:48,564 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,564 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,649 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:48,650 DEBUG: Start: Training
-2016-09-06 08:19:48,656 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:48,657 DEBUG: Start: Training
-2016-09-06 08:19:48,669 DEBUG: Info: Time for Training: 0.107176065445[s]
-2016-09-06 08:19:48,669 DEBUG: Done: Training
-2016-09-06 08:19:48,669 DEBUG: Start: Predicting
-2016-09-06 08:19:48,675 DEBUG: Done: Predicting
-2016-09-06 08:19:48,675 DEBUG: Start: Getting Results
-2016-09-06 08:19:48,676 DEBUG: Done: Getting Results
-2016-09-06 08:19:48,677 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,677 INFO: Done: Result Analysis
-2016-09-06 08:19:48,678 DEBUG: Info: Time for Training: 0.116644859314[s]
-2016-09-06 08:19:48,678 DEBUG: Done: Training
-2016-09-06 08:19:48,679 DEBUG: Start: Predicting
-2016-09-06 08:19:48,683 DEBUG: Done: Predicting
-2016-09-06 08:19:48,683 DEBUG: Start: Getting Results
-2016-09-06 08:19:48,684 DEBUG: Done: Getting Results
-2016-09-06 08:19:48,684 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5574
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,684 INFO: Done: Result Analysis
-2016-09-06 08:19:48,808 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,808 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:48,809 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:19:48,809 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:19:48,809 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:48,809 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:48,809 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,809 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:48,809 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,809 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:48,810 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:48,810 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:48,810 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,810 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:48,871 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:48,871 DEBUG: Start: Training
-2016-09-06 08:19:48,873 DEBUG: Info: Time for Training: 0.0652639865875[s]
-2016-09-06 08:19:48,873 DEBUG: Done: Training
-2016-09-06 08:19:48,873 DEBUG: Start: Predicting
-2016-09-06 08:19:48,876 DEBUG: Done: Predicting
-2016-09-06 08:19:48,876 DEBUG: Start: Getting Results
-2016-09-06 08:19:48,877 DEBUG: Done: Getting Results
-2016-09-06 08:19:48,877 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.785714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.785714285714
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,877 INFO: Done: Result Analysis
-2016-09-06 08:19:48,901 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:48,901 DEBUG: Start: Training
-2016-09-06 08:19:48,906 DEBUG: Info: Time for Training: 0.0979499816895[s]
-2016-09-06 08:19:48,906 DEBUG: Done: Training
-2016-09-06 08:19:48,906 DEBUG: Start: Predicting
-2016-09-06 08:19:48,909 DEBUG: Done: Predicting
-2016-09-06 08:19:48,909 DEBUG: Start: Getting Results
-2016-09-06 08:19:48,911 DEBUG: Done: Getting Results
-2016-09-06 08:19:48,911 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:48,911 INFO: Done: Result Analysis
-2016-09-06 08:19:49,054 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:49,054 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:49,054 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:19:49,054 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:19:49,055 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:49,055 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:49,055 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:49,055 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:49,055 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:49,055 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:49,055 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:49,055 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:49,055 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:49,055 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:49,109 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:49,109 DEBUG: Start: Training
-2016-09-06 08:19:49,110 DEBUG: Info: Time for Training: 0.0561349391937[s]
-2016-09-06 08:19:49,110 DEBUG: Done: Training
-2016-09-06 08:19:49,110 DEBUG: Start: Predicting
-2016-09-06 08:19:49,117 DEBUG: Done: Predicting
-2016-09-06 08:19:49,117 DEBUG: Start: Getting Results
-2016-09-06 08:19:49,118 DEBUG: Done: Getting Results
-2016-09-06 08:19:49,118 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 08:19:49,119 INFO: Done: Result Analysis
-2016-09-06 08:19:49,654 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:49,654 DEBUG: Start: Training
-2016-09-06 08:19:49,677 DEBUG: Info: Time for Training: 0.623654842377[s]
-2016-09-06 08:19:49,678 DEBUG: Done: Training
-2016-09-06 08:19:49,678 DEBUG: Start: Predicting
-2016-09-06 08:19:49,684 DEBUG: Done: Predicting
-2016-09-06 08:19:49,684 DEBUG: Start: Getting Results
-2016-09-06 08:19:49,685 DEBUG: Done: Getting Results
-2016-09-06 08:19:49,685 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:19:49,685 INFO: Done: Result Analysis
-2016-09-06 08:19:49,802 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:49,802 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:19:49,803 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:49,803 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:49,803 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:49,803 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:49,804 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:49,804 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:49,804 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:19:49,804 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:49,804 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:49,804 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:49,805 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:49,805 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:49,887 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:49,887 DEBUG: Start: Training
-2016-09-06 08:19:49,888 DEBUG: Info: Time for Training: 0.0861909389496[s]
-2016-09-06 08:19:49,888 DEBUG: Done: Training
-2016-09-06 08:19:49,888 DEBUG: Start: Predicting
-2016-09-06 08:19:49,906 DEBUG: Done: Predicting
-2016-09-06 08:19:49,906 DEBUG: Start: Getting Results
-2016-09-06 08:19:49,907 DEBUG: Done: Getting Results
-2016-09-06 08:19:49,907 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:49,907 INFO: Done: Result Analysis
-2016-09-06 08:19:49,908 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:49,908 DEBUG: Start: Training
-2016-09-06 08:19:49,930 DEBUG: Info: Time for Training: 0.126912117004[s]
-2016-09-06 08:19:49,930 DEBUG: Done: Training
-2016-09-06 08:19:49,930 DEBUG: Start: Predicting
-2016-09-06 08:19:49,934 DEBUG: Done: Predicting
-2016-09-06 08:19:49,934 DEBUG: Start: Getting Results
-2016-09-06 08:19:49,935 DEBUG: Done: Getting Results
-2016-09-06 08:19:49,935 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:19:49,936 INFO: Done: Result Analysis
-2016-09-06 08:19:50,055 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,055 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,055 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:19:50,055 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:19:50,055 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:50,055 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:50,056 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:50,056 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 08:19:50,056 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:50,056 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 08:19:50,056 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:50,056 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:50,056 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,056 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,137 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:50,138 DEBUG: Start: Training
-2016-09-06 08:19:50,144 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:50,144 DEBUG: Start: Training
-2016-09-06 08:19:50,155 DEBUG: Info: Time for Training: 0.100615978241[s]
-2016-09-06 08:19:50,155 DEBUG: Done: Training
-2016-09-06 08:19:50,155 DEBUG: Start: Predicting
-2016-09-06 08:19:50,161 DEBUG: Done: Predicting
-2016-09-06 08:19:50,161 DEBUG: Start: Getting Results
-2016-09-06 08:19:50,162 DEBUG: Done: Getting Results
-2016-09-06 08:19:50,162 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:19:50,163 INFO: Done: Result Analysis
-2016-09-06 08:19:50,164 DEBUG: Info: Time for Training: 0.109777927399[s]
-2016-09-06 08:19:50,164 DEBUG: Done: Training
-2016-09-06 08:19:50,165 DEBUG: Start: Predicting
-2016-09-06 08:19:50,169 DEBUG: Done: Predicting
-2016-09-06 08:19:50,169 DEBUG: Start: Getting Results
-2016-09-06 08:19:50,170 DEBUG: Done: Getting Results
-2016-09-06 08:19:50,170 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5574
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:19:50,170 INFO: Done: Result Analysis
-2016-09-06 08:19:50,299 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,299 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,300 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:19:50,300 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:19:50,300 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:50,300 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:50,301 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:50,301 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:50,301 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:50,301 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:50,301 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:50,301 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:50,301 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,301 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,364 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:50,365 DEBUG: Start: Training
-2016-09-06 08:19:50,367 DEBUG: Info: Time for Training: 0.0675768852234[s]
-2016-09-06 08:19:50,367 DEBUG: Done: Training
-2016-09-06 08:19:50,367 DEBUG: Start: Predicting
-2016-09-06 08:19:50,369 DEBUG: Done: Predicting
-2016-09-06 08:19:50,369 DEBUG: Start: Getting Results
-2016-09-06 08:19:50,370 DEBUG: Done: Getting Results
-2016-09-06 08:19:50,370 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.819047619048
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.819047619048
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:19:50,371 INFO: Done: Result Analysis
-2016-09-06 08:19:50,395 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:50,395 DEBUG: Start: Training
-2016-09-06 08:19:50,400 DEBUG: Info: Time for Training: 0.100778102875[s]
-2016-09-06 08:19:50,400 DEBUG: Done: Training
-2016-09-06 08:19:50,400 DEBUG: Start: Predicting
-2016-09-06 08:19:50,403 DEBUG: Done: Predicting
-2016-09-06 08:19:50,403 DEBUG: Start: Getting Results
-2016-09-06 08:19:50,405 DEBUG: Done: Getting Results
-2016-09-06 08:19:50,405 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:19:50,405 INFO: Done: Result Analysis
-2016-09-06 08:19:50,554 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,554 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:19:50,554 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:50,554 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:50,555 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:19:50,555 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:50,555 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:50,556 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:50,556 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:50,556 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:50,556 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:50,556 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:50,556 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,556 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:50,612 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:50,612 DEBUG: Start: Training
-2016-09-06 08:19:50,612 DEBUG: Info: Time for Training: 0.0590949058533[s]
-2016-09-06 08:19:50,613 DEBUG: Done: Training
-2016-09-06 08:19:50,613 DEBUG: Start: Predicting
-2016-09-06 08:19:50,620 DEBUG: Done: Predicting
-2016-09-06 08:19:50,620 DEBUG: Start: Getting Results
-2016-09-06 08:19:50,621 DEBUG: Done: Getting Results
-2016-09-06 08:19:50,621 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 30
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:19:50,622 INFO: Done: Result Analysis
-2016-09-06 08:19:51,138 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:51,138 DEBUG: Start: Training
-2016-09-06 08:19:51,207 DEBUG: Info: Time for Training: 0.653259992599[s]
-2016-09-06 08:19:51,207 DEBUG: Done: Training
-2016-09-06 08:19:51,207 DEBUG: Start: Predicting
-2016-09-06 08:19:51,215 DEBUG: Done: Predicting
-2016-09-06 08:19:51,215 DEBUG: Start: Getting Results
-2016-09-06 08:19:51,216 DEBUG: Done: Getting Results
-2016-09-06 08:19:51,216 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 25, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:19:51,216 INFO: Done: Result Analysis
-2016-09-06 08:19:51,302 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:51,302 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:19:51,302 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:19:51,302 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:19:51,302 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:51,302 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:19:51,303 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:51,303 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:19:51,303 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:51,303 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:19:51,303 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:51,303 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:19:51,303 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:51,303 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:19:51,397 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:51,397 DEBUG: Start: Training
-2016-09-06 08:19:51,398 DEBUG: Info: Time for Training: 0.0974688529968[s]
-2016-09-06 08:19:51,398 DEBUG: Done: Training
-2016-09-06 08:19:51,399 DEBUG: Start: Predicting
-2016-09-06 08:19:51,415 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:19:51,415 DEBUG: Start: Training
-2016-09-06 08:19:51,417 DEBUG: Done: Predicting
-2016-09-06 08:19:51,418 DEBUG: Start: Getting Results
-2016-09-06 08:19:51,419 DEBUG: Done: Getting Results
-2016-09-06 08:19:51,419 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:51,419 INFO: Done: Result Analysis
-2016-09-06 08:19:51,437 DEBUG: Info: Time for Training: 0.136286020279[s]
-2016-09-06 08:19:51,437 DEBUG: Done: Training
-2016-09-06 08:19:51,437 DEBUG: Start: Predicting
-2016-09-06 08:19:51,442 DEBUG: Done: Predicting
-2016-09-06 08:19:51,442 DEBUG: Start: Getting Results
-2016-09-06 08:19:51,443 DEBUG: Done: Getting Results
-2016-09-06 08:19:51,444 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:19:51,444 INFO: Done: Result Analysis
-2016-09-06 08:19:51,702 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:19:51,703 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:19:51,703 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:19:51,703 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:19:51,703 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:19:51,703 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:19:51,704 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:19:51,704 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:19:51,704 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:19:51,704 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:19:51,705 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:19:51,705 INFO: Done: Read Database Files
-2016-09-06 08:19:51,705 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:19:51,705 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:19:51,705 INFO: Done: Read Database Files
-2016-09-06 08:19:51,706 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:19:51,709 INFO: Done: Determine validation split
-2016-09-06 08:19:51,709 INFO: Start: Determine 5 folds
-2016-09-06 08:19:51,710 INFO: Done: Determine validation split
-2016-09-06 08:19:51,710 INFO: Start: Determine 5 folds
-2016-09-06 08:19:51,716 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:19:51,717 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:19:51,717 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:19:51,717 INFO: Done: Determine folds
-2016-09-06 08:19:51,717 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:19:51,717 INFO: Start: Classification
-2016-09-06 08:19:51,717 INFO: Start: Fold number 1
-2016-09-06 08:19:51,719 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:19:51,720 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:19:51,720 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:19:51,720 INFO: Done: Determine folds
-2016-09-06 08:19:51,720 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:19:51,720 INFO: Start: Classification
-2016-09-06 08:19:51,720 INFO: Start: Fold number 1
-2016-09-06 08:19:51,747 INFO: Start: Classification
-2016-09-06 08:19:51,753 DEBUG: Start: Iteration 1
-2016-09-06 08:19:51,761 DEBUG: View 0 : 0.562162162162
-2016-09-06 08:19:51,769 DEBUG: View 1 : 0.545945945946
-2016-09-06 08:19:51,777 DEBUG: View 2 : 0.491891891892
-2016-09-06 08:19:51,784 DEBUG: View 3 : 0.524324324324
-2016-09-06 08:19:51,784 INFO: Done: Fold number 1
-2016-09-06 08:19:51,784 INFO: Start: Fold number 2
-2016-09-06 08:19:51,816 INFO: Start: Classification
-2016-09-06 08:19:51,818 DEBUG: Best view : View1
-2016-09-06 08:19:51,849 INFO: Done: Fold number 2
-2016-09-06 08:19:51,850 INFO: Start: Fold number 3
-2016-09-06 08:19:51,881 INFO: Start: Classification
-2016-09-06 08:19:51,905 DEBUG: Start: Iteration 2
-2016-09-06 08:19:51,911 INFO: Done: Fold number 3
-2016-09-06 08:19:51,911 INFO: Start: Fold number 4
-2016-09-06 08:19:51,912 DEBUG: View 0 : 0.691891891892
-2016-09-06 08:19:51,920 DEBUG: View 1 : 0.724324324324
-2016-09-06 08:19:51,928 DEBUG: View 2 : 0.643243243243
-2016-09-06 08:19:51,936 DEBUG: View 3 : 0.632432432432
-2016-09-06 08:19:51,941 INFO: Start: Classification
-2016-09-06 08:19:51,972 INFO: Done: Fold number 4
-2016-09-06 08:19:51,972 INFO: Start: Fold number 5
-2016-09-06 08:19:51,975 DEBUG: Best view : View1
-2016-09-06 08:19:52,002 INFO: Start: Classification
-2016-09-06 08:19:52,034 INFO: Done: Fold number 5
-2016-09-06 08:19:52,034 INFO: Done: Classification
-2016-09-06 08:19:52,035 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:19:52,035 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:19:52,039 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 56.7715963199
- -On Test : 55.6097560976
- -On Validation : 55.2808988764
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:19:52,039 INFO: Done: Result Analysis
-2016-09-06 08:19:52,138 DEBUG: Start: Iteration 3
-2016-09-06 08:19:52,153 DEBUG: View 0 : 0.691891891892
-2016-09-06 08:19:52,161 DEBUG: View 1 : 0.724324324324
-2016-09-06 08:19:52,169 DEBUG: View 2 : 0.643243243243
-2016-09-06 08:19:52,176 DEBUG: View 3 : 0.632432432432
-2016-09-06 08:19:52,219 DEBUG: Best view : View1
-2016-09-06 08:19:52,442 DEBUG: Start: Iteration 4
-2016-09-06 08:19:52,449 DEBUG: View 0 : 0.675675675676
-2016-09-06 08:19:52,457 DEBUG: View 1 : 0.627027027027
-2016-09-06 08:19:52,464 DEBUG: View 2 : 0.659459459459
-2016-09-06 08:19:52,472 DEBUG: View 3 : 0.616216216216
-2016-09-06 08:19:52,517 DEBUG: Best view : View0
-2016-09-06 08:19:52,810 INFO: Start: Classification
-2016-09-06 08:19:53,293 INFO: Done: Fold number 1
-2016-09-06 08:19:53,293 INFO: Start: Fold number 2
-2016-09-06 08:19:53,323 DEBUG: Start: Iteration 1
-2016-09-06 08:19:53,332 DEBUG: View 0 : 0.477272727273
-2016-09-06 08:19:53,340 DEBUG: View 1 : 0.585227272727
-2016-09-06 08:19:53,347 DEBUG: View 2 : 0.539772727273
-2016-09-06 08:19:53,355 DEBUG: View 3 : 0.539772727273
-2016-09-06 08:19:53,387 DEBUG: Best view : View0
-2016-09-06 08:19:53,465 DEBUG: Start: Iteration 2
-2016-09-06 08:19:53,472 DEBUG: View 0 : 0.727272727273
-2016-09-06 08:19:53,480 DEBUG: View 1 : 0.744318181818
-2016-09-06 08:19:53,487 DEBUG: View 2 : 0.698863636364
-2016-09-06 08:19:53,494 DEBUG: View 3 : 0.630681818182
-2016-09-06 08:19:53,531 DEBUG: Best view : View1
-2016-09-06 08:19:53,675 DEBUG: Start: Iteration 3
-2016-09-06 08:19:53,682 DEBUG: View 0 : 0.727272727273
-2016-09-06 08:19:53,689 DEBUG: View 1 : 0.744318181818
-2016-09-06 08:19:53,697 DEBUG: View 2 : 0.664772727273
-2016-09-06 08:19:53,704 DEBUG: View 3 : 0.653409090909
-2016-09-06 08:19:53,744 DEBUG: Best view : View1
-2016-09-06 08:19:53,957 DEBUG: Start: Iteration 4
-2016-09-06 08:19:53,964 DEBUG: View 0 : 0.659090909091
-2016-09-06 08:19:53,972 DEBUG: View 1 : 0.727272727273
-2016-09-06 08:19:53,979 DEBUG: View 2 : 0.698863636364
-2016-09-06 08:19:53,986 DEBUG: View 3 : 0.653409090909
-2016-09-06 08:19:54,029 DEBUG: Best view : View1
-2016-09-06 08:19:54,306 INFO: Start: Classification
-2016-09-06 08:19:54,773 INFO: Done: Fold number 2
-2016-09-06 08:19:54,773 INFO: Start: Fold number 3
-2016-09-06 08:19:54,805 DEBUG: Start: Iteration 1
-2016-09-06 08:19:54,812 DEBUG: View 0 : 0.448087431694
-2016-09-06 08:19:54,819 DEBUG: View 1 : 0.448087431694
-2016-09-06 08:19:54,825 DEBUG: View 2 : 0.448087431694
-2016-09-06 08:19:54,833 DEBUG: View 3 : 0.448087431694
-2016-09-06 08:19:54,833 WARNING: WARNING: All bad for iteration 0
-2016-09-06 08:19:54,869 DEBUG: Best view : View0
-2016-09-06 08:19:54,950 DEBUG: Start: Iteration 2
-2016-09-06 08:19:54,957 DEBUG: View 0 : 0.688524590164
-2016-09-06 08:19:54,965 DEBUG: View 1 : 0.75956284153
-2016-09-06 08:19:54,973 DEBUG: View 2 : 0.672131147541
-2016-09-06 08:19:54,981 DEBUG: View 3 : 0.72131147541
-2016-09-06 08:19:55,020 DEBUG: Best view : View1
-2016-09-06 08:19:55,172 DEBUG: Start: Iteration 3
-2016-09-06 08:19:55,179 DEBUG: View 0 : 0.688524590164
-2016-09-06 08:19:55,187 DEBUG: View 1 : 0.75956284153
-2016-09-06 08:19:55,195 DEBUG: View 2 : 0.672131147541
-2016-09-06 08:19:55,203 DEBUG: View 3 : 0.72131147541
-2016-09-06 08:19:55,245 DEBUG: Best view : View1
-2016-09-06 08:19:55,465 DEBUG: Start: Iteration 4
-2016-09-06 08:19:55,473 DEBUG: View 0 : 0.672131147541
-2016-09-06 08:19:55,480 DEBUG: View 1 : 0.765027322404
-2016-09-06 08:19:55,488 DEBUG: View 2 : 0.639344262295
-2016-09-06 08:19:55,495 DEBUG: View 3 : 0.606557377049
-2016-09-06 08:19:55,540 DEBUG: Best view : View1
-2016-09-06 08:19:55,830 INFO: Start: Classification
-2016-09-06 08:19:56,307 INFO: Done: Fold number 3
-2016-09-06 08:19:56,307 INFO: Start: Fold number 4
-2016-09-06 08:19:56,339 DEBUG: Start: Iteration 1
-2016-09-06 08:19:56,345 DEBUG: View 0 : 0.537634408602
-2016-09-06 08:19:56,352 DEBUG: View 1 : 0.537634408602
-2016-09-06 08:19:56,359 DEBUG: View 2 : 0.537634408602
-2016-09-06 08:19:56,365 DEBUG: View 3 : 0.537634408602
-2016-09-06 08:19:56,398 DEBUG: Best view : View0
-2016-09-06 08:19:56,481 DEBUG: Start: Iteration 2
-2016-09-06 08:19:56,488 DEBUG: View 0 : 0.682795698925
-2016-09-06 08:19:56,495 DEBUG: View 1 : 0.720430107527
-2016-09-06 08:19:56,503 DEBUG: View 2 : 0.661290322581
-2016-09-06 08:19:56,511 DEBUG: View 3 : 0.698924731183
-2016-09-06 08:19:56,549 DEBUG: Best view : View1
-2016-09-06 08:19:56,704 DEBUG: Start: Iteration 3
-2016-09-06 08:19:56,711 DEBUG: View 0 : 0.682795698925
-2016-09-06 08:19:56,719 DEBUG: View 1 : 0.720430107527
-2016-09-06 08:19:56,726 DEBUG: View 2 : 0.661290322581
-2016-09-06 08:19:56,734 DEBUG: View 3 : 0.698924731183
-2016-09-06 08:19:56,776 DEBUG: Best view : View1
-2016-09-06 08:19:57,000 DEBUG: Start: Iteration 4
-2016-09-06 08:19:57,008 DEBUG: View 0 : 0.731182795699
-2016-09-06 08:19:57,015 DEBUG: View 1 : 0.677419354839
-2016-09-06 08:19:57,023 DEBUG: View 2 : 0.655913978495
-2016-09-06 08:19:57,031 DEBUG: View 3 : 0.688172043011
-2016-09-06 08:19:57,075 DEBUG: Best view : View0
-2016-09-06 08:19:57,369 INFO: Start: Classification
-2016-09-06 08:19:57,852 INFO: Done: Fold number 4
-2016-09-06 08:19:57,852 INFO: Start: Fold number 5
-2016-09-06 08:19:57,882 DEBUG: Start: Iteration 1
-2016-09-06 08:19:57,889 DEBUG: View 0 : 0.541436464088
-2016-09-06 08:19:57,896 DEBUG: View 1 : 0.541436464088
-2016-09-06 08:19:57,903 DEBUG: View 2 : 0.541436464088
-2016-09-06 08:19:57,910 DEBUG: View 3 : 0.541436464088
-2016-09-06 08:19:57,941 DEBUG: Best view : View0
-2016-09-06 08:19:58,021 DEBUG: Start: Iteration 2
-2016-09-06 08:19:58,029 DEBUG: View 0 : 0.685082872928
-2016-09-06 08:19:58,036 DEBUG: View 1 : 0.696132596685
-2016-09-06 08:19:58,044 DEBUG: View 2 : 0.701657458564
-2016-09-06 08:19:58,052 DEBUG: View 3 : 0.668508287293
-2016-09-06 08:19:58,090 DEBUG: Best view : View2
-2016-09-06 08:19:58,240 DEBUG: Start: Iteration 3
-2016-09-06 08:19:58,247 DEBUG: View 0 : 0.685082872928
-2016-09-06 08:19:58,254 DEBUG: View 1 : 0.696132596685
-2016-09-06 08:19:58,262 DEBUG: View 2 : 0.701657458564
-2016-09-06 08:19:58,269 DEBUG: View 3 : 0.668508287293
-2016-09-06 08:19:58,310 DEBUG: Best view : View2
-2016-09-06 08:19:58,531 DEBUG: Start: Iteration 4
-2016-09-06 08:19:58,538 DEBUG: View 0 : 0.662983425414
-2016-09-06 08:19:58,546 DEBUG: View 1 : 0.707182320442
-2016-09-06 08:19:58,553 DEBUG: View 2 : 0.618784530387
-2016-09-06 08:19:58,561 DEBUG: View 3 : 0.690607734807
-2016-09-06 08:19:58,605 DEBUG: Best view : View1
-2016-09-06 08:19:58,894 INFO: Start: Classification
-2016-09-06 08:19:59,367 INFO: Done: Fold number 5
-2016-09-06 08:19:59,367 INFO: Done: Classification
-2016-09-06 08:19:59,367 INFO: Info: Time for Classification: 7[s]
-2016-09-06 08:19:59,367 INFO: Start: Result Analysis for Mumbo
-2016-09-06 08:20:01,913 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 73.0058582753
- -On Test : 48.7804878049
- -On Validation : 68.0898876404Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 8), View1 of shape (300, 16), View2 of shape (300, 16), View3 of shape (300, 18)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:07 0:00:00
- Total 0:00:20 0:00:02
- So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.262162162162
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.262162162162
- - Percentage of time chosen : 0.3
- - On View2 :
- - Mean average Accuracy : 0.243783783784
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.240540540541
- - Percentage of time chosen : 0.0
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.259090909091
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.280113636364
- - Percentage of time chosen : 0.3
- - On View2 :
- - Mean average Accuracy : 0.260227272727
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.247727272727
- - Percentage of time chosen : 0.0
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.249726775956
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.273224043716
- - Percentage of time chosen : 0.3
- - On View2 :
- - Mean average Accuracy : 0.243169398907
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.249726775956
- - Percentage of time chosen : 0.0
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.263440860215
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.265591397849
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.251612903226
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.262365591398
- - Percentage of time chosen : 0.0
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.257458563536
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.26408839779
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.25635359116
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.256906077348
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 46.4864864865
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View1
- Fold 2
- Accuracy on train : 44.8863636364
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 3
- Accuracy on train : 44.8087431694
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 4
- Accuracy on train : 46.2365591398
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 5
- Accuracy on train : 45.8563535912
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 72.4324324324
- Accuracy on test : 0.0
- Accuracy on validation : 60.6741573034
- Selected View : View1
- Fold 2
- Accuracy on train : 74.4318181818
- Accuracy on test : 0.0
- Accuracy on validation : 70.7865168539
- Selected View : View1
- Fold 3
- Accuracy on train : 75.956284153
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View1
- Fold 4
- Accuracy on train : 72.0430107527
- Accuracy on test : 0.0
- Accuracy on validation : 66.2921348315
- Selected View : View1
- Fold 5
- Accuracy on train : 70.1657458564
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View2
- - Iteration 3
- Fold 1
- Accuracy on train : 72.4324324324
- Accuracy on test : 0.0
- Accuracy on validation : 60.6741573034
- Selected View : View1
- Fold 2
- Accuracy on train : 74.4318181818
- Accuracy on test : 0.0
- Accuracy on validation : 70.7865168539
- Selected View : View1
- Fold 3
- Accuracy on train : 75.956284153
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View1
- Fold 4
- Accuracy on train : 72.0430107527
- Accuracy on test : 0.0
- Accuracy on validation : 66.2921348315
- Selected View : View1
- Fold 5
- Accuracy on train : 70.1657458564
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View2
- - Iteration 4
- Fold 1
- Accuracy on train : 72.4324324324
- Accuracy on test : 0.0
- Accuracy on validation : 60.6741573034
- Selected View : View0
- Fold 2
- Accuracy on train : 74.4318181818
- Accuracy on test : 0.0
- Accuracy on validation : 70.7865168539
- Selected View : View1
- Fold 3
- Accuracy on train : 75.956284153
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View1
- Fold 4
- Accuracy on train : 72.0430107527
- Accuracy on test : 0.0
- Accuracy on validation : 66.2921348315
- Selected View : View0
- Fold 5
- Accuracy on train : 70.1657458564
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View1
- - Iteration 5
- Fold 1
- Accuracy on train : 46.4864864865
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 2
- Accuracy on train : 44.8863636364
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 3
- Accuracy on train : 44.8087431694
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 4
- Accuracy on train : 46.2365591398
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 5
- Accuracy on train : 45.8563535912
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
-2016-09-06 08:20:02,113 INFO: Done: Result Analysis
-2016-09-06 08:20:02,265 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:02,265 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:02,265 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:20:02,266 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:02,266 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:02,266 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:20:02,266 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:20:02,266 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:20:02,267 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:20:02,267 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:20:02,267 INFO: Done: Read Database Files
-2016-09-06 08:20:02,267 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:20:02,267 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:20:02,268 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:20:02,268 INFO: Done: Read Database Files
-2016-09-06 08:20:02,268 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:20:02,271 INFO: Done: Determine validation split
-2016-09-06 08:20:02,271 INFO: Start: Determine 5 folds
-2016-09-06 08:20:02,272 INFO: Done: Determine validation split
-2016-09-06 08:20:02,272 INFO: Start: Determine 5 folds
-2016-09-06 08:20:02,278 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:20:02,278 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:20:02,278 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:20:02,278 INFO: Done: Determine folds
-2016-09-06 08:20:02,278 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:20:02,278 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:20:02,278 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:20:02,278 INFO: Start: Classification
-2016-09-06 08:20:02,278 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:20:02,278 INFO: Start: Fold number 1
-2016-09-06 08:20:02,279 INFO: Done: Determine folds
-2016-09-06 08:20:02,279 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:20:02,279 INFO: Start: Classification
-2016-09-06 08:20:02,279 INFO: Start: Fold number 1
-2016-09-06 08:20:02,310 INFO: Start: Classification
-2016-09-06 08:20:02,337 INFO: Start: Classification
-2016-09-06 08:20:02,368 INFO: Done: Fold number 1
-2016-09-06 08:20:02,368 INFO: Start: Fold number 2
-2016-09-06 08:20:02,390 INFO: Done: Fold number 1
-2016-09-06 08:20:02,391 INFO: Start: Fold number 2
-2016-09-06 08:20:02,415 INFO: Start: Classification
-2016-09-06 08:20:02,419 INFO: Start: Classification
-2016-09-06 08:20:02,446 INFO: Done: Fold number 2
-2016-09-06 08:20:02,446 INFO: Start: Fold number 3
-2016-09-06 08:20:02,492 INFO: Start: Classification
-2016-09-06 08:20:02,493 INFO: Done: Fold number 2
-2016-09-06 08:20:02,493 INFO: Start: Fold number 3
-2016-09-06 08:20:02,520 INFO: Start: Classification
-2016-09-06 08:20:02,522 INFO: Done: Fold number 3
-2016-09-06 08:20:02,522 INFO: Start: Fold number 4
-2016-09-06 08:20:02,567 INFO: Start: Classification
-2016-09-06 08:20:02,593 INFO: Done: Fold number 3
-2016-09-06 08:20:02,593 INFO: Start: Fold number 4
-2016-09-06 08:20:02,597 INFO: Done: Fold number 4
-2016-09-06 08:20:02,597 INFO: Start: Fold number 5
-2016-09-06 08:20:02,621 INFO: Start: Classification
-2016-09-06 08:20:02,643 INFO: Start: Classification
-2016-09-06 08:20:02,675 INFO: Done: Fold number 5
-2016-09-06 08:20:02,675 INFO: Done: Classification
-2016-09-06 08:20:02,675 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:20:02,675 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:20:02,680 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 58.9879948287
- -On Test : 55.1219512195
- -On Validation : 57.3033707865
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:02,680 INFO: Done: Result Analysis
-2016-09-06 08:20:02,694 INFO: Done: Fold number 4
-2016-09-06 08:20:02,694 INFO: Start: Fold number 5
-2016-09-06 08:20:02,720 INFO: Start: Classification
-2016-09-06 08:20:02,792 INFO: Done: Fold number 5
-2016-09-06 08:20:02,792 INFO: Done: Classification
-2016-09-06 08:20:02,792 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:20:02,792 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:20:02,796 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 54.932173067
- -On Test : 55.6097560976
- -On Validation : 57.3033707865
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:02,796 INFO: Done: Result Analysis
-2016-09-06 08:20:02,918 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:02,918 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:02,918 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:02,919 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:02,919 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:20:02,919 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:20:02,920 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:20:02,920 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:20:02,921 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:20:02,921 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:20:02,921 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:20:02,921 INFO: Done: Read Database Files
-2016-09-06 08:20:02,921 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:20:02,922 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:20:02,922 INFO: Done: Read Database Files
-2016-09-06 08:20:02,922 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:20:02,926 INFO: Done: Determine validation split
-2016-09-06 08:20:02,926 INFO: Done: Determine validation split
-2016-09-06 08:20:02,926 INFO: Start: Determine 5 folds
-2016-09-06 08:20:02,926 INFO: Start: Determine 5 folds
-2016-09-06 08:20:02,932 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:20:02,932 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:20:02,932 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:20:02,933 INFO: Done: Determine folds
-2016-09-06 08:20:02,933 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:20:02,933 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:20:02,933 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:20:02,933 INFO: Start: Classification
-2016-09-06 08:20:02,933 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:20:02,933 INFO: Start: Fold number 1
-2016-09-06 08:20:02,933 INFO: Done: Determine folds
-2016-09-06 08:20:02,933 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:20:02,933 INFO: Start: Classification
-2016-09-06 08:20:02,934 INFO: Start: Fold number 1
-2016-09-06 08:20:02,961 INFO: Start: Classification
-2016-09-06 08:20:02,964 INFO: Start: Classification
-2016-09-06 08:20:02,989 INFO: Done: Fold number 1
-2016-09-06 08:20:02,990 INFO: Start: Fold number 2
-2016-09-06 08:20:03,014 INFO: Done: Fold number 1
-2016-09-06 08:20:03,014 INFO: Start: Fold number 2
-2016-09-06 08:20:03,015 INFO: Start: Classification
-2016-09-06 08:20:03,040 INFO: Done: Fold number 2
-2016-09-06 08:20:03,040 INFO: Start: Fold number 3
-2016-09-06 08:20:03,043 INFO: Start: Classification
-2016-09-06 08:20:03,066 INFO: Start: Classification
-2016-09-06 08:20:03,077 INFO: Done: Fold number 2
-2016-09-06 08:20:03,077 INFO: Start: Fold number 3
-2016-09-06 08:20:03,094 INFO: Done: Fold number 3
-2016-09-06 08:20:03,094 INFO: Start: Fold number 4
-2016-09-06 08:20:03,107 INFO: Start: Classification
-2016-09-06 08:20:03,120 INFO: Start: Classification
-2016-09-06 08:20:03,141 INFO: Done: Fold number 3
-2016-09-06 08:20:03,141 INFO: Start: Fold number 4
-2016-09-06 08:20:03,147 INFO: Done: Fold number 4
-2016-09-06 08:20:03,147 INFO: Start: Fold number 5
-2016-09-06 08:20:03,169 INFO: Start: Classification
-2016-09-06 08:20:03,173 INFO: Start: Classification
-2016-09-06 08:20:03,199 INFO: Done: Fold number 5
-2016-09-06 08:20:03,199 INFO: Done: Classification
-2016-09-06 08:20:03,199 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:20:03,199 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:20:03,201 INFO: Done: Fold number 4
-2016-09-06 08:20:03,202 INFO: Start: Fold number 5
-2016-09-06 08:20:03,203 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 54.6341463415
- -On Validation : 84.7191011236
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:03,203 INFO: Done: Result Analysis
-2016-09-06 08:20:03,228 INFO: Start: Classification
-2016-09-06 08:20:03,259 INFO: Done: Fold number 5
-2016-09-06 08:20:03,259 INFO: Done: Classification
-2016-09-06 08:20:03,259 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:20:03,259 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:20:03,264 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 31.9649680433
- -On Test : 36.0975609756
- -On Validation : 31.2359550562
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:03,264 INFO: Done: Result Analysis
-2016-09-06 08:20:03,365 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:03,365 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:03,365 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:03,365 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:20:03,366 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:03,366 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:20:03,366 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:20:03,366 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:20:03,367 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:20:03,367 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:20:03,367 INFO: Done: Read Database Files
-2016-09-06 08:20:03,367 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:20:03,367 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:20:03,368 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:20:03,368 INFO: Done: Read Database Files
-2016-09-06 08:20:03,368 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:20:03,371 INFO: Done: Determine validation split
-2016-09-06 08:20:03,371 INFO: Start: Determine 5 folds
-2016-09-06 08:20:03,371 INFO: Done: Determine validation split
-2016-09-06 08:20:03,372 INFO: Start: Determine 5 folds
-2016-09-06 08:20:03,378 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:20:03,378 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:20:03,378 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:20:03,378 INFO: Done: Determine folds
-2016-09-06 08:20:03,379 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:20:03,379 INFO: Start: Classification
-2016-09-06 08:20:03,379 INFO: Start: Fold number 1
-2016-09-06 08:20:03,380 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:20:03,380 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:20:03,380 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:20:03,380 INFO: Done: Determine folds
-2016-09-06 08:20:03,381 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:20:03,381 INFO: Start: Classification
-2016-09-06 08:20:03,381 INFO: Start: Fold number 1
-2016-09-06 08:20:03,398 INFO: Start: Classification
-2016-09-06 08:20:03,403 INFO: Start: Classification
-2016-09-06 08:20:03,429 INFO: Done: Fold number 1
-2016-09-06 08:20:03,430 INFO: Start: Fold number 2
-2016-09-06 08:20:03,432 INFO: Done: Fold number 1
-2016-09-06 08:20:03,432 INFO: Start: Fold number 2
-2016-09-06 08:20:03,447 INFO: Start: Classification
-2016-09-06 08:20:03,453 INFO: Start: Classification
-2016-09-06 08:20:03,477 INFO: Done: Fold number 2
-2016-09-06 08:20:03,477 INFO: Start: Fold number 3
-2016-09-06 08:20:03,483 INFO: Done: Fold number 2
-2016-09-06 08:20:03,483 INFO: Start: Fold number 3
-2016-09-06 08:20:03,495 INFO: Start: Classification
-2016-09-06 08:20:03,503 INFO: Start: Classification
-2016-09-06 08:20:03,527 INFO: Done: Fold number 3
-2016-09-06 08:20:03,527 INFO: Start: Fold number 4
-2016-09-06 08:20:03,533 INFO: Done: Fold number 3
-2016-09-06 08:20:03,533 INFO: Start: Fold number 4
-2016-09-06 08:20:03,544 INFO: Start: Classification
-2016-09-06 08:20:03,555 INFO: Start: Classification
-2016-09-06 08:20:03,575 INFO: Done: Fold number 4
-2016-09-06 08:20:03,575 INFO: Start: Fold number 5
-2016-09-06 08:20:03,585 INFO: Done: Fold number 4
-2016-09-06 08:20:03,585 INFO: Start: Fold number 5
-2016-09-06 08:20:03,592 INFO: Start: Classification
-2016-09-06 08:20:03,605 INFO: Start: Classification
-2016-09-06 08:20:03,622 INFO: Done: Fold number 5
-2016-09-06 08:20:03,622 INFO: Done: Classification
-2016-09-06 08:20:03,623 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:20:03,623 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:20:03,627 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 52.1951219512
- -On Validation : 81.797752809
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - K nearest Neighbors with n_neighbors: 1.0
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:03,627 INFO: Done: Result Analysis
-2016-09-06 08:20:03,633 INFO: Done: Fold number 5
-2016-09-06 08:20:03,633 INFO: Done: Classification
-2016-09-06 08:20:03,633 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:20:03,633 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:20:03,638 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 59.6237187794
- -On Test : 49.756097561
- -On Validation : 57.5280898876
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Decision Tree with max_depth : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:03,638 INFO: Done: Result Analysis
-2016-09-06 08:20:03,717 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:03,717 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:03,718 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:20:03,718 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:03,718 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:20:03,718 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:03,719 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:20:03,719 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:20:03,719 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:20:03,719 INFO: Done: Read Database Files
-2016-09-06 08:20:03,719 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:20:03,720 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:20:03,721 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:20:03,722 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:20:03,722 INFO: Done: Read Database Files
-2016-09-06 08:20:03,722 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:20:03,724 INFO: Done: Determine validation split
-2016-09-06 08:20:03,724 INFO: Start: Determine 5 folds
-2016-09-06 08:20:03,729 INFO: Done: Determine validation split
-2016-09-06 08:20:03,729 INFO: Start: Determine 5 folds
-2016-09-06 08:20:03,732 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:20:03,732 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:20:03,733 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:20:03,733 INFO: Done: Determine folds
-2016-09-06 08:20:03,733 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:20:03,733 INFO: Start: Classification
-2016-09-06 08:20:03,733 INFO: Start: Fold number 1
-2016-09-06 08:20:03,743 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:20:03,743 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:20:03,743 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:20:03,743 INFO: Done: Determine folds
-2016-09-06 08:20:03,743 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:20:03,744 INFO: Start: Classification
-2016-09-06 08:20:03,744 INFO: Start: Fold number 1
-2016-09-06 08:20:03,756 INFO: Start: Classification
-2016-09-06 08:20:03,805 INFO: Done: Fold number 1
-2016-09-06 08:20:03,805 INFO: Start: Fold number 2
-2016-09-06 08:20:03,835 INFO: Start: Classification
-2016-09-06 08:20:03,866 INFO: Start: Classification
-2016-09-06 08:20:03,906 INFO: Done: Fold number 2
-2016-09-06 08:20:03,906 INFO: Start: Fold number 3
-2016-09-06 08:20:03,921 INFO: Done: Fold number 1
-2016-09-06 08:20:03,921 INFO: Start: Fold number 2
-2016-09-06 08:20:03,941 INFO: Start: Classification
-2016-09-06 08:20:03,991 INFO: Done: Fold number 3
-2016-09-06 08:20:03,992 INFO: Start: Fold number 4
-2016-09-06 08:20:04,026 INFO: Start: Classification
-2016-09-06 08:20:04,074 INFO: Done: Fold number 4
-2016-09-06 08:20:04,074 INFO: Start: Fold number 5
-2016-09-06 08:20:04,081 INFO: Start: Classification
-2016-09-06 08:20:04,106 INFO: Start: Classification
-2016-09-06 08:20:04,139 INFO: Done: Fold number 2
-2016-09-06 08:20:04,140 INFO: Start: Fold number 3
-2016-09-06 08:20:04,154 INFO: Done: Fold number 5
-2016-09-06 08:20:04,155 INFO: Done: Classification
-2016-09-06 08:20:04,155 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:20:04,155 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:20:04,163 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 70.4896592488
- -On Test : 49.756097561
- -On Validation : 67.6404494382
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:04,163 INFO: Done: Result Analysis
-2016-09-06 08:20:04,252 INFO: Start: Classification
-2016-09-06 08:20:04,292 INFO: Done: Fold number 3
-2016-09-06 08:20:04,292 INFO: Start: Fold number 4
-2016-09-06 08:20:04,403 INFO: Start: Classification
-2016-09-06 08:20:04,438 INFO: Done: Fold number 4
-2016-09-06 08:20:04,438 INFO: Start: Fold number 5
-2016-09-06 08:20:04,525 INFO: Start: Classification
-2016-09-06 08:20:04,559 INFO: Done: Fold number 5
-2016-09-06 08:20:04,559 INFO: Done: Classification
-2016-09-06 08:20:04,559 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:20:04,559 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:20:04,564 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 98.9068726244
- -On Test : 50.243902439
- -On Validation : 84.9438202247
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:04,564 INFO: Done: Result Analysis
-2016-09-06 08:20:04,667 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:20:04,668 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:20:04,668 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 08:20:04,669 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 08:20:04,669 INFO: Info: Shape of View2 :(300, 16)
-2016-09-06 08:20:04,670 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 08:20:04,670 INFO: Done: Read Database Files
-2016-09-06 08:20:04,671 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:20:04,676 INFO: Done: Determine validation split
-2016-09-06 08:20:04,676 INFO: Start: Determine 5 folds
-2016-09-06 08:20:04,683 INFO: Info: Length of Learning Sets: 170
-2016-09-06 08:20:04,683 INFO: Info: Length of Testing Sets: 41
-2016-09-06 08:20:04,683 INFO: Info: Length of Validation Set: 89
-2016-09-06 08:20:04,683 INFO: Done: Determine folds
-2016-09-06 08:20:04,684 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:20:04,684 INFO: Start: Classification
-2016-09-06 08:20:04,684 INFO: Start: Fold number 1
-2016-09-06 08:20:04,725 INFO: Start: Classification
-2016-09-06 08:20:04,755 INFO: Done: Fold number 1
-2016-09-06 08:20:04,755 INFO: Start: Fold number 2
-2016-09-06 08:20:04,798 INFO: Start: Classification
-2016-09-06 08:20:04,833 INFO: Done: Fold number 2
-2016-09-06 08:20:04,833 INFO: Start: Fold number 3
-2016-09-06 08:20:04,874 INFO: Start: Classification
-2016-09-06 08:20:04,907 INFO: Done: Fold number 3
-2016-09-06 08:20:04,907 INFO: Start: Fold number 4
-2016-09-06 08:20:04,947 INFO: Start: Classification
-2016-09-06 08:20:04,979 INFO: Done: Fold number 4
-2016-09-06 08:20:04,979 INFO: Start: Fold number 5
-2016-09-06 08:20:05,014 INFO: Start: Classification
-2016-09-06 08:20:05,056 INFO: Done: Fold number 5
-2016-09-06 08:20:05,057 INFO: Done: Classification
-2016-09-06 08:20:05,057 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:20:05,057 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:20:05,062 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 77.7279485987
- -On Test : 47.8048780488
- -On Validation : 70.1123595506
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SVM Linear with C : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:20:05,063 INFO: Done: Result Analysis
-2016-09-06 08:20:05,318 DEBUG: Start: Deleting 2 temporary datasets for multiprocessing
-2016-09-06 08:20:05,319 DEBUG: Start: Deleting datasets for multiprocessing
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1612f21cc8188cb372c386ccb827f8bc95251db1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1c35c0eecfd5c56ea0fc2d69505ea88124cd1d0a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081945Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.847619047619
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.847619047619
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9b99cb3b4ef805716a43fa16940a5d363a0a7bf8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0b4dfa5e4af7554e8029e407837b1129c499d61f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 25, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index de87038fb489b426af9f6111d3309cb214911c75..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0597ef419bab7a6a198dc705ec794d05a5543f63..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081946Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e89e6124e3d9d944dc0a6cb6d4c6069b40561390..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 581b506bc1337a4d853b16cfd9474763d5527544..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.790476190476
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.790476190476
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 575ec7ab26d8a4bd99905b7138ca802dabbf1684..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 30
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 97b41d5bf45286b41cc7ebdeed21d90030693a57..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a38dc962e6355e100590cb71fa5bed31cfb002e7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081947Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0ce94693411ec0fc120fe764d8af2dcb151b3722..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 70684b07e168493e3376d8f394e369da94630bd5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.785714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.785714285714
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b62ada2bfa6a3ae548cc46d3ce08405124dc80c5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 25, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1bd958e29c8a22769c0582b911598fb8949a4e55..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 66681f80a2104052b0154f822ae8d9fe456220ab..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6e8a8b21593856f99767416696b9932449565d56..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5574
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 30604daa66e8f46d6613e610a3fa893ea17709b4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081948Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cc7e68377754562e7aff97c7fd61663d05c36dd1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8fee39767d3aecf6c70068e5d9348d4f7ccf2a88..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cf3d45889c97719340d3d57e4eae6d1ff09396ad..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0f90ba1b62b8b5630c7f6f2bf225d24beb68df26..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081949Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ea57a76b7d58db891aab4d1f3b0cd45d4c0c5928..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8ff0bf29fa3f8c200b8c1799b078ed932e8bdb0e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.819047619048
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 5
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.819047619048
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7e46e3e5ba4b02facd482ab5ac654fa4b6b0e2c5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 30
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2455c24b1f1bb3afa74754536f8b31c4250c9cab..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5574
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a3e078d9b53b8ce0367068b4730ae6ecc8615306..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081950Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8917d3eb2e384b052d59c5e1ec988eefad56ca64..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 25, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d4c5de22d757692901641e580b1e176bf13aa9ed..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5c1ebfae154456bdc93576e9421f07225552c212..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081951Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7056
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-081952Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-081952Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index c2b439ffa033d1fc7b06c114e0ff8cae706b76d3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-081952Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 56.7715963199
- -On Test : 55.6097560976
- -On Validation : 55.2808988764
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index 30116f80b4864414da56f3e09a31e6b95d4e9639..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5f9c24d495c4046a8cd4fffb4e1604d96f714838..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082001Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,235 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 73.0058582753
- -On Test : 48.7804878049
- -On Validation : 68.0898876404Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 8), View1 of shape (300, 16), View2 of shape (300, 16), View3 of shape (300, 18)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:07 0:00:00
- Total 0:00:20 0:00:02
- So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.262162162162
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.262162162162
- - Percentage of time chosen : 0.3
- - On View2 :
- - Mean average Accuracy : 0.243783783784
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.240540540541
- - Percentage of time chosen : 0.0
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.259090909091
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.280113636364
- - Percentage of time chosen : 0.3
- - On View2 :
- - Mean average Accuracy : 0.260227272727
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.247727272727
- - Percentage of time chosen : 0.0
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.249726775956
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.273224043716
- - Percentage of time chosen : 0.3
- - On View2 :
- - Mean average Accuracy : 0.243169398907
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.249726775956
- - Percentage of time chosen : 0.0
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.263440860215
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.265591397849
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.251612903226
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.262365591398
- - Percentage of time chosen : 0.0
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.257458563536
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.26408839779
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.25635359116
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.256906077348
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 46.4864864865
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View1
- Fold 2
- Accuracy on train : 44.8863636364
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 3
- Accuracy on train : 44.8087431694
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 4
- Accuracy on train : 46.2365591398
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 5
- Accuracy on train : 45.8563535912
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 72.4324324324
- Accuracy on test : 0.0
- Accuracy on validation : 60.6741573034
- Selected View : View1
- Fold 2
- Accuracy on train : 74.4318181818
- Accuracy on test : 0.0
- Accuracy on validation : 70.7865168539
- Selected View : View1
- Fold 3
- Accuracy on train : 75.956284153
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View1
- Fold 4
- Accuracy on train : 72.0430107527
- Accuracy on test : 0.0
- Accuracy on validation : 66.2921348315
- Selected View : View1
- Fold 5
- Accuracy on train : 70.1657458564
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View2
- - Iteration 3
- Fold 1
- Accuracy on train : 72.4324324324
- Accuracy on test : 0.0
- Accuracy on validation : 60.6741573034
- Selected View : View1
- Fold 2
- Accuracy on train : 74.4318181818
- Accuracy on test : 0.0
- Accuracy on validation : 70.7865168539
- Selected View : View1
- Fold 3
- Accuracy on train : 75.956284153
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View1
- Fold 4
- Accuracy on train : 72.0430107527
- Accuracy on test : 0.0
- Accuracy on validation : 66.2921348315
- Selected View : View1
- Fold 5
- Accuracy on train : 70.1657458564
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View2
- - Iteration 4
- Fold 1
- Accuracy on train : 72.4324324324
- Accuracy on test : 0.0
- Accuracy on validation : 60.6741573034
- Selected View : View0
- Fold 2
- Accuracy on train : 74.4318181818
- Accuracy on test : 0.0
- Accuracy on validation : 70.7865168539
- Selected View : View1
- Fold 3
- Accuracy on train : 75.956284153
- Accuracy on test : 0.0
- Accuracy on validation : 67.4157303371
- Selected View : View1
- Fold 4
- Accuracy on train : 72.0430107527
- Accuracy on test : 0.0
- Accuracy on validation : 66.2921348315
- Selected View : View0
- Fold 5
- Accuracy on train : 70.1657458564
- Accuracy on test : 0.0
- Accuracy on validation : 75.2808988764
- Selected View : View1
- - Iteration 5
- Fold 1
- Accuracy on train : 46.4864864865
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 2
- Accuracy on train : 44.8863636364
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 3
- Accuracy on train : 44.8087431694
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 4
- Accuracy on train : 46.2365591398
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
- Fold 5
- Accuracy on train : 45.8563535912
- Accuracy on test : 0.0
- Accuracy on validation : 43.8202247191
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index ee39e0b3c167e58e053879c49c413e0be3e7ac6a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 54.932173067
- -On Test : 55.6097560976
- -On Validation : 57.3033707865
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 32117b2bd41dc3017bec8a740c0bbe44a13acd8d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082002Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 58.9879948287
- -On Test : 55.1219512195
- -On Validation : 57.3033707865
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 48aa919de97794f7623f20d5cbe4c625330b38b5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 54.6341463415
- -On Validation : 84.7191011236
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 4de9aa3db28ac3a3c522ad347c0709c547dc59d5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 59.6237187794
- -On Test : 49.756097561
- -On Validation : 57.5280898876
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Decision Tree with max_depth : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 8198fc8a542ffdffe4400499b3d4199fb4a6e87e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 52.1951219512
- -On Validation : 81.797752809
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - K nearest Neighbors with n_neighbors: 1.0
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index c17fa56c7acb3fc5c7e569371b9d540591e02e88..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082003Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 31.9649680433
- -On Test : 36.0975609756
- -On Validation : 31.2359550562
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 2a8fb89b3fc368260f55607cad500efba292638e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 98.9068726244
- -On Test : 50.243902439
- -On Validation : 84.9438202247
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index be905ac83448ea425cd211bfd6b6ebe51f1982cf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082004Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 70.4896592488
- -On Test : 49.756097561
- -On Validation : 67.6404494382
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082005Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082005Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3c818560e4f5199deb2d28bb240d52a3a21dcb8b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082005Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 77.7279485987
- -On Test : 47.8048780488
- -On Validation : 70.1123595506
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SVM Linear with C : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082145-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-082145-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index a34af1e45e9f79e9b852f8b60e82629db166e8ed..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082145-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2265 +0,0 @@
-2016-09-06 08:21:45,928 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 08:21:45,928 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00013571875 Gbytes /!\
-2016-09-06 08:21:50,942 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 08:21:50,945 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 08:21:50,999 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:50,999 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:50,999 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:21:50,999 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:21:50,999 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:50,999 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:51,000 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,000 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,000 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,000 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,001 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:51,001 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:51,001 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,001 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,064 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:51,064 DEBUG: Start: Training
-2016-09-06 08:21:51,067 DEBUG: Info: Time for Training: 0.068393945694[s]
-2016-09-06 08:21:51,067 DEBUG: Done: Training
-2016-09-06 08:21:51,067 DEBUG: Start: Predicting
-2016-09-06 08:21:51,069 DEBUG: Done: Predicting
-2016-09-06 08:21:51,070 DEBUG: Start: Getting Results
-2016-09-06 08:21:51,071 DEBUG: Done: Getting Results
-2016-09-06 08:21:51,071 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,071 INFO: Done: Result Analysis
-2016-09-06 08:21:51,106 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:51,106 DEBUG: Start: Training
-2016-09-06 08:21:51,111 DEBUG: Info: Time for Training: 0.112962007523[s]
-2016-09-06 08:21:51,111 DEBUG: Done: Training
-2016-09-06 08:21:51,111 DEBUG: Start: Predicting
-2016-09-06 08:21:51,114 DEBUG: Done: Predicting
-2016-09-06 08:21:51,114 DEBUG: Start: Getting Results
-2016-09-06 08:21:51,115 DEBUG: Done: Getting Results
-2016-09-06 08:21:51,116 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,116 INFO: Done: Result Analysis
-2016-09-06 08:21:51,252 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:51,252 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:21:51,252 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:51,252 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:51,253 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:21:51,253 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:51,254 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,254 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,254 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,254 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:51,254 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,254 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,254 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:51,254 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,340 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:51,341 DEBUG: Start: Training
-2016-09-06 08:21:51,342 DEBUG: Info: Time for Training: 0.0906808376312[s]
-2016-09-06 08:21:51,342 DEBUG: Done: Training
-2016-09-06 08:21:51,342 DEBUG: Start: Predicting
-2016-09-06 08:21:51,354 DEBUG: Done: Predicting
-2016-09-06 08:21:51,354 DEBUG: Start: Getting Results
-2016-09-06 08:21:51,356 DEBUG: Done: Getting Results
-2016-09-06 08:21:51,356 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.642857142857
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,356 INFO: Done: Result Analysis
-2016-09-06 08:21:51,671 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:51,671 DEBUG: Start: Training
-2016-09-06 08:21:51,698 DEBUG: Info: Time for Training: 0.446767807007[s]
-2016-09-06 08:21:51,698 DEBUG: Done: Training
-2016-09-06 08:21:51,698 DEBUG: Start: Predicting
-2016-09-06 08:21:51,702 DEBUG: Done: Predicting
-2016-09-06 08:21:51,703 DEBUG: Start: Getting Results
-2016-09-06 08:21:51,704 DEBUG: Done: Getting Results
-2016-09-06 08:21:51,704 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 10, max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,704 INFO: Done: Result Analysis
-2016-09-06 08:21:51,794 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:51,794 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:51,794 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:21:51,794 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:21:51,794 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:51,794 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:51,795 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,795 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:51,795 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,795 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:51,795 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:51,795 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:51,795 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,795 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:51,891 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:51,892 DEBUG: Start: Training
-2016-09-06 08:21:51,893 DEBUG: Info: Time for Training: 0.0996568202972[s]
-2016-09-06 08:21:51,893 DEBUG: Done: Training
-2016-09-06 08:21:51,893 DEBUG: Start: Predicting
-2016-09-06 08:21:51,895 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:51,896 DEBUG: Start: Training
-2016-09-06 08:21:51,919 DEBUG: Info: Time for Training: 0.125552892685[s]
-2016-09-06 08:21:51,919 DEBUG: Done: Training
-2016-09-06 08:21:51,919 DEBUG: Start: Predicting
-2016-09-06 08:21:51,921 DEBUG: Done: Predicting
-2016-09-06 08:21:51,922 DEBUG: Start: Getting Results
-2016-09-06 08:21:51,923 DEBUG: Done: Getting Results
-2016-09-06 08:21:51,923 DEBUG: Done: Predicting
-2016-09-06 08:21:51,923 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,923 DEBUG: Start: Getting Results
-2016-09-06 08:21:51,923 INFO: Done: Result Analysis
-2016-09-06 08:21:51,924 DEBUG: Done: Getting Results
-2016-09-06 08:21:51,924 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.457142857143
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:21:51,924 INFO: Done: Result Analysis
-2016-09-06 08:21:52,041 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,041 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,041 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:21:52,041 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:52,041 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:21:52,042 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:52,042 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:52,042 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 08:21:52,042 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:52,042 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 08:21:52,042 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:52,042 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:52,042 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,042 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,126 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:52,126 DEBUG: Start: Training
-2016-09-06 08:21:52,141 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:52,141 DEBUG: Start: Training
-2016-09-06 08:21:52,146 DEBUG: Info: Time for Training: 0.105118989944[s]
-2016-09-06 08:21:52,146 DEBUG: Done: Training
-2016-09-06 08:21:52,146 DEBUG: Start: Predicting
-2016-09-06 08:21:52,153 DEBUG: Done: Predicting
-2016-09-06 08:21:52,153 DEBUG: Start: Getting Results
-2016-09-06 08:21:52,154 DEBUG: Done: Getting Results
-2016-09-06 08:21:52,154 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,154 INFO: Done: Result Analysis
-2016-09-06 08:21:52,161 DEBUG: Info: Time for Training: 0.120436906815[s]
-2016-09-06 08:21:52,161 DEBUG: Done: Training
-2016-09-06 08:21:52,161 DEBUG: Start: Predicting
-2016-09-06 08:21:52,166 DEBUG: Done: Predicting
-2016-09-06 08:21:52,166 DEBUG: Start: Getting Results
-2016-09-06 08:21:52,167 DEBUG: Done: Getting Results
-2016-09-06 08:21:52,167 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8526
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,167 INFO: Done: Result Analysis
-2016-09-06 08:21:52,284 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,284 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,284 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:21:52,284 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:21:52,284 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:52,284 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:52,285 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:52,285 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:52,285 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:52,285 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:52,285 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:52,285 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:52,285 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,285 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,339 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:52,339 DEBUG: Start: Training
-2016-09-06 08:21:52,341 DEBUG: Info: Time for Training: 0.0580468177795[s]
-2016-09-06 08:21:52,341 DEBUG: Done: Training
-2016-09-06 08:21:52,341 DEBUG: Start: Predicting
-2016-09-06 08:21:52,345 DEBUG: Done: Predicting
-2016-09-06 08:21:52,345 DEBUG: Start: Getting Results
-2016-09-06 08:21:52,347 DEBUG: Done: Getting Results
-2016-09-06 08:21:52,347 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,347 INFO: Done: Result Analysis
-2016-09-06 08:21:52,369 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:52,369 DEBUG: Start: Training
-2016-09-06 08:21:52,373 DEBUG: Info: Time for Training: 0.0902559757233[s]
-2016-09-06 08:21:52,373 DEBUG: Done: Training
-2016-09-06 08:21:52,374 DEBUG: Start: Predicting
-2016-09-06 08:21:52,377 DEBUG: Done: Predicting
-2016-09-06 08:21:52,377 DEBUG: Start: Getting Results
-2016-09-06 08:21:52,378 DEBUG: Done: Getting Results
-2016-09-06 08:21:52,378 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,379 INFO: Done: Result Analysis
-2016-09-06 08:21:52,529 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,529 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:52,529 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:21:52,529 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:21:52,529 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:52,529 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:52,530 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:52,530 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:52,530 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:52,530 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:52,530 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:52,530 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:52,530 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,530 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:52,584 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:52,584 DEBUG: Start: Training
-2016-09-06 08:21:52,584 DEBUG: Info: Time for Training: 0.0556619167328[s]
-2016-09-06 08:21:52,584 DEBUG: Done: Training
-2016-09-06 08:21:52,584 DEBUG: Start: Predicting
-2016-09-06 08:21:52,592 DEBUG: Done: Predicting
-2016-09-06 08:21:52,592 DEBUG: Start: Getting Results
-2016-09-06 08:21:52,593 DEBUG: Done: Getting Results
-2016-09-06 08:21:52,593 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,593 INFO: Done: Result Analysis
-2016-09-06 08:21:52,896 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:52,896 DEBUG: Start: Training
-2016-09-06 08:21:52,923 DEBUG: Info: Time for Training: 0.394659996033[s]
-2016-09-06 08:21:52,923 DEBUG: Done: Training
-2016-09-06 08:21:52,924 DEBUG: Start: Predicting
-2016-09-06 08:21:52,928 DEBUG: Done: Predicting
-2016-09-06 08:21:52,928 DEBUG: Start: Getting Results
-2016-09-06 08:21:52,929 DEBUG: Done: Getting Results
-2016-09-06 08:21:52,929 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 10, max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:21:52,929 INFO: Done: Result Analysis
-2016-09-06 08:21:53,082 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,082 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,082 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:21:53,082 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:21:53,083 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:53,083 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:53,083 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:53,083 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:53,083 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:53,083 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:53,083 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:53,083 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:53,083 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,083 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,174 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:53,174 DEBUG: Start: Training
-2016-09-06 08:21:53,175 DEBUG: Info: Time for Training: 0.09352684021[s]
-2016-09-06 08:21:53,175 DEBUG: Done: Training
-2016-09-06 08:21:53,175 DEBUG: Start: Predicting
-2016-09-06 08:21:53,192 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:53,192 DEBUG: Start: Training
-2016-09-06 08:21:53,197 DEBUG: Done: Predicting
-2016-09-06 08:21:53,197 DEBUG: Start: Getting Results
-2016-09-06 08:21:53,198 DEBUG: Done: Getting Results
-2016-09-06 08:21:53,199 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,199 INFO: Done: Result Analysis
-2016-09-06 08:21:53,211 DEBUG: Info: Time for Training: 0.129293203354[s]
-2016-09-06 08:21:53,211 DEBUG: Done: Training
-2016-09-06 08:21:53,211 DEBUG: Start: Predicting
-2016-09-06 08:21:53,214 DEBUG: Done: Predicting
-2016-09-06 08:21:53,215 DEBUG: Start: Getting Results
-2016-09-06 08:21:53,216 DEBUG: Done: Getting Results
-2016-09-06 08:21:53,216 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,216 INFO: Done: Result Analysis
-2016-09-06 08:21:53,328 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,328 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,328 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:21:53,328 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:21:53,328 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:53,328 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:53,329 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:53,329 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 08:21:53,329 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:53,329 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 08:21:53,329 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:53,329 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:53,329 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,329 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,427 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:53,428 DEBUG: Start: Training
-2016-09-06 08:21:53,436 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:53,437 DEBUG: Start: Training
-2016-09-06 08:21:53,453 DEBUG: Info: Time for Training: 0.125381946564[s]
-2016-09-06 08:21:53,453 DEBUG: Done: Training
-2016-09-06 08:21:53,453 DEBUG: Start: Predicting
-2016-09-06 08:21:53,461 DEBUG: Done: Predicting
-2016-09-06 08:21:53,461 DEBUG: Start: Getting Results
-2016-09-06 08:21:53,462 DEBUG: Info: Time for Training: 0.134202957153[s]
-2016-09-06 08:21:53,462 DEBUG: Done: Training
-2016-09-06 08:21:53,462 DEBUG: Start: Predicting
-2016-09-06 08:21:53,463 DEBUG: Done: Getting Results
-2016-09-06 08:21:53,463 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,463 INFO: Done: Result Analysis
-2016-09-06 08:21:53,467 DEBUG: Done: Predicting
-2016-09-06 08:21:53,468 DEBUG: Start: Getting Results
-2016-09-06 08:21:53,469 DEBUG: Done: Getting Results
-2016-09-06 08:21:53,469 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,470 INFO: Done: Result Analysis
-2016-09-06 08:21:53,586 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,586 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:21:53,586 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:53,586 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,587 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:53,587 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:53,587 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:21:53,587 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:53,587 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,587 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:53,588 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:53,588 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:53,588 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:53,588 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,660 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:53,660 DEBUG: Start: Training
-2016-09-06 08:21:53,663 DEBUG: Info: Time for Training: 0.0775690078735[s]
-2016-09-06 08:21:53,663 DEBUG: Done: Training
-2016-09-06 08:21:53,663 DEBUG: Start: Predicting
-2016-09-06 08:21:53,666 DEBUG: Done: Predicting
-2016-09-06 08:21:53,666 DEBUG: Start: Getting Results
-2016-09-06 08:21:53,667 DEBUG: Done: Getting Results
-2016-09-06 08:21:53,667 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,667 INFO: Done: Result Analysis
-2016-09-06 08:21:53,716 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:53,717 DEBUG: Start: Training
-2016-09-06 08:21:53,724 DEBUG: Info: Time for Training: 0.138279914856[s]
-2016-09-06 08:21:53,724 DEBUG: Done: Training
-2016-09-06 08:21:53,724 DEBUG: Start: Predicting
-2016-09-06 08:21:53,729 DEBUG: Done: Predicting
-2016-09-06 08:21:53,729 DEBUG: Start: Getting Results
-2016-09-06 08:21:53,731 DEBUG: Done: Getting Results
-2016-09-06 08:21:53,731 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,732 INFO: Done: Result Analysis
-2016-09-06 08:21:53,836 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,837 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:53,837 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:21:53,837 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:21:53,837 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:53,838 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:53,838 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:53,838 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:53,838 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:53,839 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:53,839 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,839 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:53,839 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:53,839 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:53,895 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:53,895 DEBUG: Start: Training
-2016-09-06 08:21:53,895 DEBUG: Info: Time for Training: 0.0599460601807[s]
-2016-09-06 08:21:53,896 DEBUG: Done: Training
-2016-09-06 08:21:53,896 DEBUG: Start: Predicting
-2016-09-06 08:21:53,904 DEBUG: Done: Predicting
-2016-09-06 08:21:53,904 DEBUG: Start: Getting Results
-2016-09-06 08:21:53,905 DEBUG: Done: Getting Results
-2016-09-06 08:21:53,905 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:21:53,905 INFO: Done: Result Analysis
-2016-09-06 08:21:54,238 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:54,238 DEBUG: Start: Training
-2016-09-06 08:21:54,266 DEBUG: Info: Time for Training: 0.42955493927[s]
-2016-09-06 08:21:54,266 DEBUG: Done: Training
-2016-09-06 08:21:54,266 DEBUG: Start: Predicting
-2016-09-06 08:21:54,270 DEBUG: Done: Predicting
-2016-09-06 08:21:54,271 DEBUG: Start: Getting Results
-2016-09-06 08:21:54,272 DEBUG: Done: Getting Results
-2016-09-06 08:21:54,272 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 10, max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,272 INFO: Done: Result Analysis
-2016-09-06 08:21:54,378 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,378 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,379 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:21:54,379 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:21:54,379 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:54,379 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:54,380 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:54,380 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:54,380 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:54,380 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:54,380 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:54,380 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:54,380 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,380 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,489 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:54,489 DEBUG: Start: Training
-2016-09-06 08:21:54,490 DEBUG: Info: Time for Training: 0.112372159958[s]
-2016-09-06 08:21:54,490 DEBUG: Done: Training
-2016-09-06 08:21:54,490 DEBUG: Start: Predicting
-2016-09-06 08:21:54,505 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:54,505 DEBUG: Start: Training
-2016-09-06 08:21:54,512 DEBUG: Done: Predicting
-2016-09-06 08:21:54,512 DEBUG: Start: Getting Results
-2016-09-06 08:21:54,513 DEBUG: Done: Getting Results
-2016-09-06 08:21:54,513 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,513 INFO: Done: Result Analysis
-2016-09-06 08:21:54,531 DEBUG: Info: Time for Training: 0.153424978256[s]
-2016-09-06 08:21:54,531 DEBUG: Done: Training
-2016-09-06 08:21:54,531 DEBUG: Start: Predicting
-2016-09-06 08:21:54,535 DEBUG: Done: Predicting
-2016-09-06 08:21:54,535 DEBUG: Start: Getting Results
-2016-09-06 08:21:54,536 DEBUG: Done: Getting Results
-2016-09-06 08:21:54,536 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,537 INFO: Done: Result Analysis
-2016-09-06 08:21:54,626 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,626 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,626 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 08:21:54,626 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 08:21:54,626 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:54,626 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:54,627 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:54,627 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 08:21:54,628 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:54,628 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 08:21:54,628 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:54,628 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:54,628 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,628 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,713 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:54,713 DEBUG: Start: Training
-2016-09-06 08:21:54,724 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:54,724 DEBUG: Start: Training
-2016-09-06 08:21:54,732 DEBUG: Info: Time for Training: 0.107496976852[s]
-2016-09-06 08:21:54,733 DEBUG: Done: Training
-2016-09-06 08:21:54,733 DEBUG: Start: Predicting
-2016-09-06 08:21:54,739 DEBUG: Done: Predicting
-2016-09-06 08:21:54,739 DEBUG: Start: Getting Results
-2016-09-06 08:21:54,740 DEBUG: Done: Getting Results
-2016-09-06 08:21:54,740 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,741 INFO: Done: Result Analysis
-2016-09-06 08:21:54,743 DEBUG: Info: Time for Training: 0.118160963058[s]
-2016-09-06 08:21:54,743 DEBUG: Done: Training
-2016-09-06 08:21:54,743 DEBUG: Start: Predicting
-2016-09-06 08:21:54,748 DEBUG: Done: Predicting
-2016-09-06 08:21:54,748 DEBUG: Start: Getting Results
-2016-09-06 08:21:54,749 DEBUG: Done: Getting Results
-2016-09-06 08:21:54,749 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,749 INFO: Done: Result Analysis
-2016-09-06 08:21:54,871 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,871 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:54,872 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 08:21:54,872 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 08:21:54,872 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:54,872 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:54,872 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:54,872 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:54,872 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:54,872 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:54,872 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:54,872 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:54,872 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,872 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:54,925 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:54,925 DEBUG: Start: Training
-2016-09-06 08:21:54,927 DEBUG: Info: Time for Training: 0.0561249256134[s]
-2016-09-06 08:21:54,927 DEBUG: Done: Training
-2016-09-06 08:21:54,927 DEBUG: Start: Predicting
-2016-09-06 08:21:54,930 DEBUG: Done: Predicting
-2016-09-06 08:21:54,930 DEBUG: Start: Getting Results
-2016-09-06 08:21:54,931 DEBUG: Done: Getting Results
-2016-09-06 08:21:54,932 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,932 INFO: Done: Result Analysis
-2016-09-06 08:21:54,952 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:54,953 DEBUG: Start: Training
-2016-09-06 08:21:54,956 DEBUG: Info: Time for Training: 0.0851340293884[s]
-2016-09-06 08:21:54,956 DEBUG: Done: Training
-2016-09-06 08:21:54,956 DEBUG: Start: Predicting
-2016-09-06 08:21:54,959 DEBUG: Done: Predicting
-2016-09-06 08:21:54,959 DEBUG: Start: Getting Results
-2016-09-06 08:21:54,961 DEBUG: Done: Getting Results
-2016-09-06 08:21:54,961 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:21:54,961 INFO: Done: Result Analysis
-2016-09-06 08:21:55,013 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:55,014 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 08:21:55,014 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:55,015 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:55,015 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:55,015 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:55,015 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:55,015 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:55,016 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 08:21:55,016 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:55,017 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:55,017 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:55,018 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:55,018 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:55,069 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:55,070 DEBUG: Start: Training
-2016-09-06 08:21:55,070 DEBUG: Info: Time for Training: 0.0571830272675[s]
-2016-09-06 08:21:55,070 DEBUG: Done: Training
-2016-09-06 08:21:55,070 DEBUG: Start: Predicting
-2016-09-06 08:21:55,077 DEBUG: Done: Predicting
-2016-09-06 08:21:55,077 DEBUG: Start: Getting Results
-2016-09-06 08:21:55,078 DEBUG: Done: Getting Results
-2016-09-06 08:21:55,078 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 08:21:55,078 INFO: Done: Result Analysis
-2016-09-06 08:21:55,421 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:55,421 DEBUG: Start: Training
-2016-09-06 08:21:55,449 DEBUG: Info: Time for Training: 0.434818983078[s]
-2016-09-06 08:21:55,449 DEBUG: Done: Training
-2016-09-06 08:21:55,450 DEBUG: Start: Predicting
-2016-09-06 08:21:55,455 DEBUG: Done: Predicting
-2016-09-06 08:21:55,455 DEBUG: Start: Getting Results
-2016-09-06 08:21:55,457 DEBUG: Done: Getting Results
-2016-09-06 08:21:55,457 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 10, max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 08:21:55,457 INFO: Done: Result Analysis
-2016-09-06 08:21:55,566 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:55,566 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 08:21:55,567 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 08:21:55,567 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 08:21:55,567 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:55,567 DEBUG: Start: Determine Train/Test split
-2016-09-06 08:21:55,568 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:55,568 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 08:21:55,568 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:55,568 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 08:21:55,568 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:55,568 DEBUG: Done: Determine Train/Test split
-2016-09-06 08:21:55,569 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:55,569 DEBUG: Start: RandomSearch best settings with 2 iterations
-2016-09-06 08:21:55,688 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:55,689 DEBUG: Start: Training
-2016-09-06 08:21:55,690 DEBUG: Info: Time for Training: 0.12415099144[s]
-2016-09-06 08:21:55,690 DEBUG: Done: Training
-2016-09-06 08:21:55,690 DEBUG: Start: Predicting
-2016-09-06 08:21:55,695 DEBUG: Done: RandomSearch best settings
-2016-09-06 08:21:55,695 DEBUG: Start: Training
-2016-09-06 08:21:55,713 DEBUG: Info: Time for Training: 0.146976232529[s]
-2016-09-06 08:21:55,713 DEBUG: Done: Training
-2016-09-06 08:21:55,713 DEBUG: Start: Predicting
-2016-09-06 08:21:55,713 DEBUG: Done: Predicting
-2016-09-06 08:21:55,713 DEBUG: Start: Getting Results
-2016-09-06 08:21:55,714 DEBUG: Done: Getting Results
-2016-09-06 08:21:55,714 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:55,715 INFO: Done: Result Analysis
-2016-09-06 08:21:55,716 DEBUG: Done: Predicting
-2016-09-06 08:21:55,716 DEBUG: Start: Getting Results
-2016-09-06 08:21:55,717 DEBUG: Done: Getting Results
-2016-09-06 08:21:55,717 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 08:21:55,718 INFO: Done: Result Analysis
-2016-09-06 08:21:55,958 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:21:55,958 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:21:55,959 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 08:21:55,959 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:21:55,959 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:21:55,959 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:21:55,960 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:21:55,960 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:21:55,961 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:21:55,961 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:21:55,961 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:21:55,961 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:21:55,961 INFO: Done: Read Database Files
-2016-09-06 08:21:55,962 INFO: Done: Read Database Files
-2016-09-06 08:21:55,962 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:21:55,962 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:21:55,967 INFO: Done: Determine validation split
-2016-09-06 08:21:55,967 INFO: Done: Determine validation split
-2016-09-06 08:21:55,967 INFO: Start: Determine 5 folds
-2016-09-06 08:21:55,967 INFO: Start: Determine 5 folds
-2016-09-06 08:21:55,975 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:21:55,975 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:21:55,975 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:21:55,975 INFO: Done: Determine folds
-2016-09-06 08:21:55,975 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 08:21:55,975 INFO: Start: Classification
-2016-09-06 08:21:55,975 INFO: Start: Fold number 1
-2016-09-06 08:21:55,977 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:21:55,977 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:21:55,977 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:21:55,977 INFO: Done: Determine folds
-2016-09-06 08:21:55,977 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:21:55,977 INFO: Start: Classification
-2016-09-06 08:21:55,977 INFO: Start: Fold number 1
-2016-09-06 08:21:56,010 DEBUG: Start: Iteration 1
-2016-09-06 08:21:56,019 DEBUG: View 0 : 0.5
-2016-09-06 08:21:56,027 DEBUG: View 1 : 0.522222222222
-2016-09-06 08:21:56,035 DEBUG: View 2 : 0.522222222222
-2016-09-06 08:21:56,042 DEBUG: View 3 : 0.555555555556
-2016-09-06 08:21:56,051 INFO: Start: Classification
-2016-09-06 08:21:56,077 DEBUG: Best view : View3
-2016-09-06 08:21:56,131 INFO: Done: Fold number 1
-2016-09-06 08:21:56,132 INFO: Start: Fold number 2
-2016-09-06 08:21:56,163 DEBUG: Start: Iteration 2
-2016-09-06 08:21:56,171 DEBUG: View 0 : 0.727777777778
-2016-09-06 08:21:56,178 DEBUG: View 1 : 0.655555555556
-2016-09-06 08:21:56,186 DEBUG: View 2 : 0.716666666667
-2016-09-06 08:21:56,193 DEBUG: View 3 : 0.65
-2016-09-06 08:21:56,201 INFO: Start: Classification
-2016-09-06 08:21:56,233 DEBUG: Best view : View0
-2016-09-06 08:21:56,282 INFO: Done: Fold number 2
-2016-09-06 08:21:56,283 INFO: Start: Fold number 3
-2016-09-06 08:21:56,350 INFO: Start: Classification
-2016-09-06 08:21:56,408 DEBUG: Start: Iteration 3
-2016-09-06 08:21:56,422 DEBUG: View 0 : 0.727777777778
-2016-09-06 08:21:56,429 DEBUG: View 1 : 0.677777777778
-2016-09-06 08:21:56,436 INFO: Done: Fold number 3
-2016-09-06 08:21:56,436 INFO: Start: Fold number 4
-2016-09-06 08:21:56,437 DEBUG: View 2 : 0.716666666667
-2016-09-06 08:21:56,445 DEBUG: View 3 : 0.65
-2016-09-06 08:21:56,487 DEBUG: Best view : View0
-2016-09-06 08:21:56,504 INFO: Start: Classification
-2016-09-06 08:21:56,585 INFO: Done: Fold number 4
-2016-09-06 08:21:56,585 INFO: Start: Fold number 5
-2016-09-06 08:21:56,653 INFO: Start: Classification
-2016-09-06 08:21:56,712 DEBUG: Start: Iteration 4
-2016-09-06 08:21:56,720 DEBUG: View 0 : 0.694444444444
-2016-09-06 08:21:56,728 DEBUG: View 1 : 0.627777777778
-2016-09-06 08:21:56,733 INFO: Done: Fold number 5
-2016-09-06 08:21:56,733 INFO: Done: Classification
-2016-09-06 08:21:56,733 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:21:56,734 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:21:56,735 DEBUG: View 2 : 0.672222222222
-2016-09-06 08:21:56,739 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 47.619047619
- -On Validation : 85.5555555556
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 43
- - Random Forest with num_esimators : 10, max_depth : 21
- - SVM Linear with C : 8282
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:21:56,740 INFO: Done: Result Analysis
-2016-09-06 08:21:56,743 DEBUG: View 3 : 0.666666666667
-2016-09-06 08:21:56,786 DEBUG: Best view : View2
-2016-09-06 08:21:57,080 INFO: Start: Classification
-2016-09-06 08:21:57,551 INFO: Done: Fold number 1
-2016-09-06 08:21:57,552 INFO: Start: Fold number 2
-2016-09-06 08:21:57,580 DEBUG: Start: Iteration 1
-2016-09-06 08:21:57,588 DEBUG: View 0 : 0.534482758621
-2016-09-06 08:21:57,594 DEBUG: View 1 : 0.511494252874
-2016-09-06 08:21:57,601 DEBUG: View 2 : 0.534482758621
-2016-09-06 08:21:57,607 DEBUG: View 3 : 0.477011494253
-2016-09-06 08:21:57,638 DEBUG: Best view : View0
-2016-09-06 08:21:57,715 DEBUG: Start: Iteration 2
-2016-09-06 08:21:57,722 DEBUG: View 0 : 0.741379310345
-2016-09-06 08:21:57,729 DEBUG: View 1 : 0.609195402299
-2016-09-06 08:21:57,736 DEBUG: View 2 : 0.718390804598
-2016-09-06 08:21:57,743 DEBUG: View 3 : 0.649425287356
-2016-09-06 08:21:57,779 DEBUG: Best view : View0
-2016-09-06 08:21:57,922 DEBUG: Start: Iteration 3
-2016-09-06 08:21:57,930 DEBUG: View 0 : 0.741379310345
-2016-09-06 08:21:57,936 DEBUG: View 1 : 0.609195402299
-2016-09-06 08:21:57,943 DEBUG: View 2 : 0.718390804598
-2016-09-06 08:21:57,950 DEBUG: View 3 : 0.649425287356
-2016-09-06 08:21:57,990 DEBUG: Best view : View0
-2016-09-06 08:21:58,199 DEBUG: Start: Iteration 4
-2016-09-06 08:21:58,207 DEBUG: View 0 : 0.735632183908
-2016-09-06 08:21:58,213 DEBUG: View 1 : 0.603448275862
-2016-09-06 08:21:58,220 DEBUG: View 2 : 0.649425287356
-2016-09-06 08:21:58,227 DEBUG: View 3 : 0.637931034483
-2016-09-06 08:21:58,268 DEBUG: Best view : View0
-2016-09-06 08:21:58,541 INFO: Start: Classification
-2016-09-06 08:21:59,002 INFO: Done: Fold number 2
-2016-09-06 08:21:59,002 INFO: Start: Fold number 3
-2016-09-06 08:21:59,032 DEBUG: Start: Iteration 1
-2016-09-06 08:21:59,039 DEBUG: View 0 : 0.489010989011
-2016-09-06 08:21:59,045 DEBUG: View 1 : 0.489010989011
-2016-09-06 08:21:59,052 DEBUG: View 2 : 0.521978021978
-2016-09-06 08:21:59,059 DEBUG: View 3 : 0.554945054945
-2016-09-06 08:21:59,090 DEBUG: Best view : View1
-2016-09-06 08:21:59,170 DEBUG: Start: Iteration 2
-2016-09-06 08:21:59,177 DEBUG: View 0 : 0.637362637363
-2016-09-06 08:21:59,184 DEBUG: View 1 : 0.659340659341
-2016-09-06 08:21:59,192 DEBUG: View 2 : 0.565934065934
-2016-09-06 08:21:59,199 DEBUG: View 3 : 0.648351648352
-2016-09-06 08:21:59,237 DEBUG: Best view : View1
-2016-09-06 08:21:59,385 DEBUG: Start: Iteration 3
-2016-09-06 08:21:59,392 DEBUG: View 0 : 0.637362637363
-2016-09-06 08:21:59,399 DEBUG: View 1 : 0.659340659341
-2016-09-06 08:21:59,407 DEBUG: View 2 : 0.565934065934
-2016-09-06 08:21:59,414 DEBUG: View 3 : 0.648351648352
-2016-09-06 08:21:59,455 DEBUG: Best view : View1
-2016-09-06 08:21:59,671 INFO: Start: Classification
-2016-09-06 08:22:00,026 INFO: Done: Fold number 3
-2016-09-06 08:22:00,026 INFO: Start: Fold number 4
-2016-09-06 08:22:00,057 DEBUG: Start: Iteration 1
-2016-09-06 08:22:00,064 DEBUG: View 0 : 0.559782608696
-2016-09-06 08:22:00,071 DEBUG: View 1 : 0.538043478261
-2016-09-06 08:22:00,078 DEBUG: View 2 : 0.510869565217
-2016-09-06 08:22:00,085 DEBUG: View 3 : 0.516304347826
-2016-09-06 08:22:00,117 DEBUG: Best view : View0
-2016-09-06 08:22:00,199 DEBUG: Start: Iteration 2
-2016-09-06 08:22:00,207 DEBUG: View 0 : 0.717391304348
-2016-09-06 08:22:00,214 DEBUG: View 1 : 0.679347826087
-2016-09-06 08:22:00,221 DEBUG: View 2 : 0.663043478261
-2016-09-06 08:22:00,228 DEBUG: View 3 : 0.663043478261
-2016-09-06 08:22:00,267 DEBUG: Best view : View0
-2016-09-06 08:22:00,419 DEBUG: Start: Iteration 3
-2016-09-06 08:22:00,427 DEBUG: View 0 : 0.717391304348
-2016-09-06 08:22:00,434 DEBUG: View 1 : 0.679347826087
-2016-09-06 08:22:00,442 DEBUG: View 2 : 0.663043478261
-2016-09-06 08:22:00,449 DEBUG: View 3 : 0.663043478261
-2016-09-06 08:22:00,490 DEBUG: Best view : View0
-2016-09-06 08:22:00,711 DEBUG: Start: Iteration 4
-2016-09-06 08:22:00,719 DEBUG: View 0 : 0.679347826087
-2016-09-06 08:22:00,726 DEBUG: View 1 : 0.646739130435
-2016-09-06 08:22:00,734 DEBUG: View 2 : 0.608695652174
-2016-09-06 08:22:00,741 DEBUG: View 3 : 0.592391304348
-2016-09-06 08:22:00,784 DEBUG: Best view : View1
-2016-09-06 08:22:01,075 INFO: Start: Classification
-2016-09-06 08:22:01,552 INFO: Done: Fold number 4
-2016-09-06 08:22:01,553 INFO: Start: Fold number 5
-2016-09-06 08:22:01,582 DEBUG: Start: Iteration 1
-2016-09-06 08:22:01,590 DEBUG: View 0 : 0.469273743017
-2016-09-06 08:22:01,596 DEBUG: View 1 : 0.513966480447
-2016-09-06 08:22:01,603 DEBUG: View 2 : 0.54748603352
-2016-09-06 08:22:01,609 DEBUG: View 3 : 0.586592178771
-2016-09-06 08:22:01,641 DEBUG: Best view : View0
-2016-09-06 08:22:01,720 DEBUG: Start: Iteration 2
-2016-09-06 08:22:01,727 DEBUG: View 0 : 0.703910614525
-2016-09-06 08:22:01,734 DEBUG: View 1 : 0.703910614525
-2016-09-06 08:22:01,742 DEBUG: View 2 : 0.664804469274
-2016-09-06 08:22:01,748 DEBUG: View 3 : 0.642458100559
-2016-09-06 08:22:01,786 DEBUG: Best view : View0
-2016-09-06 08:22:01,933 DEBUG: Start: Iteration 3
-2016-09-06 08:22:01,941 DEBUG: View 0 : 0.703910614525
-2016-09-06 08:22:01,948 DEBUG: View 1 : 0.703910614525
-2016-09-06 08:22:01,955 DEBUG: View 2 : 0.664804469274
-2016-09-06 08:22:01,962 DEBUG: View 3 : 0.642458100559
-2016-09-06 08:22:02,002 DEBUG: Best view : View0
-2016-09-06 08:22:02,216 DEBUG: Start: Iteration 4
-2016-09-06 08:22:02,223 DEBUG: View 0 : 0.642458100559
-2016-09-06 08:22:02,230 DEBUG: View 1 : 0.642458100559
-2016-09-06 08:22:02,238 DEBUG: View 2 : 0.536312849162
-2016-09-06 08:22:02,245 DEBUG: View 3 : 0.653631284916
-2016-09-06 08:22:02,288 DEBUG: Best view : View0
-2016-09-06 08:22:02,569 INFO: Start: Classification
-2016-09-06 08:22:03,036 INFO: Done: Fold number 5
-2016-09-06 08:22:03,036 INFO: Done: Classification
-2016-09-06 08:22:03,036 INFO: Info: Time for Classification: 7[s]
-2016-09-06 08:22:03,036 INFO: Start: Result Analysis for Mumbo
-2016-09-06 08:22:05,430 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 70.9959933267
- -On Test : 48.0952380952
- -On Validation : 70.0Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 19), View1 of shape (300, 7), View2 of shape (300, 18), View3 of shape (300, 6)
- -5 folds
- - Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:06 0:00:00
- Total 0:00:19 0:00:02
- So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.265
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.248333333333
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.262777777778
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.252222222222
- - Percentage of time chosen : 0.1
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.275287356322
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.233333333333
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.262068965517
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.241379310345
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.176373626374
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.180769230769
- - Percentage of time chosen : 0.3
- - On View2 :
- - Mean average Accuracy : 0.165384615385
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.185164835165
- - Percentage of time chosen : 0.0
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.267391304348
- - Percentage of time chosen : 0.9
- - On View1 :
- - Mean average Accuracy : 0.254347826087
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.244565217391
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.24347826087
- - Percentage of time chosen : 0.0
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.251955307263
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.256424581006
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.241340782123
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.25251396648
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 47.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View3
- Fold 2
- Accuracy on train : 46.5517241379
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 3
- Accuracy on train : 47.2527472527
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View1
- Fold 4
- Accuracy on train : 47.8260869565
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 5
- Accuracy on train : 46.3687150838
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 72.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 77.7777777778
- Selected View : View0
- Fold 2
- Accuracy on train : 74.1379310345
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- Fold 3
- Accuracy on train : 65.9340659341
- Accuracy on test : 0.0
- Accuracy on validation : 61.1111111111
- Selected View : View1
- Fold 4
- Accuracy on train : 71.7391304348
- Accuracy on test : 0.0
- Accuracy on validation : 70.0
- Selected View : View0
- Fold 5
- Accuracy on train : 70.3910614525
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- - Iteration 3
- Fold 1
- Accuracy on train : 72.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 77.7777777778
- Selected View : View0
- Fold 2
- Accuracy on train : 74.1379310345
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- Fold 3
- Accuracy on train : 65.9340659341
- Accuracy on test : 0.0
- Accuracy on validation : 61.1111111111
- Selected View : View1
- Fold 4
- Accuracy on train : 71.7391304348
- Accuracy on test : 0.0
- Accuracy on validation : 70.0
- Selected View : View0
- Fold 5
- Accuracy on train : 70.3910614525
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- - Iteration 4
- Fold 1
- Accuracy on train : 72.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 77.7777777778
- Selected View : View2
- Fold 2
- Accuracy on train : 74.1379310345
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- Fold 3
- Accuracy on train : 47.2527472527
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 71.7391304348
- Accuracy on test : 0.0
- Accuracy on validation : 70.0
- Selected View : View1
- Fold 5
- Accuracy on train : 70.3910614525
- Accuracy on test : 0.0
- Accuracy on validation : 70.0
- Selected View : View0
- - Iteration 5
- Fold 1
- Accuracy on train : 47.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 46.5517241379
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 47.8260869565
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 5
- Accuracy on train : 46.3687150838
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
-2016-09-06 08:22:05,624 INFO: Done: Result Analysis
-2016-09-06 08:22:05,722 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:05,723 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:05,723 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:05,723 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:05,723 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:22:05,724 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:22:05,724 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:22:05,724 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:22:05,724 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:22:05,725 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:22:05,725 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:22:05,725 INFO: Done: Read Database Files
-2016-09-06 08:22:05,725 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:22:05,725 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:22:05,725 INFO: Done: Read Database Files
-2016-09-06 08:22:05,725 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:22:05,729 INFO: Done: Determine validation split
-2016-09-06 08:22:05,729 INFO: Done: Determine validation split
-2016-09-06 08:22:05,730 INFO: Start: Determine 5 folds
-2016-09-06 08:22:05,730 INFO: Start: Determine 5 folds
-2016-09-06 08:22:05,737 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:22:05,737 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:22:05,737 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:22:05,737 INFO: Done: Determine folds
-2016-09-06 08:22:05,738 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:22:05,738 INFO: Start: Classification
-2016-09-06 08:22:05,738 INFO: Start: Fold number 1
-2016-09-06 08:22:05,742 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:22:05,742 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:22:05,742 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:22:05,742 INFO: Done: Determine folds
-2016-09-06 08:22:05,742 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:22:05,742 INFO: Start: Classification
-2016-09-06 08:22:05,743 INFO: Start: Fold number 1
-2016-09-06 08:22:05,814 INFO: Start: Classification
-2016-09-06 08:22:05,835 INFO: Start: Classification
-2016-09-06 08:22:05,876 INFO: Done: Fold number 1
-2016-09-06 08:22:05,876 INFO: Start: Fold number 2
-2016-09-06 08:22:05,900 INFO: Done: Fold number 1
-2016-09-06 08:22:05,900 INFO: Start: Fold number 2
-2016-09-06 08:22:05,968 INFO: Start: Classification
-2016-09-06 08:22:05,968 INFO: Start: Classification
-2016-09-06 08:22:06,009 INFO: Done: Fold number 2
-2016-09-06 08:22:06,009 INFO: Start: Fold number 3
-2016-09-06 08:22:06,054 INFO: Done: Fold number 2
-2016-09-06 08:22:06,054 INFO: Start: Fold number 3
-2016-09-06 08:22:06,101 INFO: Start: Classification
-2016-09-06 08:22:06,124 INFO: Start: Classification
-2016-09-06 08:22:06,142 INFO: Done: Fold number 3
-2016-09-06 08:22:06,142 INFO: Start: Fold number 4
-2016-09-06 08:22:06,211 INFO: Done: Fold number 3
-2016-09-06 08:22:06,212 INFO: Start: Fold number 4
-2016-09-06 08:22:06,233 INFO: Start: Classification
-2016-09-06 08:22:06,274 INFO: Done: Fold number 4
-2016-09-06 08:22:06,274 INFO: Start: Fold number 5
-2016-09-06 08:22:06,279 INFO: Start: Classification
-2016-09-06 08:22:06,364 INFO: Done: Fold number 4
-2016-09-06 08:22:06,364 INFO: Start: Fold number 5
-2016-09-06 08:22:06,367 INFO: Start: Classification
-2016-09-06 08:22:06,408 INFO: Done: Fold number 5
-2016-09-06 08:22:06,408 INFO: Done: Classification
-2016-09-06 08:22:06,408 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:22:06,408 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:22:06,413 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 51.9047619048
- -On Validation : 86.6666666667
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 43
- - Random Forest with num_esimators : 10, max_depth : 21
- - SVM Linear with C : 8282
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:06,413 INFO: Done: Result Analysis
-2016-09-06 08:22:06,432 INFO: Start: Classification
-2016-09-06 08:22:06,514 INFO: Done: Fold number 5
-2016-09-06 08:22:06,514 INFO: Done: Classification
-2016-09-06 08:22:06,515 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:22:06,515 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:22:06,519 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 99.2288882289
- -On Test : 44.7619047619
- -On Validation : 88.2222222222
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 43
- - Random Forest with num_esimators : 10, max_depth : 21
- - SVM Linear with C : 8282
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:06,520 INFO: Done: Result Analysis
-2016-09-06 08:22:06,667 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:06,667 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:06,667 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:06,668 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:06,668 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:22:06,668 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:22:06,669 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:22:06,669 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:22:06,669 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:22:06,669 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:22:06,670 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:22:06,670 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:22:06,670 INFO: Done: Read Database Files
-2016-09-06 08:22:06,670 INFO: Done: Read Database Files
-2016-09-06 08:22:06,670 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:22:06,670 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:22:06,675 INFO: Done: Determine validation split
-2016-09-06 08:22:06,675 INFO: Done: Determine validation split
-2016-09-06 08:22:06,675 INFO: Start: Determine 5 folds
-2016-09-06 08:22:06,675 INFO: Start: Determine 5 folds
-2016-09-06 08:22:06,684 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:22:06,684 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:22:06,684 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:22:06,685 INFO: Done: Determine folds
-2016-09-06 08:22:06,685 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:22:06,685 INFO: Start: Classification
-2016-09-06 08:22:06,685 INFO: Start: Fold number 1
-2016-09-06 08:22:06,688 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:22:06,688 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:22:06,688 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:22:06,688 INFO: Done: Determine folds
-2016-09-06 08:22:06,688 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:22:06,688 INFO: Start: Classification
-2016-09-06 08:22:06,689 INFO: Start: Fold number 1
-2016-09-06 08:22:06,715 INFO: Start: Classification
-2016-09-06 08:22:06,740 INFO: Done: Fold number 1
-2016-09-06 08:22:06,740 INFO: Start: Fold number 2
-2016-09-06 08:22:06,760 INFO: Start: Classification
-2016-09-06 08:22:06,768 INFO: Start: Classification
-2016-09-06 08:22:06,795 INFO: Done: Fold number 2
-2016-09-06 08:22:06,796 INFO: Start: Fold number 3
-2016-09-06 08:22:06,807 INFO: Done: Fold number 1
-2016-09-06 08:22:06,807 INFO: Start: Fold number 2
-2016-09-06 08:22:06,820 INFO: Start: Classification
-2016-09-06 08:22:06,847 INFO: Done: Fold number 3
-2016-09-06 08:22:06,847 INFO: Start: Fold number 4
-2016-09-06 08:22:06,872 INFO: Start: Classification
-2016-09-06 08:22:06,878 INFO: Start: Classification
-2016-09-06 08:22:06,898 INFO: Done: Fold number 4
-2016-09-06 08:22:06,898 INFO: Start: Fold number 5
-2016-09-06 08:22:06,921 INFO: Start: Classification
-2016-09-06 08:22:06,924 INFO: Done: Fold number 2
-2016-09-06 08:22:06,924 INFO: Start: Fold number 3
-2016-09-06 08:22:06,947 INFO: Done: Fold number 5
-2016-09-06 08:22:06,947 INFO: Done: Classification
-2016-09-06 08:22:06,947 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:22:06,947 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:22:06,951 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 48.5714285714
- -On Validation : 85.1111111111
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:06,951 INFO: Done: Result Analysis
-2016-09-06 08:22:06,992 INFO: Start: Classification
-2016-09-06 08:22:07,038 INFO: Done: Fold number 3
-2016-09-06 08:22:07,038 INFO: Start: Fold number 4
-2016-09-06 08:22:07,106 INFO: Start: Classification
-2016-09-06 08:22:07,149 INFO: Done: Fold number 4
-2016-09-06 08:22:07,149 INFO: Start: Fold number 5
-2016-09-06 08:22:07,214 INFO: Start: Classification
-2016-09-06 08:22:07,257 INFO: Done: Fold number 5
-2016-09-06 08:22:07,257 INFO: Done: Classification
-2016-09-06 08:22:07,257 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:22:07,257 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:22:07,263 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 8.28366620128
- -On Test : 0.952380952381
- -On Validation : 6.66666666667
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 43
- - Random Forest with num_esimators : 10, max_depth : 21
- - SVM Linear with C : 8282
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:07,263 INFO: Done: Result Analysis
-2016-09-06 08:22:07,423 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:07,423 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:07,424 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:07,424 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:07,424 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:22:07,424 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:22:07,425 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:22:07,425 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:22:07,425 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:22:07,425 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:22:07,425 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:22:07,426 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:22:07,426 INFO: Done: Read Database Files
-2016-09-06 08:22:07,426 INFO: Done: Read Database Files
-2016-09-06 08:22:07,426 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:22:07,426 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:22:07,429 INFO: Done: Determine validation split
-2016-09-06 08:22:07,430 INFO: Done: Determine validation split
-2016-09-06 08:22:07,430 INFO: Start: Determine 5 folds
-2016-09-06 08:22:07,430 INFO: Start: Determine 5 folds
-2016-09-06 08:22:07,438 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:22:07,438 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:22:07,438 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:22:07,438 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:22:07,438 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:22:07,438 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:22:07,438 INFO: Done: Determine folds
-2016-09-06 08:22:07,438 INFO: Done: Determine folds
-2016-09-06 08:22:07,438 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:22:07,438 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:22:07,438 INFO: Start: Classification
-2016-09-06 08:22:07,438 INFO: Start: Classification
-2016-09-06 08:22:07,438 INFO: Start: Fold number 1
-2016-09-06 08:22:07,438 INFO: Start: Fold number 1
-2016-09-06 08:22:07,457 INFO: Start: Classification
-2016-09-06 08:22:07,457 INFO: Start: Classification
-2016-09-06 08:22:07,485 INFO: Done: Fold number 1
-2016-09-06 08:22:07,485 INFO: Start: Fold number 2
-2016-09-06 08:22:07,487 INFO: Done: Fold number 1
-2016-09-06 08:22:07,487 INFO: Start: Fold number 2
-2016-09-06 08:22:07,504 INFO: Start: Classification
-2016-09-06 08:22:07,515 INFO: Start: Classification
-2016-09-06 08:22:07,532 INFO: Done: Fold number 2
-2016-09-06 08:22:07,533 INFO: Start: Fold number 3
-2016-09-06 08:22:07,549 INFO: Start: Classification
-2016-09-06 08:22:07,553 INFO: Done: Fold number 2
-2016-09-06 08:22:07,554 INFO: Start: Fold number 3
-2016-09-06 08:22:07,576 INFO: Start: Classification
-2016-09-06 08:22:07,578 INFO: Done: Fold number 3
-2016-09-06 08:22:07,578 INFO: Start: Fold number 4
-2016-09-06 08:22:07,595 INFO: Start: Classification
-2016-09-06 08:22:07,604 INFO: Done: Fold number 3
-2016-09-06 08:22:07,604 INFO: Start: Fold number 4
-2016-09-06 08:22:07,626 INFO: Done: Fold number 4
-2016-09-06 08:22:07,626 INFO: Start: Fold number 5
-2016-09-06 08:22:07,629 INFO: Start: Classification
-2016-09-06 08:22:07,644 INFO: Start: Classification
-2016-09-06 08:22:07,661 INFO: Done: Fold number 4
-2016-09-06 08:22:07,662 INFO: Start: Fold number 5
-2016-09-06 08:22:07,673 INFO: Done: Fold number 5
-2016-09-06 08:22:07,673 INFO: Done: Classification
-2016-09-06 08:22:07,673 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:22:07,673 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:22:07,678 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 47.1428571429
- -On Validation : 88.4444444444
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - K nearest Neighbors with n_neighbors: 1.0
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:07,678 INFO: Done: Result Analysis
-2016-09-06 08:22:07,682 INFO: Start: Classification
-2016-09-06 08:22:07,710 INFO: Done: Fold number 5
-2016-09-06 08:22:07,710 INFO: Done: Classification
-2016-09-06 08:22:07,710 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:22:07,710 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:22:07,716 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 59.5363054247
- -On Test : 48.5714285714
- -On Validation : 55.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Decision Tree with max_depth : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:07,716 INFO: Done: Result Analysis
-2016-09-06 08:22:07,866 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:07,866 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:07,866 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:07,866 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:07,867 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:22:07,867 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:22:07,867 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:22:07,867 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:22:07,868 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:22:07,868 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:22:07,868 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:22:07,868 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:22:07,868 INFO: Done: Read Database Files
-2016-09-06 08:22:07,868 INFO: Done: Read Database Files
-2016-09-06 08:22:07,869 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:22:07,869 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:22:07,872 INFO: Done: Determine validation split
-2016-09-06 08:22:07,873 INFO: Start: Determine 5 folds
-2016-09-06 08:22:07,873 INFO: Done: Determine validation split
-2016-09-06 08:22:07,873 INFO: Start: Determine 5 folds
-2016-09-06 08:22:07,881 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:22:07,881 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:22:07,881 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:22:07,881 INFO: Done: Determine folds
-2016-09-06 08:22:07,881 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:22:07,881 INFO: Start: Classification
-2016-09-06 08:22:07,882 INFO: Start: Fold number 1
-2016-09-06 08:22:07,883 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:22:07,884 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:22:07,884 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:22:07,884 INFO: Done: Determine folds
-2016-09-06 08:22:07,884 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:22:07,884 INFO: Start: Classification
-2016-09-06 08:22:07,884 INFO: Start: Fold number 1
-2016-09-06 08:22:07,904 INFO: Start: Classification
-2016-09-06 08:22:07,958 INFO: Done: Fold number 1
-2016-09-06 08:22:07,958 INFO: Start: Fold number 2
-2016-09-06 08:22:07,976 INFO: Start: Classification
-2016-09-06 08:22:07,992 INFO: Start: Classification
-2016-09-06 08:22:08,011 INFO: Done: Fold number 2
-2016-09-06 08:22:08,011 INFO: Start: Fold number 3
-2016-09-06 08:22:08,036 INFO: Start: Classification
-2016-09-06 08:22:08,040 INFO: Done: Fold number 1
-2016-09-06 08:22:08,040 INFO: Start: Fold number 2
-2016-09-06 08:22:08,067 INFO: Done: Fold number 3
-2016-09-06 08:22:08,068 INFO: Start: Fold number 4
-2016-09-06 08:22:08,091 INFO: Start: Classification
-2016-09-06 08:22:08,134 INFO: Done: Fold number 4
-2016-09-06 08:22:08,134 INFO: Start: Fold number 5
-2016-09-06 08:22:08,167 INFO: Start: Classification
-2016-09-06 08:22:08,172 INFO: Start: Classification
-2016-09-06 08:22:08,217 INFO: Done: Fold number 2
-2016-09-06 08:22:08,217 INFO: Start: Fold number 3
-2016-09-06 08:22:08,222 INFO: Done: Fold number 5
-2016-09-06 08:22:08,227 INFO: Done: Classification
-2016-09-06 08:22:08,228 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:22:08,228 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:22:08,238 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 70.0117009413
- -On Test : 49.5238095238
- -On Validation : 63.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:08,241 INFO: Done: Result Analysis
-2016-09-06 08:22:08,350 INFO: Start: Classification
-2016-09-06 08:22:08,400 INFO: Done: Fold number 3
-2016-09-06 08:22:08,400 INFO: Start: Fold number 4
-2016-09-06 08:22:08,532 INFO: Start: Classification
-2016-09-06 08:22:08,570 INFO: Done: Fold number 4
-2016-09-06 08:22:08,571 INFO: Start: Fold number 5
-2016-09-06 08:22:08,655 INFO: Start: Classification
-2016-09-06 08:22:08,689 INFO: Done: Fold number 5
-2016-09-06 08:22:08,689 INFO: Done: Classification
-2016-09-06 08:22:08,689 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:22:08,689 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:22:08,694 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 98.9016594713
- -On Test : 47.1428571429
- -On Validation : 86.2222222222
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:08,694 INFO: Done: Result Analysis
-2016-09-06 08:22:08,819 INFO: ### Main Programm for Multiview Classification
-2016-09-06 08:22:08,820 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 08:22:08,821 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 08:22:08,821 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 08:22:08,822 INFO: Info: Shape of View2 :(300, 18)
-2016-09-06 08:22:08,823 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 08:22:08,823 INFO: Done: Read Database Files
-2016-09-06 08:22:08,823 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 08:22:08,828 INFO: Done: Determine validation split
-2016-09-06 08:22:08,828 INFO: Start: Determine 5 folds
-2016-09-06 08:22:08,841 INFO: Info: Length of Learning Sets: 168
-2016-09-06 08:22:08,841 INFO: Info: Length of Testing Sets: 42
-2016-09-06 08:22:08,841 INFO: Info: Length of Validation Set: 90
-2016-09-06 08:22:08,841 INFO: Done: Determine folds
-2016-09-06 08:22:08,841 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 08:22:08,842 INFO: Start: Classification
-2016-09-06 08:22:08,842 INFO: Start: Fold number 1
-2016-09-06 08:22:08,881 INFO: Start: Classification
-2016-09-06 08:22:08,913 INFO: Done: Fold number 1
-2016-09-06 08:22:08,913 INFO: Start: Fold number 2
-2016-09-06 08:22:08,958 INFO: Start: Classification
-2016-09-06 08:22:09,000 INFO: Done: Fold number 2
-2016-09-06 08:22:09,001 INFO: Start: Fold number 3
-2016-09-06 08:22:09,035 INFO: Start: Classification
-2016-09-06 08:22:09,066 INFO: Done: Fold number 3
-2016-09-06 08:22:09,067 INFO: Start: Fold number 4
-2016-09-06 08:22:09,100 INFO: Start: Classification
-2016-09-06 08:22:09,129 INFO: Done: Fold number 4
-2016-09-06 08:22:09,129 INFO: Start: Fold number 5
-2016-09-06 08:22:09,165 INFO: Start: Classification
-2016-09-06 08:22:09,195 INFO: Done: Fold number 5
-2016-09-06 08:22:09,195 INFO: Done: Classification
-2016-09-06 08:22:09,195 INFO: Info: Time for Classification: 0[s]
-2016-09-06 08:22:09,195 INFO: Start: Result Analysis for Fusion
-2016-09-06 08:22:09,200 INFO: Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 74.6411370567
- -On Test : 49.5238095238
- -On Validation : 67.5555555556
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SVM Linear with C : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-06 08:22:09,200 INFO: Done: Result Analysis
-2016-09-06 08:22:09,363 DEBUG: Start: Deleting 2 temporary datasets for multiprocessing
-2016-09-06 08:22:09,363 DEBUG: Start: Deleting datasets for multiprocessing
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1e1ff6f278f96baed9e4caa7ba323ef22a37d266..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e514a6f6d351994501c3584feb4311f84f97958f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6af21963cdf040eca8bff5084f7ab174a885375b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.642857142857
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ba94ca1a7486edf6154ac7edfe6a2747972a37da..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 10, max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 76fed0d2a01adcc1eb52f11b05bd803ff4b75165..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6c815dfb5a95e5f87a8ae18b28a8044227f99768..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082151Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.457142857143
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ee87e72983a6f531e3d84ddcc4a3ecd6af897e3f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0a445c378d1777a799192cceee96c9de56f17e73..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5a85ac3a607de6ea8c39f9304b8632d46fb2b194..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6d85da2a1e4d44b5a01e5a4662ab2d229a237013..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 10, max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 74dec77bf0d60776462c3df2f17e6a067a2e786d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8526
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a48bd5d1624f7bdf0d200383aa71d1400dc1f00b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082152Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 95412304f35ffa986279ce68b7676b55c8e998de..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 501b808349f661b4e79677d83650bda9f8e2c674..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ae7647f03fa1f3a649c60467f0feff79d250576b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a32290765dd05bad866f27c315728807bb9dcf51..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 72b8490ab1447b48eb441dfcf7ceca1e7fc95e73..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2e8eb890de9aa8da7ab71dee2df836fcd40103f7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3b739757bc148dac2428716e1d49467a348a36a6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082153Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e949584bc418601850fe560c491ff28b5f1ee523..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index edea8d848f045bc08c4eb4e72e89abeb77fac478..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2a8420c7a1faf25fd795d968e1389a0627b220bb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 10, max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c5b216bf481a042fac010b4dd8d8ec1cd1eca0b7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1a1a0d9bb36ae04ad09862a003cf66dc5dc98e55..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db4f16ed705616c5e7d2e85f9a3bcf1324d8ce3e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b766ae96b620420f1b35d3252c32c6cc81c6a13d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082154Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c5f4425da32baf528ada5d9d015098f074ea80cd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0c89a467b5849f658f9e6c9ae46d5aa19daf249c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 10, max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 110eb6654e0f2454005fe3b894a57e0b952c186f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ebbde3e7ed6bbe5fc9cdc312a52316f6dcaddf65..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082155Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8282
- - Executed on 1 core(s)
- - Got configuration using randomized search with 2 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082156Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082156Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 7063676480e764cd1745ed644afc02aed3618cfb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082156Results-Fusion-LateFusion-BayesianInference-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 47.619047619
- -On Validation : 85.5555555556
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.25, 0.25, 0.25, 0.25
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 43
- - Random Forest with num_esimators : 10, max_depth : 21
- - SVM Linear with C : 8282
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index 71887a23e9e1b610ce45ae80e05375008d2c4c10..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index c3badee15d136249c9b989d9cc0171a6221b4a15..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082205Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,230 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 70.9959933267
- -On Test : 48.0952380952
- -On Validation : 70.0Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 19), View1 of shape (300, 7), View2 of shape (300, 18), View3 of shape (300, 6)
- -5 folds
- - Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:06 0:00:00
- Total 0:00:19 0:00:02
- So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.265
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.248333333333
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.262777777778
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.252222222222
- - Percentage of time chosen : 0.1
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.275287356322
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.233333333333
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.262068965517
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.241379310345
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.176373626374
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.180769230769
- - Percentage of time chosen : 0.3
- - On View2 :
- - Mean average Accuracy : 0.165384615385
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.185164835165
- - Percentage of time chosen : 0.0
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.267391304348
- - Percentage of time chosen : 0.9
- - On View1 :
- - Mean average Accuracy : 0.254347826087
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.244565217391
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.24347826087
- - Percentage of time chosen : 0.0
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.251955307263
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.256424581006
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.241340782123
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.25251396648
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 47.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View3
- Fold 2
- Accuracy on train : 46.5517241379
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 3
- Accuracy on train : 47.2527472527
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View1
- Fold 4
- Accuracy on train : 47.8260869565
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 5
- Accuracy on train : 46.3687150838
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 72.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 77.7777777778
- Selected View : View0
- Fold 2
- Accuracy on train : 74.1379310345
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- Fold 3
- Accuracy on train : 65.9340659341
- Accuracy on test : 0.0
- Accuracy on validation : 61.1111111111
- Selected View : View1
- Fold 4
- Accuracy on train : 71.7391304348
- Accuracy on test : 0.0
- Accuracy on validation : 70.0
- Selected View : View0
- Fold 5
- Accuracy on train : 70.3910614525
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- - Iteration 3
- Fold 1
- Accuracy on train : 72.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 77.7777777778
- Selected View : View0
- Fold 2
- Accuracy on train : 74.1379310345
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- Fold 3
- Accuracy on train : 65.9340659341
- Accuracy on test : 0.0
- Accuracy on validation : 61.1111111111
- Selected View : View1
- Fold 4
- Accuracy on train : 71.7391304348
- Accuracy on test : 0.0
- Accuracy on validation : 70.0
- Selected View : View0
- Fold 5
- Accuracy on train : 70.3910614525
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- - Iteration 4
- Fold 1
- Accuracy on train : 72.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 77.7777777778
- Selected View : View2
- Fold 2
- Accuracy on train : 74.1379310345
- Accuracy on test : 0.0
- Accuracy on validation : 71.1111111111
- Selected View : View0
- Fold 3
- Accuracy on train : 47.2527472527
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 71.7391304348
- Accuracy on test : 0.0
- Accuracy on validation : 70.0
- Selected View : View1
- Fold 5
- Accuracy on train : 70.3910614525
- Accuracy on test : 0.0
- Accuracy on validation : 70.0
- Selected View : View0
- - Iteration 5
- Fold 1
- Accuracy on train : 47.7777777778
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 46.5517241379
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 47.8260869565
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 5
- Accuracy on train : 46.3687150838
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index b4d6401f0484d8ef99493579aca13cf8d18a74fc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 48.5714285714
- -On Validation : 85.1111111111
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Adaboost with num_esimators : 1, base_estimators : DecisionTreeClassifier
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-MajorityVoting-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-MajorityVoting-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 831a920ac6180d1b2487d222f82ea8e254065320..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-MajorityVoting-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 99.2288882289
- -On Test : 44.7619047619
- -On Validation : 88.2222222222
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 43
- - Random Forest with num_esimators : 10, max_depth : 21
- - SVM Linear with C : 8282
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-SVMForLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-SVMForLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 288138935606259b2c9b65a109a11b9cf9e00f60..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082206Results-Fusion-LateFusion-SVMForLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 51.9047619048
- -On Validation : 86.6666666667
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 43
- - Random Forest with num_esimators : 10, max_depth : 21
- - SVM Linear with C : 8282
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index db0c2c18fcb4f1e32238944bf078b317c9aea430..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 59.5363054247
- -On Test : 48.5714285714
- -On Validation : 55.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Decision Tree with max_depth : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index ec9120dee4be2892277abb1f874688d24731b8e9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 47.1428571429
- -On Validation : 88.4444444444
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - K nearest Neighbors with n_neighbors: 1.0
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-LateFusion-WeightedLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-LateFusion-WeightedLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index b8150a2f1513668df3e177e385b5144242ef7bf9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082207Results-Fusion-LateFusion-WeightedLinear-KNN-RandomForest-SVMRBF-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,35 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 8.28366620128
- -On Test : 0.952380952381
- -On Validation : 6.66666666667
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 1.0, 1.0, 1.0
- -With monoview classifiers :
- - K nearest Neighbors with n_neighbors: 43
- - Random Forest with num_esimators : 10, max_depth : 21
- - SVM Linear with C : 8282
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index e76f9e9fbc6ae655b812b5de32963da22a490e91..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 98.9016594713
- -On Test : 47.1428571429
- -On Validation : 86.2222222222
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - Random Forest with num_esimators : 25, max_depth : 5
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 9b3ab32d049c84a99643a949b907a615e604845e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082208Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 70.0117009413
- -On Test : 49.5238095238
- -On Validation : 63.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SGDClassifier with loss : log, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-082209Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-082209Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index d845d77310146e434e31622165ce95892984c2a4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-082209Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 74.6411370567
- -On Test : 49.5238095238
- -On Validation : 67.5555555556
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 1.0, 1.0, 1.0 with monoview classifier :
- - SVM Linear with C : 1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:00 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index c45e025b25568e4e18bd3cae72e495b3e0d20abd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-092557-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,8 +0,0 @@
-2016-09-06 09:25:57,714 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 09:25:57,717 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 09:25:57,717 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 09:25:57,717 DEBUG: Start: Determine Train/Test split
-2016-09-06 09:25:57,717 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 09:25:57,717 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 09:25:57,717 DEBUG: Done: Determine Train/Test split
-2016-09-06 09:25:57,717 DEBUG: Start: RandomSearch best settings with 30 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 8bd52115dc8051d6d39f7aa867ab793b4db84c1a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,15 +0,0 @@
-2016-09-06 10:06:22,879 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:06:22,881 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:06:22,881 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:06:22,881 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:06:22,882 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:06:22,882 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:06:22,882 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:06:22,882 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:06:22,965 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:06:22,965 DEBUG: Start: Training
-2016-09-06 10:06:22,970 DEBUG: Info: Time for Training: 0.0897569656372[s]
-2016-09-06 10:06:22,971 DEBUG: Done: Training
-2016-09-06 10:06:22,971 DEBUG: Start: Predicting
-2016-09-06 10:06:22,984 DEBUG: Done: Predicting
-2016-09-06 10:06:22,984 DEBUG: Start: Getting Results
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index b3758f074b7c859f23b08a7aea92ac6164b7b190..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100729-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1310 +0,0 @@
-2016-09-06 10:07:29,943 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:07:29,945 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:29,945 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:29,945 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:29,946 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:29,946 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:29,946 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:29,946 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,025 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,025 DEBUG: Start: Training
-2016-09-06 10:07:30,029 DEBUG: Info: Time for Training: 0.0845639705658[s]
-2016-09-06 10:07:30,029 DEBUG: Done: Training
-2016-09-06 10:07:30,029 DEBUG: Start: Predicting
-2016-09-06 10:07:30,032 DEBUG: Done: Predicting
-2016-09-06 10:07:30,032 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,033 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,034 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,034 INFO: Done: Result Analysis
-2016-09-06 10:07:30,035 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,035 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:30,035 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,035 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,035 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,035 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,035 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,065 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,065 DEBUG: Start: Training
-2016-09-06 10:07:30,066 DEBUG: Info: Time for Training: 0.0320420265198[s]
-2016-09-06 10:07:30,067 DEBUG: Done: Training
-2016-09-06 10:07:30,067 DEBUG: Start: Predicting
-2016-09-06 10:07:30,068 DEBUG: Done: Predicting
-2016-09-06 10:07:30,068 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,069 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,069 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.866666666667
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.866666666667
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,070 INFO: Done: Result Analysis
-2016-09-06 10:07:30,071 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,071 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:30,071 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,071 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,071 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,071 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,071 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,113 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,113 DEBUG: Start: Training
-2016-09-06 10:07:30,113 DEBUG: Info: Time for Training: 0.0430898666382[s]
-2016-09-06 10:07:30,113 DEBUG: Done: Training
-2016-09-06 10:07:30,114 DEBUG: Start: Predicting
-2016-09-06 10:07:30,119 DEBUG: Done: Predicting
-2016-09-06 10:07:30,120 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,121 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,121 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 44
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,121 INFO: Done: Result Analysis
-2016-09-06 10:07:30,122 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,122 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:30,122 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,122 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,123 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,123 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,123 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,425 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,425 DEBUG: Start: Training
-2016-09-06 10:07:30,470 DEBUG: Info: Time for Training: 0.348073959351[s]
-2016-09-06 10:07:30,470 DEBUG: Done: Training
-2016-09-06 10:07:30,470 DEBUG: Start: Predicting
-2016-09-06 10:07:30,475 DEBUG: Done: Predicting
-2016-09-06 10:07:30,475 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,476 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,476 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.766666666667
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 18, max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.766666666667
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,477 INFO: Done: Result Analysis
-2016-09-06 10:07:30,478 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,478 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:30,478 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,478 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,478 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,478 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,478 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,540 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,540 DEBUG: Start: Training
-2016-09-06 10:07:30,541 DEBUG: Info: Time for Training: 0.0633749961853[s]
-2016-09-06 10:07:30,541 DEBUG: Done: Training
-2016-09-06 10:07:30,541 DEBUG: Start: Predicting
-2016-09-06 10:07:30,587 DEBUG: Done: Predicting
-2016-09-06 10:07:30,587 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,589 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,590 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,590 INFO: Done: Result Analysis
-2016-09-06 10:07:30,592 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,592 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:30,592 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,593 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,593 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,593 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,593 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,640 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,640 DEBUG: Start: Training
-2016-09-06 10:07:30,658 DEBUG: Info: Time for Training: 0.0669250488281[s]
-2016-09-06 10:07:30,658 DEBUG: Done: Training
-2016-09-06 10:07:30,658 DEBUG: Start: Predicting
-2016-09-06 10:07:30,661 DEBUG: Done: Predicting
-2016-09-06 10:07:30,661 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,662 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,662 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 491
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,662 INFO: Done: Result Analysis
-2016-09-06 10:07:30,663 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,664 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:30,664 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,664 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,664 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,664 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,664 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,709 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,710 DEBUG: Start: Training
-2016-09-06 10:07:30,726 DEBUG: Info: Time for Training: 0.0627450942993[s]
-2016-09-06 10:07:30,726 DEBUG: Done: Training
-2016-09-06 10:07:30,726 DEBUG: Start: Predicting
-2016-09-06 10:07:30,729 DEBUG: Done: Predicting
-2016-09-06 10:07:30,729 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,730 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,730 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2405
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,730 INFO: Done: Result Analysis
-2016-09-06 10:07:30,731 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,731 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:30,731 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,732 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:07:30,732 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:07:30,732 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,732 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,773 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,773 DEBUG: Start: Training
-2016-09-06 10:07:30,790 DEBUG: Info: Time for Training: 0.0591881275177[s]
-2016-09-06 10:07:30,790 DEBUG: Done: Training
-2016-09-06 10:07:30,790 DEBUG: Start: Predicting
-2016-09-06 10:07:30,794 DEBUG: Done: Predicting
-2016-09-06 10:07:30,795 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,796 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,796 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9676
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,796 INFO: Done: Result Analysis
-2016-09-06 10:07:30,797 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,797 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:30,797 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,797 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:30,798 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:30,798 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,798 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,840 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,840 DEBUG: Start: Training
-2016-09-06 10:07:30,844 DEBUG: Info: Time for Training: 0.0470898151398[s]
-2016-09-06 10:07:30,844 DEBUG: Done: Training
-2016-09-06 10:07:30,844 DEBUG: Start: Predicting
-2016-09-06 10:07:30,846 DEBUG: Done: Predicting
-2016-09-06 10:07:30,846 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,848 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,848 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,848 INFO: Done: Result Analysis
-2016-09-06 10:07:30,849 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,849 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:30,849 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,849 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:30,849 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:30,850 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,850 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,877 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,877 DEBUG: Start: Training
-2016-09-06 10:07:30,879 DEBUG: Info: Time for Training: 0.0301620960236[s]
-2016-09-06 10:07:30,879 DEBUG: Done: Training
-2016-09-06 10:07:30,879 DEBUG: Start: Predicting
-2016-09-06 10:07:30,880 DEBUG: Done: Predicting
-2016-09-06 10:07:30,880 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,882 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,882 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,882 INFO: Done: Result Analysis
-2016-09-06 10:07:30,883 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,883 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:30,883 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,883 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:30,883 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:30,883 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,884 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:30,909 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:30,909 DEBUG: Start: Training
-2016-09-06 10:07:30,910 DEBUG: Info: Time for Training: 0.0272629261017[s]
-2016-09-06 10:07:30,910 DEBUG: Done: Training
-2016-09-06 10:07:30,910 DEBUG: Start: Predicting
-2016-09-06 10:07:30,914 DEBUG: Done: Predicting
-2016-09-06 10:07:30,914 DEBUG: Start: Getting Results
-2016-09-06 10:07:30,916 DEBUG: Done: Getting Results
-2016-09-06 10:07:30,916 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:30,916 INFO: Done: Result Analysis
-2016-09-06 10:07:30,917 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:30,917 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:30,917 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:30,917 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:30,918 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:30,918 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:30,918 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,132 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,133 DEBUG: Start: Training
-2016-09-06 10:07:31,164 DEBUG: Info: Time for Training: 0.247138977051[s]
-2016-09-06 10:07:31,164 DEBUG: Done: Training
-2016-09-06 10:07:31,164 DEBUG: Start: Predicting
-2016-09-06 10:07:31,168 DEBUG: Done: Predicting
-2016-09-06 10:07:31,168 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,169 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,169 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 12, max_depth : 17
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.980952380952
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,170 INFO: Done: Result Analysis
-2016-09-06 10:07:31,171 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,171 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:31,171 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,171 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:31,171 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:31,171 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,171 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,209 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,209 DEBUG: Start: Training
-2016-09-06 10:07:31,210 DEBUG: Info: Time for Training: 0.0394690036774[s]
-2016-09-06 10:07:31,210 DEBUG: Done: Training
-2016-09-06 10:07:31,210 DEBUG: Start: Predicting
-2016-09-06 10:07:31,211 DEBUG: Done: Predicting
-2016-09-06 10:07:31,212 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,213 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,213 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,213 INFO: Done: Result Analysis
-2016-09-06 10:07:31,214 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,214 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:31,214 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,214 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:31,215 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:31,215 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,215 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,257 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,257 DEBUG: Start: Training
-2016-09-06 10:07:31,275 DEBUG: Info: Time for Training: 0.061126947403[s]
-2016-09-06 10:07:31,275 DEBUG: Done: Training
-2016-09-06 10:07:31,275 DEBUG: Start: Predicting
-2016-09-06 10:07:31,277 DEBUG: Done: Predicting
-2016-09-06 10:07:31,277 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,279 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,279 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1250
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,279 INFO: Done: Result Analysis
-2016-09-06 10:07:31,280 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,280 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:31,280 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,280 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:31,280 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:31,281 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,281 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,329 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,329 DEBUG: Start: Training
-2016-09-06 10:07:31,347 DEBUG: Info: Time for Training: 0.0671949386597[s]
-2016-09-06 10:07:31,347 DEBUG: Done: Training
-2016-09-06 10:07:31,347 DEBUG: Start: Predicting
-2016-09-06 10:07:31,349 DEBUG: Done: Predicting
-2016-09-06 10:07:31,349 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,351 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,351 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 432
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,351 INFO: Done: Result Analysis
-2016-09-06 10:07:31,352 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,352 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:31,352 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,352 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:07:31,352 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:07:31,352 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,353 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,393 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,393 DEBUG: Start: Training
-2016-09-06 10:07:31,409 DEBUG: Info: Time for Training: 0.0578751564026[s]
-2016-09-06 10:07:31,410 DEBUG: Done: Training
-2016-09-06 10:07:31,410 DEBUG: Start: Predicting
-2016-09-06 10:07:31,414 DEBUG: Done: Predicting
-2016-09-06 10:07:31,414 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,415 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,415 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6005
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,415 INFO: Done: Result Analysis
-2016-09-06 10:07:31,416 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,416 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:31,417 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,417 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,417 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,417 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,417 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,461 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,461 DEBUG: Start: Training
-2016-09-06 10:07:31,465 DEBUG: Info: Time for Training: 0.0488121509552[s]
-2016-09-06 10:07:31,465 DEBUG: Done: Training
-2016-09-06 10:07:31,465 DEBUG: Start: Predicting
-2016-09-06 10:07:31,467 DEBUG: Done: Predicting
-2016-09-06 10:07:31,467 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,469 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,469 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,469 INFO: Done: Result Analysis
-2016-09-06 10:07:31,470 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,470 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:31,470 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,471 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,471 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,471 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,471 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,502 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,502 DEBUG: Start: Training
-2016-09-06 10:07:31,505 DEBUG: Info: Time for Training: 0.0348341464996[s]
-2016-09-06 10:07:31,505 DEBUG: Done: Training
-2016-09-06 10:07:31,505 DEBUG: Start: Predicting
-2016-09-06 10:07:31,506 DEBUG: Done: Predicting
-2016-09-06 10:07:31,506 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,507 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,507 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,508 INFO: Done: Result Analysis
-2016-09-06 10:07:31,509 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,509 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:31,509 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,509 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,509 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,509 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,509 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,535 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,535 DEBUG: Start: Training
-2016-09-06 10:07:31,535 DEBUG: Info: Time for Training: 0.0270109176636[s]
-2016-09-06 10:07:31,535 DEBUG: Done: Training
-2016-09-06 10:07:31,535 DEBUG: Start: Predicting
-2016-09-06 10:07:31,540 DEBUG: Done: Predicting
-2016-09-06 10:07:31,540 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,541 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,541 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.628571428571
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,541 INFO: Done: Result Analysis
-2016-09-06 10:07:31,542 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,542 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:31,542 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,543 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,543 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,543 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,543 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,808 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,808 DEBUG: Start: Training
-2016-09-06 10:07:31,847 DEBUG: Info: Time for Training: 0.305594921112[s]
-2016-09-06 10:07:31,848 DEBUG: Done: Training
-2016-09-06 10:07:31,848 DEBUG: Start: Predicting
-2016-09-06 10:07:31,852 DEBUG: Done: Predicting
-2016-09-06 10:07:31,852 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,854 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,854 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 15, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,854 INFO: Done: Result Analysis
-2016-09-06 10:07:31,855 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,855 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:31,855 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,855 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,855 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,856 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,856 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,893 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,894 DEBUG: Start: Training
-2016-09-06 10:07:31,894 DEBUG: Info: Time for Training: 0.0398690700531[s]
-2016-09-06 10:07:31,895 DEBUG: Done: Training
-2016-09-06 10:07:31,895 DEBUG: Start: Predicting
-2016-09-06 10:07:31,896 DEBUG: Done: Predicting
-2016-09-06 10:07:31,896 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,897 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,897 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,898 INFO: Done: Result Analysis
-2016-09-06 10:07:31,899 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,899 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:31,899 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,899 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,899 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,899 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,899 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:31,943 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:31,943 DEBUG: Start: Training
-2016-09-06 10:07:31,963 DEBUG: Info: Time for Training: 0.0644710063934[s]
-2016-09-06 10:07:31,963 DEBUG: Done: Training
-2016-09-06 10:07:31,963 DEBUG: Start: Predicting
-2016-09-06 10:07:31,965 DEBUG: Done: Predicting
-2016-09-06 10:07:31,965 DEBUG: Start: Getting Results
-2016-09-06 10:07:31,967 DEBUG: Done: Getting Results
-2016-09-06 10:07:31,967 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6060
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:31,967 INFO: Done: Result Analysis
-2016-09-06 10:07:31,968 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:31,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:31,968 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:31,968 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:31,968 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:31,969 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:31,969 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,017 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:32,017 DEBUG: Start: Training
-2016-09-06 10:07:32,034 DEBUG: Info: Time for Training: 0.0663030147552[s]
-2016-09-06 10:07:32,034 DEBUG: Done: Training
-2016-09-06 10:07:32,034 DEBUG: Start: Predicting
-2016-09-06 10:07:32,037 DEBUG: Done: Predicting
-2016-09-06 10:07:32,037 DEBUG: Start: Getting Results
-2016-09-06 10:07:32,038 DEBUG: Done: Getting Results
-2016-09-06 10:07:32,039 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 569
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,039 INFO: Done: Result Analysis
-2016-09-06 10:07:32,040 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,040 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:32,040 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:32,040 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:07:32,040 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:07:32,040 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:32,041 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,082 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:32,082 DEBUG: Start: Training
-2016-09-06 10:07:32,098 DEBUG: Info: Time for Training: 0.0586409568787[s]
-2016-09-06 10:07:32,098 DEBUG: Done: Training
-2016-09-06 10:07:32,098 DEBUG: Start: Predicting
-2016-09-06 10:07:32,103 DEBUG: Done: Predicting
-2016-09-06 10:07:32,103 DEBUG: Start: Getting Results
-2016-09-06 10:07:32,104 DEBUG: Done: Getting Results
-2016-09-06 10:07:32,104 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1510
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,104 INFO: Done: Result Analysis
-2016-09-06 10:07:32,105 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,105 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:32,106 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:32,106 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,106 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,106 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:32,106 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,152 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:32,152 DEBUG: Start: Training
-2016-09-06 10:07:32,156 DEBUG: Info: Time for Training: 0.0510900020599[s]
-2016-09-06 10:07:32,156 DEBUG: Done: Training
-2016-09-06 10:07:32,156 DEBUG: Start: Predicting
-2016-09-06 10:07:32,158 DEBUG: Done: Predicting
-2016-09-06 10:07:32,158 DEBUG: Start: Getting Results
-2016-09-06 10:07:32,160 DEBUG: Done: Getting Results
-2016-09-06 10:07:32,160 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,160 INFO: Done: Result Analysis
-2016-09-06 10:07:32,161 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,161 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:32,161 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:32,162 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,162 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,162 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:32,162 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,195 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:32,195 DEBUG: Start: Training
-2016-09-06 10:07:32,198 DEBUG: Info: Time for Training: 0.0367488861084[s]
-2016-09-06 10:07:32,198 DEBUG: Done: Training
-2016-09-06 10:07:32,198 DEBUG: Start: Predicting
-2016-09-06 10:07:32,199 DEBUG: Done: Predicting
-2016-09-06 10:07:32,199 DEBUG: Start: Getting Results
-2016-09-06 10:07:32,201 DEBUG: Done: Getting Results
-2016-09-06 10:07:32,201 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,201 INFO: Done: Result Analysis
-2016-09-06 10:07:32,202 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,202 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:32,202 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:32,202 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,202 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,202 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:32,203 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,228 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:32,228 DEBUG: Start: Training
-2016-09-06 10:07:32,229 DEBUG: Info: Time for Training: 0.0273449420929[s]
-2016-09-06 10:07:32,229 DEBUG: Done: Training
-2016-09-06 10:07:32,229 DEBUG: Start: Predicting
-2016-09-06 10:07:32,234 DEBUG: Done: Predicting
-2016-09-06 10:07:32,234 DEBUG: Start: Getting Results
-2016-09-06 10:07:32,235 DEBUG: Done: Getting Results
-2016-09-06 10:07:32,235 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,235 INFO: Done: Result Analysis
-2016-09-06 10:07:32,236 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,236 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:32,236 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:32,237 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,237 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,237 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:32,237 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,394 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:32,395 DEBUG: Start: Training
-2016-09-06 10:07:32,416 DEBUG: Info: Time for Training: 0.180504083633[s]
-2016-09-06 10:07:32,417 DEBUG: Done: Training
-2016-09-06 10:07:32,417 DEBUG: Start: Predicting
-2016-09-06 10:07:32,420 DEBUG: Done: Predicting
-2016-09-06 10:07:32,420 DEBUG: Start: Getting Results
-2016-09-06 10:07:32,421 DEBUG: Done: Getting Results
-2016-09-06 10:07:32,421 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,421 INFO: Done: Result Analysis
-2016-09-06 10:07:32,423 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,423 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:32,423 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:32,423 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,423 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,423 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:32,423 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,461 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:32,461 DEBUG: Start: Training
-2016-09-06 10:07:32,462 DEBUG: Info: Time for Training: 0.0399260520935[s]
-2016-09-06 10:07:32,462 DEBUG: Done: Training
-2016-09-06 10:07:32,462 DEBUG: Start: Predicting
-2016-09-06 10:07:32,464 DEBUG: Done: Predicting
-2016-09-06 10:07:32,464 DEBUG: Start: Getting Results
-2016-09-06 10:07:32,465 DEBUG: Done: Getting Results
-2016-09-06 10:07:32,465 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,465 INFO: Done: Result Analysis
-2016-09-06 10:07:32,466 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,466 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:32,466 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:32,467 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,467 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,467 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:32,467 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,512 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:32,512 DEBUG: Start: Training
-2016-09-06 10:07:32,532 DEBUG: Info: Time for Training: 0.0659711360931[s]
-2016-09-06 10:07:32,532 DEBUG: Done: Training
-2016-09-06 10:07:32,532 DEBUG: Start: Predicting
-2016-09-06 10:07:32,535 DEBUG: Done: Predicting
-2016-09-06 10:07:32,535 DEBUG: Start: Getting Results
-2016-09-06 10:07:32,536 DEBUG: Done: Getting Results
-2016-09-06 10:07:32,536 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3802
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,536 INFO: Done: Result Analysis
-2016-09-06 10:07:32,537 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,537 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:32,537 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:32,538 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,538 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,538 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:32,538 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,587 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:32,587 DEBUG: Start: Training
-2016-09-06 10:07:32,607 DEBUG: Info: Time for Training: 0.0699560642242[s]
-2016-09-06 10:07:32,607 DEBUG: Done: Training
-2016-09-06 10:07:32,607 DEBUG: Start: Predicting
-2016-09-06 10:07:32,610 DEBUG: Done: Predicting
-2016-09-06 10:07:32,610 DEBUG: Start: Getting Results
-2016-09-06 10:07:32,612 DEBUG: Done: Getting Results
-2016-09-06 10:07:32,612 INFO: Classification on Fake database for View3 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6378
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,612 INFO: Done: Result Analysis
-2016-09-06 10:07:32,613 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:32,613 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:32,613 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:32,613 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:32,614 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:32,614 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:32,614 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:32,656 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:32,656 DEBUG: Start: Training
-2016-09-06 10:07:32,673 DEBUG: Info: Time for Training: 0.0603799819946[s]
-2016-09-06 10:07:32,673 DEBUG: Done: Training
-2016-09-06 10:07:32,673 DEBUG: Start: Predicting
-2016-09-06 10:07:32,678 DEBUG: Done: Predicting
-2016-09-06 10:07:32,678 DEBUG: Start: Getting Results
-2016-09-06 10:07:32,679 DEBUG: Done: Getting Results
-2016-09-06 10:07:32,679 INFO: Classification on Fake database for View3 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6783
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:32,680 INFO: Done: Result Analysis
-2016-09-06 10:07:32,681 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:07:32,681 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:07:32,682 INFO: Info: Shape of View0 :(300, 11)
-2016-09-06 10:07:32,682 INFO: Info: Shape of View1 :(300, 8)
-2016-09-06 10:07:32,683 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 10:07:32,683 INFO: Info: Shape of View3 :(300, 17)
-2016-09-06 10:07:32,683 INFO: Done: Read Database Files
-2016-09-06 10:07:32,683 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:07:32,687 INFO: Done: Determine validation split
-2016-09-06 10:07:32,687 INFO: Start: Determine 5 folds
-2016-09-06 10:07:32,693 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:07:32,693 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:07:32,693 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:07:32,693 INFO: Done: Determine folds
-2016-09-06 10:07:32,693 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:07:32,693 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 10:07:32,694 DEBUG: Start: Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index eb326c0fc6bcd447955f86b145727dc3e8ff26a9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 13, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ae733e1929e1fed929217346a5778fecd6d24bbe..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 21
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3db0decfd13e4809f32d387c2294101916f6d217..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 67f77df13c82767b09a14984ef010522d76ea256..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.766666666667
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 18, max_depth : 3
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.766666666667
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 20a567ad4268629af76247ce19c513a1bfa0c3d1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 22e8c0747170ca6d854ec7a1b202073c1e824957..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 491
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8c9cc330c2407ddec3757900e68d0d80cda77d7c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2405
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 410684488936523cc786b3cb99cdc3d1af517f50..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100730Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9676
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7029410e61e2defbf0c3d6e0a04793cee95c42a2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 13ea4b2bd1a9c3fba231da153d22d20d29a66e6d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 23
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f008d4bd08a7e52a2022a752cc496953663b8b26..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.628571428571
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e9ecf6a0dde3197db2479f22375921b46affe8bd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 15, max_depth : 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5522a9de8ebabadaf173f22caa10ec161bcdf64c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index da23f07f8bf468a26b1e28e16e93f8c6626438f4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6060
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 112d345dbd4a4d073ff80588f498674bf01c6f82..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 432
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 271f35f0db34c50d7cb1dec55ba9f49b465fa14a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100731Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6005
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dded33adf64dfde2f4a884c90384a58550736d62..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 13882acdefaf4969fb9147557dc0c24b766a78bb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6dd96027caf46c0f007b861206a51669831a460c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 13
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 218c51f12b84c5e7564a76147dee57556114a13d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e4f1a860801220bf4e8171c3580e91ae4c1c1d59..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 96c36fc10a589a4c758c055af91b2630d4224536..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3802
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 371176119cb7a828e922f38ddea2afa8d19977e2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6378
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 824dde8583270a06d7b34ad3e0a4386c6cc6a4f8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100732Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6783
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index d22ae631189680a7633be60c9606007d36dfb195..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100738-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1376 +0,0 @@
-2016-09-06 10:07:38,682 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:07:38,682 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00015915625 Gbytes /!\
-2016-09-06 10:07:43,694 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:07:43,696 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:07:43,766 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:43,766 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:43,767 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:43,767 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:43,768 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:43,768 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:43,768 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:43,768 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:43,768 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:43,768 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:43,769 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:43,769 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:43,769 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:43,769 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:43,808 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:43,808 DEBUG: Start: Training
-2016-09-06 10:07:43,810 DEBUG: Info: Time for Training: 0.0434219837189[s]
-2016-09-06 10:07:43,810 DEBUG: Done: Training
-2016-09-06 10:07:43,810 DEBUG: Start: Predicting
-2016-09-06 10:07:43,813 DEBUG: Done: Predicting
-2016-09-06 10:07:43,813 DEBUG: Start: Getting Results
-2016-09-06 10:07:43,815 DEBUG: Done: Getting Results
-2016-09-06 10:07:43,815 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:43,816 INFO: Done: Result Analysis
-2016-09-06 10:07:43,825 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:43,826 DEBUG: Start: Training
-2016-09-06 10:07:43,831 DEBUG: Info: Time for Training: 0.0656042098999[s]
-2016-09-06 10:07:43,831 DEBUG: Done: Training
-2016-09-06 10:07:43,831 DEBUG: Start: Predicting
-2016-09-06 10:07:43,834 DEBUG: Done: Predicting
-2016-09-06 10:07:43,834 DEBUG: Start: Getting Results
-2016-09-06 10:07:43,836 DEBUG: Done: Getting Results
-2016-09-06 10:07:43,836 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:07:43,836 INFO: Done: Result Analysis
-2016-09-06 10:07:43,916 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:43,916 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:43,916 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:43,916 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:43,916 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:43,916 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:43,917 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:43,917 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:43,917 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:43,917 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:43,917 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:43,917 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:43,918 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:43,918 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:43,951 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:43,951 DEBUG: Start: Training
-2016-09-06 10:07:43,952 DEBUG: Info: Time for Training: 0.0365099906921[s]
-2016-09-06 10:07:43,952 DEBUG: Done: Training
-2016-09-06 10:07:43,952 DEBUG: Start: Predicting
-2016-09-06 10:07:43,958 DEBUG: Done: Predicting
-2016-09-06 10:07:43,959 DEBUG: Start: Getting Results
-2016-09-06 10:07:43,960 DEBUG: Done: Getting Results
-2016-09-06 10:07:43,960 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 32
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:43,960 INFO: Done: Result Analysis
-2016-09-06 10:07:44,207 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:44,207 DEBUG: Start: Training
-2016-09-06 10:07:44,249 DEBUG: Info: Time for Training: 0.333906173706[s]
-2016-09-06 10:07:44,249 DEBUG: Done: Training
-2016-09-06 10:07:44,249 DEBUG: Start: Predicting
-2016-09-06 10:07:44,255 DEBUG: Done: Predicting
-2016-09-06 10:07:44,255 DEBUG: Start: Getting Results
-2016-09-06 10:07:44,257 DEBUG: Done: Getting Results
-2016-09-06 10:07:44,257 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 16, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,257 INFO: Done: Result Analysis
-2016-09-06 10:07:44,364 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,364 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,364 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:44,364 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:44,365 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:44,365 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:44,365 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:44,365 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:44,366 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:44,366 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:44,366 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:44,366 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:44,366 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,366 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,435 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:44,436 DEBUG: Start: Training
-2016-09-06 10:07:44,437 DEBUG: Info: Time for Training: 0.0736479759216[s]
-2016-09-06 10:07:44,437 DEBUG: Done: Training
-2016-09-06 10:07:44,437 DEBUG: Start: Predicting
-2016-09-06 10:07:44,441 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:44,441 DEBUG: Start: Training
-2016-09-06 10:07:44,450 DEBUG: Done: Predicting
-2016-09-06 10:07:44,450 DEBUG: Start: Getting Results
-2016-09-06 10:07:44,453 DEBUG: Done: Getting Results
-2016-09-06 10:07:44,453 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,454 INFO: Done: Result Analysis
-2016-09-06 10:07:44,468 DEBUG: Info: Time for Training: 0.104951143265[s]
-2016-09-06 10:07:44,468 DEBUG: Done: Training
-2016-09-06 10:07:44,468 DEBUG: Start: Predicting
-2016-09-06 10:07:44,472 DEBUG: Done: Predicting
-2016-09-06 10:07:44,472 DEBUG: Start: Getting Results
-2016-09-06 10:07:44,473 DEBUG: Done: Getting Results
-2016-09-06 10:07:44,473 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,473 INFO: Done: Result Analysis
-2016-09-06 10:07:44,609 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,609 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,609 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:44,609 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:44,609 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:44,609 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:44,610 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:44,610 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:44,610 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:07:44,610 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:44,610 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:07:44,610 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,610 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:44,610 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,657 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:44,657 DEBUG: Start: Training
-2016-09-06 10:07:44,663 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:44,663 DEBUG: Start: Training
-2016-09-06 10:07:44,675 DEBUG: Info: Time for Training: 0.066269159317[s]
-2016-09-06 10:07:44,675 DEBUG: Done: Training
-2016-09-06 10:07:44,675 DEBUG: Start: Predicting
-2016-09-06 10:07:44,680 DEBUG: Done: Predicting
-2016-09-06 10:07:44,681 DEBUG: Start: Getting Results
-2016-09-06 10:07:44,682 DEBUG: Info: Time for Training: 0.0731539726257[s]
-2016-09-06 10:07:44,682 DEBUG: Done: Training
-2016-09-06 10:07:44,682 DEBUG: Start: Predicting
-2016-09-06 10:07:44,682 DEBUG: Done: Getting Results
-2016-09-06 10:07:44,682 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,682 INFO: Done: Result Analysis
-2016-09-06 10:07:44,686 DEBUG: Done: Predicting
-2016-09-06 10:07:44,686 DEBUG: Start: Getting Results
-2016-09-06 10:07:44,687 DEBUG: Done: Getting Results
-2016-09-06 10:07:44,687 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,688 INFO: Done: Result Analysis
-2016-09-06 10:07:44,764 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,764 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,764 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:44,764 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:44,764 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:44,764 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:44,766 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:44,766 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:44,766 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:44,766 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:44,766 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:44,766 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:44,766 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,766 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,823 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:44,823 DEBUG: Start: Training
-2016-09-06 10:07:44,826 DEBUG: Info: Time for Training: 0.0639500617981[s]
-2016-09-06 10:07:44,827 DEBUG: Done: Training
-2016-09-06 10:07:44,827 DEBUG: Start: Predicting
-2016-09-06 10:07:44,830 DEBUG: Done: Predicting
-2016-09-06 10:07:44,831 DEBUG: Start: Getting Results
-2016-09-06 10:07:44,832 DEBUG: Done: Getting Results
-2016-09-06 10:07:44,833 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,833 INFO: Done: Result Analysis
-2016-09-06 10:07:44,841 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:44,841 DEBUG: Start: Training
-2016-09-06 10:07:44,845 DEBUG: Info: Time for Training: 0.0828351974487[s]
-2016-09-06 10:07:44,845 DEBUG: Done: Training
-2016-09-06 10:07:44,846 DEBUG: Start: Predicting
-2016-09-06 10:07:44,848 DEBUG: Done: Predicting
-2016-09-06 10:07:44,848 DEBUG: Start: Getting Results
-2016-09-06 10:07:44,850 DEBUG: Done: Getting Results
-2016-09-06 10:07:44,850 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,851 INFO: Done: Result Analysis
-2016-09-06 10:07:44,909 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,910 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:44,910 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:44,910 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:44,910 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:44,910 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:44,911 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:44,911 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:44,911 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:44,912 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:44,912 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:44,912 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,912 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:44,912 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:44,945 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:44,946 DEBUG: Start: Training
-2016-09-06 10:07:44,946 DEBUG: Info: Time for Training: 0.037682056427[s]
-2016-09-06 10:07:44,946 DEBUG: Done: Training
-2016-09-06 10:07:44,946 DEBUG: Start: Predicting
-2016-09-06 10:07:44,953 DEBUG: Done: Predicting
-2016-09-06 10:07:44,954 DEBUG: Start: Getting Results
-2016-09-06 10:07:44,955 DEBUG: Done: Getting Results
-2016-09-06 10:07:44,955 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 32
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:44,955 INFO: Done: Result Analysis
-2016-09-06 10:07:45,240 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:45,240 DEBUG: Start: Training
-2016-09-06 10:07:45,287 DEBUG: Info: Time for Training: 0.378414154053[s]
-2016-09-06 10:07:45,287 DEBUG: Done: Training
-2016-09-06 10:07:45,288 DEBUG: Start: Predicting
-2016-09-06 10:07:45,295 DEBUG: Done: Predicting
-2016-09-06 10:07:45,295 DEBUG: Start: Getting Results
-2016-09-06 10:07:45,296 DEBUG: Done: Getting Results
-2016-09-06 10:07:45,296 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 16, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,296 INFO: Done: Result Analysis
-2016-09-06 10:07:45,357 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,358 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:45,358 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:45,359 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,359 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:45,359 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:45,359 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:45,359 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:45,359 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:45,359 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,360 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:45,360 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:45,360 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:45,360 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,411 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:45,411 DEBUG: Start: Training
-2016-09-06 10:07:45,412 DEBUG: Info: Time for Training: 0.0539691448212[s]
-2016-09-06 10:07:45,412 DEBUG: Done: Training
-2016-09-06 10:07:45,412 DEBUG: Start: Predicting
-2016-09-06 10:07:45,417 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:45,417 DEBUG: Start: Training
-2016-09-06 10:07:45,423 DEBUG: Done: Predicting
-2016-09-06 10:07:45,423 DEBUG: Start: Getting Results
-2016-09-06 10:07:45,426 DEBUG: Done: Getting Results
-2016-09-06 10:07:45,426 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,426 INFO: Done: Result Analysis
-2016-09-06 10:07:45,441 DEBUG: Info: Time for Training: 0.0843439102173[s]
-2016-09-06 10:07:45,441 DEBUG: Done: Training
-2016-09-06 10:07:45,441 DEBUG: Start: Predicting
-2016-09-06 10:07:45,445 DEBUG: Done: Predicting
-2016-09-06 10:07:45,445 DEBUG: Start: Getting Results
-2016-09-06 10:07:45,446 DEBUG: Done: Getting Results
-2016-09-06 10:07:45,446 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,447 INFO: Done: Result Analysis
-2016-09-06 10:07:45,509 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,509 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,509 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:45,509 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:45,510 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:45,510 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:45,510 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:45,510 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:07:45,511 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:45,511 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:07:45,511 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:45,511 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:45,511 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,511 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,559 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:45,559 DEBUG: Start: Training
-2016-09-06 10:07:45,562 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:45,562 DEBUG: Start: Training
-2016-09-06 10:07:45,577 DEBUG: Info: Time for Training: 0.0689029693604[s]
-2016-09-06 10:07:45,577 DEBUG: Done: Training
-2016-09-06 10:07:45,577 DEBUG: Start: Predicting
-2016-09-06 10:07:45,583 DEBUG: Info: Time for Training: 0.0745868682861[s]
-2016-09-06 10:07:45,583 DEBUG: Done: Training
-2016-09-06 10:07:45,583 DEBUG: Start: Predicting
-2016-09-06 10:07:45,583 DEBUG: Done: Predicting
-2016-09-06 10:07:45,583 DEBUG: Start: Getting Results
-2016-09-06 10:07:45,585 DEBUG: Done: Getting Results
-2016-09-06 10:07:45,585 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,585 INFO: Done: Result Analysis
-2016-09-06 10:07:45,587 DEBUG: Done: Predicting
-2016-09-06 10:07:45,587 DEBUG: Start: Getting Results
-2016-09-06 10:07:45,588 DEBUG: Done: Getting Results
-2016-09-06 10:07:45,588 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,589 INFO: Done: Result Analysis
-2016-09-06 10:07:45,663 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,663 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,664 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:45,664 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:45,664 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:45,664 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:45,665 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:45,665 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:45,665 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:45,665 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:45,666 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:45,666 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:45,666 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,666 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,725 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:45,725 DEBUG: Start: Training
-2016-09-06 10:07:45,729 DEBUG: Info: Time for Training: 0.0669369697571[s]
-2016-09-06 10:07:45,729 DEBUG: Done: Training
-2016-09-06 10:07:45,729 DEBUG: Start: Predicting
-2016-09-06 10:07:45,733 DEBUG: Done: Predicting
-2016-09-06 10:07:45,733 DEBUG: Start: Getting Results
-2016-09-06 10:07:45,735 DEBUG: Done: Getting Results
-2016-09-06 10:07:45,735 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,735 INFO: Done: Result Analysis
-2016-09-06 10:07:45,743 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:45,744 DEBUG: Start: Training
-2016-09-06 10:07:45,748 DEBUG: Info: Time for Training: 0.086000919342[s]
-2016-09-06 10:07:45,748 DEBUG: Done: Training
-2016-09-06 10:07:45,748 DEBUG: Start: Predicting
-2016-09-06 10:07:45,751 DEBUG: Done: Predicting
-2016-09-06 10:07:45,751 DEBUG: Start: Getting Results
-2016-09-06 10:07:45,753 DEBUG: Done: Getting Results
-2016-09-06 10:07:45,753 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,754 INFO: Done: Result Analysis
-2016-09-06 10:07:45,915 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,915 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:45,915 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:45,916 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:45,916 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:45,916 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:45,917 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:45,917 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:45,917 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:45,917 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:45,917 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:45,918 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:45,918 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,918 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:45,967 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:45,967 DEBUG: Start: Training
-2016-09-06 10:07:45,968 DEBUG: Info: Time for Training: 0.0544729232788[s]
-2016-09-06 10:07:45,968 DEBUG: Done: Training
-2016-09-06 10:07:45,968 DEBUG: Start: Predicting
-2016-09-06 10:07:45,979 DEBUG: Done: Predicting
-2016-09-06 10:07:45,979 DEBUG: Start: Getting Results
-2016-09-06 10:07:45,982 DEBUG: Done: Getting Results
-2016-09-06 10:07:45,982 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 32
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:45,982 INFO: Done: Result Analysis
-2016-09-06 10:07:46,233 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:46,233 DEBUG: Start: Training
-2016-09-06 10:07:46,276 DEBUG: Info: Time for Training: 0.361407995224[s]
-2016-09-06 10:07:46,276 DEBUG: Done: Training
-2016-09-06 10:07:46,276 DEBUG: Start: Predicting
-2016-09-06 10:07:46,281 DEBUG: Done: Predicting
-2016-09-06 10:07:46,281 DEBUG: Start: Getting Results
-2016-09-06 10:07:46,283 DEBUG: Done: Getting Results
-2016-09-06 10:07:46,283 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 16, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,283 INFO: Done: Result Analysis
-2016-09-06 10:07:46,361 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,361 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:46,361 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:46,362 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,362 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:46,362 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:46,362 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:46,362 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:46,362 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:46,362 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,362 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:46,363 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:46,363 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:46,363 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,413 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:46,414 DEBUG: Start: Training
-2016-09-06 10:07:46,415 DEBUG: Info: Time for Training: 0.0534558296204[s]
-2016-09-06 10:07:46,415 DEBUG: Done: Training
-2016-09-06 10:07:46,415 DEBUG: Start: Predicting
-2016-09-06 10:07:46,416 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:46,416 DEBUG: Start: Training
-2016-09-06 10:07:46,437 DEBUG: Done: Predicting
-2016-09-06 10:07:46,437 DEBUG: Start: Getting Results
-2016-09-06 10:07:46,437 DEBUG: Info: Time for Training: 0.077164888382[s]
-2016-09-06 10:07:46,437 DEBUG: Done: Training
-2016-09-06 10:07:46,438 DEBUG: Start: Predicting
-2016-09-06 10:07:46,438 DEBUG: Done: Getting Results
-2016-09-06 10:07:46,439 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,439 INFO: Done: Result Analysis
-2016-09-06 10:07:46,442 DEBUG: Done: Predicting
-2016-09-06 10:07:46,442 DEBUG: Start: Getting Results
-2016-09-06 10:07:46,443 DEBUG: Done: Getting Results
-2016-09-06 10:07:46,443 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.433333333333
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.433333333333
- - Score on test : 0.377777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,443 INFO: Done: Result Analysis
-2016-09-06 10:07:46,505 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,505 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:07:46,505 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:46,505 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,506 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:07:46,506 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:46,506 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:46,506 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:46,507 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:46,507 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:07:46,507 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,507 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:07:46,507 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:46,507 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,556 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:46,557 DEBUG: Start: Training
-2016-09-06 10:07:46,561 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:46,562 DEBUG: Start: Training
-2016-09-06 10:07:46,575 DEBUG: Info: Time for Training: 0.0704419612885[s]
-2016-09-06 10:07:46,575 DEBUG: Done: Training
-2016-09-06 10:07:46,575 DEBUG: Start: Predicting
-2016-09-06 10:07:46,581 DEBUG: Done: Predicting
-2016-09-06 10:07:46,581 DEBUG: Start: Getting Results
-2016-09-06 10:07:46,583 DEBUG: Info: Time for Training: 0.0788052082062[s]
-2016-09-06 10:07:46,583 DEBUG: Done: Getting Results
-2016-09-06 10:07:46,583 DEBUG: Done: Training
-2016-09-06 10:07:46,583 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,583 DEBUG: Start: Predicting
-2016-09-06 10:07:46,583 INFO: Done: Result Analysis
-2016-09-06 10:07:46,588 DEBUG: Done: Predicting
-2016-09-06 10:07:46,588 DEBUG: Start: Getting Results
-2016-09-06 10:07:46,589 DEBUG: Done: Getting Results
-2016-09-06 10:07:46,589 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,589 INFO: Done: Result Analysis
-2016-09-06 10:07:46,653 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,653 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,654 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:07:46,654 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:07:46,654 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:46,654 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:46,655 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:46,655 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:46,655 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:46,655 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:46,655 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:46,655 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:46,655 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,655 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,709 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:46,709 DEBUG: Start: Training
-2016-09-06 10:07:46,712 DEBUG: Info: Time for Training: 0.0588240623474[s]
-2016-09-06 10:07:46,712 DEBUG: Done: Training
-2016-09-06 10:07:46,712 DEBUG: Start: Predicting
-2016-09-06 10:07:46,715 DEBUG: Done: Predicting
-2016-09-06 10:07:46,716 DEBUG: Start: Getting Results
-2016-09-06 10:07:46,718 DEBUG: Done: Getting Results
-2016-09-06 10:07:46,718 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,718 INFO: Done: Result Analysis
-2016-09-06 10:07:46,727 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:46,727 DEBUG: Start: Training
-2016-09-06 10:07:46,731 DEBUG: Info: Time for Training: 0.0781581401825[s]
-2016-09-06 10:07:46,731 DEBUG: Done: Training
-2016-09-06 10:07:46,731 DEBUG: Start: Predicting
-2016-09-06 10:07:46,734 DEBUG: Done: Predicting
-2016-09-06 10:07:46,734 DEBUG: Start: Getting Results
-2016-09-06 10:07:46,736 DEBUG: Done: Getting Results
-2016-09-06 10:07:46,736 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,736 INFO: Done: Result Analysis
-2016-09-06 10:07:46,802 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,803 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:07:46,802 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:46,803 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:46,803 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:07:46,803 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:46,804 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:46,804 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:46,804 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:46,804 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:46,804 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:46,804 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:46,804 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,804 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:46,837 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:46,837 DEBUG: Start: Training
-2016-09-06 10:07:46,838 DEBUG: Info: Time for Training: 0.0363059043884[s]
-2016-09-06 10:07:46,838 DEBUG: Done: Training
-2016-09-06 10:07:46,838 DEBUG: Start: Predicting
-2016-09-06 10:07:46,844 DEBUG: Done: Predicting
-2016-09-06 10:07:46,845 DEBUG: Start: Getting Results
-2016-09-06 10:07:46,846 DEBUG: Done: Getting Results
-2016-09-06 10:07:46,846 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 32
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:46,846 INFO: Done: Result Analysis
-2016-09-06 10:07:47,123 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:47,123 DEBUG: Start: Training
-2016-09-06 10:07:47,170 DEBUG: Info: Time for Training: 0.368059158325[s]
-2016-09-06 10:07:47,170 DEBUG: Done: Training
-2016-09-06 10:07:47,170 DEBUG: Start: Predicting
-2016-09-06 10:07:47,175 DEBUG: Done: Predicting
-2016-09-06 10:07:47,176 DEBUG: Start: Getting Results
-2016-09-06 10:07:47,177 DEBUG: Done: Getting Results
-2016-09-06 10:07:47,177 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 16, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:07:47,177 INFO: Done: Result Analysis
-2016-09-06 10:07:47,249 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:47,249 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:07:47,249 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:07:47,249 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:07:47,249 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:47,249 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:07:47,250 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:47,250 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:07:47,250 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:47,250 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:07:47,250 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:47,250 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:07:47,250 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:47,251 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:07:47,297 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:47,297 DEBUG: Start: Training
-2016-09-06 10:07:47,298 DEBUG: Info: Time for Training: 0.049859046936[s]
-2016-09-06 10:07:47,298 DEBUG: Done: Training
-2016-09-06 10:07:47,298 DEBUG: Start: Predicting
-2016-09-06 10:07:47,305 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:07:47,305 DEBUG: Start: Training
-2016-09-06 10:07:47,324 DEBUG: Info: Time for Training: 0.0752458572388[s]
-2016-09-06 10:07:47,324 DEBUG: Done: Training
-2016-09-06 10:07:47,324 DEBUG: Start: Predicting
-2016-09-06 10:07:47,325 DEBUG: Done: Predicting
-2016-09-06 10:07:47,325 DEBUG: Start: Getting Results
-2016-09-06 10:07:47,327 DEBUG: Done: Getting Results
-2016-09-06 10:07:47,327 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:07:47,327 INFO: Done: Result Analysis
-2016-09-06 10:07:47,327 DEBUG: Done: Predicting
-2016-09-06 10:07:47,327 DEBUG: Start: Getting Results
-2016-09-06 10:07:47,329 DEBUG: Done: Getting Results
-2016-09-06 10:07:47,329 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:07:47,329 INFO: Done: Result Analysis
-2016-09-06 10:07:47,543 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:07:47,544 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:07:47,544 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:07:47,545 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 10:07:47,545 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:07:47,546 INFO: Info: Shape of View1 :(300, 17)
-2016-09-06 10:07:47,546 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 10:07:47,547 INFO: Info: Shape of View2 :(300, 20)
-2016-09-06 10:07:47,547 INFO: Info: Shape of View1 :(300, 17)
-2016-09-06 10:07:47,547 INFO: Info: Shape of View2 :(300, 20)
-2016-09-06 10:07:47,547 INFO: Info: Shape of View3 :(300, 10)
-2016-09-06 10:07:47,548 INFO: Done: Read Database Files
-2016-09-06 10:07:47,548 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:07:47,548 INFO: Info: Shape of View3 :(300, 10)
-2016-09-06 10:07:47,548 INFO: Done: Read Database Files
-2016-09-06 10:07:47,549 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:07:47,553 INFO: Done: Determine validation split
-2016-09-06 10:07:47,553 INFO: Start: Determine 5 folds
-2016-09-06 10:07:47,553 INFO: Done: Determine validation split
-2016-09-06 10:07:47,553 INFO: Start: Determine 5 folds
-2016-09-06 10:07:47,560 INFO: Info: Length of Learning Sets: 170
-2016-09-06 10:07:47,560 INFO: Info: Length of Testing Sets: 41
-2016-09-06 10:07:47,560 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:07:47,560 INFO: Done: Determine folds
-2016-09-06 10:07:47,560 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:07:47,561 INFO: Start: Classification
-2016-09-06 10:07:47,561 INFO: Info: Length of Learning Sets: 170
-2016-09-06 10:07:47,561 INFO: Start: Fold number 1
-2016-09-06 10:07:47,561 INFO: Info: Length of Testing Sets: 41
-2016-09-06 10:07:47,561 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:07:47,561 INFO: Done: Determine folds
-2016-09-06 10:07:47,561 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:07:47,561 INFO: Start: Classification
-2016-09-06 10:07:47,561 INFO: Start: Fold number 1
-2016-09-06 10:07:47,598 DEBUG: Start: Iteration 1
-2016-09-06 10:07:47,609 DEBUG: View 0 : 0.497175141243
-2016-09-06 10:07:47,616 DEBUG: View 1 : 0.480225988701
-2016-09-06 10:07:47,625 DEBUG: View 2 : 0.570621468927
-2016-09-06 10:07:47,632 DEBUG: View 3 : 0.570621468927
-2016-09-06 10:07:47,672 DEBUG: Best view : View2
-2016-09-06 10:07:47,759 DEBUG: Start: Iteration 2
-2016-09-06 10:07:47,767 DEBUG: View 0 : 0.71186440678
-2016-09-06 10:07:47,778 DEBUG: View 1 : 0.717514124294
-2016-09-06 10:07:47,788 DEBUG: View 2 : 0.706214689266
-2016-09-06 10:07:47,798 DEBUG: View 3 : 0.745762711864
-2016-09-06 10:07:47,844 DEBUG: Best view : View3
-2016-09-06 10:07:48,124 DEBUG: Start: Iteration 3
-2016-09-06 10:07:48,132 DEBUG: View 0 : 0.71186440678
-2016-09-06 10:07:48,140 DEBUG: View 1 : 0.717514124294
-2016-09-06 10:07:48,148 DEBUG: View 2 : 0.706214689266
-2016-09-06 10:07:48,155 DEBUG: View 3 : 0.745762711864
-2016-09-06 10:07:48,196 DEBUG: Best view : View3
-2016-09-06 10:07:48,423 DEBUG: Start: Iteration 4
-2016-09-06 10:07:48,430 DEBUG: View 0 : 0.683615819209
-2016-09-06 10:07:48,438 DEBUG: View 1 : 0.677966101695
-2016-09-06 10:07:48,446 DEBUG: View 2 : 0.666666666667
-2016-09-06 10:07:48,453 DEBUG: View 3 : 0.706214689266
-2016-09-06 10:07:48,497 DEBUG: Best view : View3
-2016-09-06 10:07:48,779 INFO: Start: Classification
-2016-09-06 10:07:49,244 INFO: Done: Fold number 1
-2016-09-06 10:07:49,244 INFO: Start: Fold number 2
-2016-09-06 10:07:49,274 DEBUG: Start: Iteration 1
-2016-09-06 10:07:49,285 DEBUG: View 0 : 0.508287292818
-2016-09-06 10:07:49,301 DEBUG: View 1 : 0.563535911602
-2016-09-06 10:07:49,309 DEBUG: View 2 : 0.497237569061
-2016-09-06 10:07:49,316 DEBUG: View 3 : 0.519337016575
-2016-09-06 10:07:49,348 DEBUG: Best view : View0
-2016-09-06 10:07:49,429 DEBUG: Start: Iteration 2
-2016-09-06 10:07:49,436 DEBUG: View 0 : 0.707182320442
-2016-09-06 10:07:49,444 DEBUG: View 1 : 0.71270718232
-2016-09-06 10:07:49,451 DEBUG: View 2 : 0.723756906077
-2016-09-06 10:07:49,458 DEBUG: View 3 : 0.657458563536
-2016-09-06 10:07:49,497 DEBUG: Best view : View2
-2016-09-06 10:07:49,646 DEBUG: Start: Iteration 3
-2016-09-06 10:07:49,654 DEBUG: View 0 : 0.707182320442
-2016-09-06 10:07:49,661 DEBUG: View 1 : 0.71270718232
-2016-09-06 10:07:49,669 DEBUG: View 2 : 0.723756906077
-2016-09-06 10:07:49,675 DEBUG: View 3 : 0.657458563536
-2016-09-06 10:07:49,717 DEBUG: Best view : View2
-2016-09-06 10:07:49,935 INFO: Start: Classification
-2016-09-06 10:07:50,288 INFO: Done: Fold number 2
-2016-09-06 10:07:50,289 INFO: Start: Fold number 3
-2016-09-06 10:07:50,319 DEBUG: Start: Iteration 1
-2016-09-06 10:07:50,327 DEBUG: View 0 : 0.481081081081
-2016-09-06 10:07:50,334 DEBUG: View 1 : 0.491891891892
-2016-09-06 10:07:50,342 DEBUG: View 2 : 0.47027027027
-2016-09-06 10:07:50,349 DEBUG: View 3 : 0.508108108108
-2016-09-06 10:07:50,382 DEBUG: Best view : View3
-2016-09-06 10:07:50,463 DEBUG: Start: Iteration 2
-2016-09-06 10:07:50,471 DEBUG: View 0 : 0.713513513514
-2016-09-06 10:07:50,478 DEBUG: View 1 : 0.702702702703
-2016-09-06 10:07:50,486 DEBUG: View 2 : 0.675675675676
-2016-09-06 10:07:50,493 DEBUG: View 3 : 0.654054054054
-2016-09-06 10:07:50,533 DEBUG: Best view : View0
-2016-09-06 10:07:50,686 DEBUG: Start: Iteration 3
-2016-09-06 10:07:50,694 DEBUG: View 0 : 0.713513513514
-2016-09-06 10:07:50,701 DEBUG: View 1 : 0.702702702703
-2016-09-06 10:07:50,709 DEBUG: View 2 : 0.675675675676
-2016-09-06 10:07:50,716 DEBUG: View 3 : 0.654054054054
-2016-09-06 10:07:50,759 DEBUG: Best view : View0
-2016-09-06 10:07:50,981 INFO: Start: Classification
-2016-09-06 10:07:51,339 INFO: Done: Fold number 3
-2016-09-06 10:07:51,340 INFO: Start: Fold number 4
-2016-09-06 10:07:51,369 DEBUG: Start: Iteration 1
-2016-09-06 10:07:51,376 DEBUG: View 0 : 0.483333333333
-2016-09-06 10:07:51,382 DEBUG: View 1 : 0.483333333333
-2016-09-06 10:07:51,389 DEBUG: View 2 : 0.483333333333
-2016-09-06 10:07:51,395 DEBUG: View 3 : 0.483333333333
-2016-09-06 10:07:51,396 WARNING: WARNING: All bad for iteration 0
-2016-09-06 10:07:51,428 DEBUG: Best view : View0
-2016-09-06 10:07:51,507 DEBUG: Start: Iteration 2
-2016-09-06 10:07:51,515 DEBUG: View 0 : 0.738888888889
-2016-09-06 10:07:51,522 DEBUG: View 1 : 0.744444444444
-2016-09-06 10:07:51,530 DEBUG: View 2 : 0.761111111111
-2016-09-06 10:07:51,537 DEBUG: View 3 : 0.711111111111
-2016-09-06 10:07:51,575 DEBUG: Best view : View2
-2016-09-06 10:07:51,724 DEBUG: Start: Iteration 3
-2016-09-06 10:07:51,732 DEBUG: View 0 : 0.738888888889
-2016-09-06 10:07:51,739 DEBUG: View 1 : 0.744444444444
-2016-09-06 10:07:51,746 DEBUG: View 2 : 0.761111111111
-2016-09-06 10:07:51,754 DEBUG: View 3 : 0.711111111111
-2016-09-06 10:07:51,795 DEBUG: Best view : View2
-2016-09-06 10:07:52,011 DEBUG: Start: Iteration 4
-2016-09-06 10:07:52,018 DEBUG: View 0 : 0.677777777778
-2016-09-06 10:07:52,026 DEBUG: View 1 : 0.711111111111
-2016-09-06 10:07:52,033 DEBUG: View 2 : 0.694444444444
-2016-09-06 10:07:52,040 DEBUG: View 3 : 0.661111111111
-2016-09-06 10:07:52,084 DEBUG: Best view : View0
-2016-09-06 10:07:52,368 INFO: Start: Classification
-2016-09-06 10:07:52,836 INFO: Done: Fold number 4
-2016-09-06 10:07:52,837 INFO: Start: Fold number 5
-2016-09-06 10:07:52,867 DEBUG: Start: Iteration 1
-2016-09-06 10:07:52,874 DEBUG: View 0 : 0.53591160221
-2016-09-06 10:07:52,881 DEBUG: View 1 : 0.563535911602
-2016-09-06 10:07:52,888 DEBUG: View 2 : 0.552486187845
-2016-09-06 10:07:52,895 DEBUG: View 3 : 0.513812154696
-2016-09-06 10:07:52,929 DEBUG: Best view : View3
-2016-09-06 10:07:53,013 DEBUG: Start: Iteration 2
-2016-09-06 10:07:53,021 DEBUG: View 0 : 0.690607734807
-2016-09-06 10:07:53,029 DEBUG: View 1 : 0.767955801105
-2016-09-06 10:07:53,036 DEBUG: View 2 : 0.696132596685
-2016-09-06 10:07:53,044 DEBUG: View 3 : 0.662983425414
-2016-09-06 10:07:53,084 DEBUG: Best view : View1
-2016-09-06 10:07:53,237 DEBUG: Start: Iteration 3
-2016-09-06 10:07:53,244 DEBUG: View 0 : 0.690607734807
-2016-09-06 10:07:53,252 DEBUG: View 1 : 0.767955801105
-2016-09-06 10:07:53,259 DEBUG: View 2 : 0.696132596685
-2016-09-06 10:07:53,267 DEBUG: View 3 : 0.662983425414
-2016-09-06 10:07:53,311 DEBUG: Best view : View1
-2016-09-06 10:07:53,532 DEBUG: Start: Iteration 4
-2016-09-06 10:07:53,539 DEBUG: View 0 : 0.624309392265
-2016-09-06 10:07:53,547 DEBUG: View 1 : 0.596685082873
-2016-09-06 10:07:53,554 DEBUG: View 2 : 0.723756906077
-2016-09-06 10:07:53,562 DEBUG: View 3 : 0.640883977901
-2016-09-06 10:07:53,606 DEBUG: Best view : View2
-2016-09-06 10:07:53,901 INFO: Start: Classification
-2016-09-06 10:07:54,384 INFO: Done: Fold number 5
-2016-09-06 10:07:54,385 INFO: Done: Classification
-2016-09-06 10:07:54,385 INFO: Info: Time for Classification: 6[s]
-2016-09-06 10:07:54,385 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6e2ae2224a16dd049e4446d63b6405d70a387c83..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5fc348df1e7bb9c2247ada4335e38e97dc5054f4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 10de1910a53aee4e4eabc5cdfa2c04f8813c5175..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100743Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 32
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ecebfbe89e65c68e8fd927a4e2320c5e3cb1073f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3476de3b859c640a3f5529b502e836cc94b2ef27..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 03ccc8e3c417ca714ae780c01bb055ba12b84419..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 32
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e69ac2b1ba6cd9fdcad4efd5202dfd26c0f1f93e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 16, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6743e8cca0afdeb2d257ec57bd6cbcea3871e1bc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9d769aa7607d81bf68ae335cf360638bf3b513c8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6b7bdbacf5b89e95fbed66bbbea6949ae2106ccb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 97799939f6607020b193dd57c58c66d06c096d82..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100744Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b51560b4410c86f618871f07250cc5c018f95e1e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 94513afce48612fc0e66480c41da85f49c291e93..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a44df1d68f37d51362df04e1af77ed5e6d469f2b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 32
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6133fdbfedadd020cda19c650640bbda60f48b34..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 16, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 268dc6e313936df6e79bfa7dbf70a5cd721a2836..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d2acb1806d89c70169a561ee253b0caa48a1f0d6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 57b1b4cd42acf685d28c8f1e6dfabef4d5c4faf9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 622583f8131f042f7f9e6d9ec4f9eba5e86d0206..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100745Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 922eef966ef4b3ce6db161dc995c24b9044f9b22..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4c6b0f496a4077d09f6943b867607f921a2c405b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a96150395083266085a41d98b78ee3a5d068b123..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 32
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6e0fa905be1eafa6afecde10e2ae6dac1fc18745..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 16, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6352e1f5f81c126ab169137ad676f54b2bdb1940..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f9f92f1b306952b3a9bf23d18ec8d9f21af11678..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.433333333333
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.433333333333
- - Score on test : 0.377777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cd43ab9430eb33e254e5d3f44a3eaca3aee30244..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 253cd1cb9be831390be2eed8653a42ca001a54db..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100746Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 34a64102eb6c091efd10e83d3c9e4023d835ad33..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 16, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 54bf6a98c46470824e8a2b1bfc5e7a514ccdf3db..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fb0c6265019d9187e0ff5ac54e9c484ee212abb2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8318
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index ccabbd2f06cc65414b5bf4edf84c1681ae807508..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100834-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1370 +0,0 @@
-2016-09-06 10:08:34,156 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:08:34,156 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00014978125 Gbytes /!\
-2016-09-06 10:08:39,164 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:08:39,166 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:08:39,222 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,222 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,222 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:39,222 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:39,222 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:39,222 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:39,223 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,223 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,223 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,223 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:39,223 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,223 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,223 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:39,223 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,265 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:39,265 DEBUG: Start: Training
-2016-09-06 10:08:39,268 DEBUG: Info: Time for Training: 0.0466451644897[s]
-2016-09-06 10:08:39,268 DEBUG: Done: Training
-2016-09-06 10:08:39,268 DEBUG: Start: Predicting
-2016-09-06 10:08:39,271 DEBUG: Done: Predicting
-2016-09-06 10:08:39,271 DEBUG: Start: Getting Results
-2016-09-06 10:08:39,272 DEBUG: Done: Getting Results
-2016-09-06 10:08:39,272 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,273 INFO: Done: Result Analysis
-2016-09-06 10:08:39,287 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:39,287 DEBUG: Start: Training
-2016-09-06 10:08:39,292 DEBUG: Info: Time for Training: 0.0710310935974[s]
-2016-09-06 10:08:39,292 DEBUG: Done: Training
-2016-09-06 10:08:39,292 DEBUG: Start: Predicting
-2016-09-06 10:08:39,295 DEBUG: Done: Predicting
-2016-09-06 10:08:39,296 DEBUG: Start: Getting Results
-2016-09-06 10:08:39,298 DEBUG: Done: Getting Results
-2016-09-06 10:08:39,298 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,298 INFO: Done: Result Analysis
-2016-09-06 10:08:39,371 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,372 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:39,372 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:39,372 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,372 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:39,372 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:39,372 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,372 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,372 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,373 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:39,373 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,373 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:39,373 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,373 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,410 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:39,410 DEBUG: Start: Training
-2016-09-06 10:08:39,411 DEBUG: Info: Time for Training: 0.039687871933[s]
-2016-09-06 10:08:39,411 DEBUG: Done: Training
-2016-09-06 10:08:39,411 DEBUG: Start: Predicting
-2016-09-06 10:08:39,418 DEBUG: Done: Predicting
-2016-09-06 10:08:39,418 DEBUG: Start: Getting Results
-2016-09-06 10:08:39,420 DEBUG: Done: Getting Results
-2016-09-06 10:08:39,420 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,420 INFO: Done: Result Analysis
-2016-09-06 10:08:39,655 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:39,655 DEBUG: Start: Training
-2016-09-06 10:08:39,694 DEBUG: Info: Time for Training: 0.322620153427[s]
-2016-09-06 10:08:39,694 DEBUG: Done: Training
-2016-09-06 10:08:39,694 DEBUG: Start: Predicting
-2016-09-06 10:08:39,700 DEBUG: Done: Predicting
-2016-09-06 10:08:39,700 DEBUG: Start: Getting Results
-2016-09-06 10:08:39,701 DEBUG: Done: Getting Results
-2016-09-06 10:08:39,701 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 14, max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,702 INFO: Done: Result Analysis
-2016-09-06 10:08:39,821 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,822 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:39,822 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:39,822 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,822 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:39,822 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:39,823 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,823 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,823 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:39,823 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,823 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,823 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,823 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:39,823 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,869 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:39,869 DEBUG: Start: Training
-2016-09-06 10:08:39,870 DEBUG: Info: Time for Training: 0.0494568347931[s]
-2016-09-06 10:08:39,870 DEBUG: Done: Training
-2016-09-06 10:08:39,870 DEBUG: Start: Predicting
-2016-09-06 10:08:39,881 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:39,881 DEBUG: Start: Training
-2016-09-06 10:08:39,897 DEBUG: Done: Predicting
-2016-09-06 10:08:39,897 DEBUG: Start: Getting Results
-2016-09-06 10:08:39,899 DEBUG: Done: Getting Results
-2016-09-06 10:08:39,899 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,899 INFO: Done: Result Analysis
-2016-09-06 10:08:39,903 DEBUG: Info: Time for Training: 0.0822620391846[s]
-2016-09-06 10:08:39,904 DEBUG: Done: Training
-2016-09-06 10:08:39,904 DEBUG: Start: Predicting
-2016-09-06 10:08:39,907 DEBUG: Done: Predicting
-2016-09-06 10:08:39,907 DEBUG: Start: Getting Results
-2016-09-06 10:08:39,908 DEBUG: Done: Getting Results
-2016-09-06 10:08:39,909 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:08:39,909 INFO: Done: Result Analysis
-2016-09-06 10:08:39,972 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,972 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:39,972 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:08:39,972 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:08:39,973 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:39,973 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:39,973 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,973 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:08:39,974 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,974 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:08:39,974 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:39,974 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:39,974 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:39,974 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,022 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:40,022 DEBUG: Start: Training
-2016-09-06 10:08:40,026 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:40,026 DEBUG: Start: Training
-2016-09-06 10:08:40,039 DEBUG: Info: Time for Training: 0.0676848888397[s]
-2016-09-06 10:08:40,039 DEBUG: Done: Training
-2016-09-06 10:08:40,039 DEBUG: Start: Predicting
-2016-09-06 10:08:40,043 DEBUG: Info: Time for Training: 0.0717689990997[s]
-2016-09-06 10:08:40,043 DEBUG: Done: Training
-2016-09-06 10:08:40,043 DEBUG: Start: Predicting
-2016-09-06 10:08:40,045 DEBUG: Done: Predicting
-2016-09-06 10:08:40,045 DEBUG: Start: Getting Results
-2016-09-06 10:08:40,047 DEBUG: Done: Getting Results
-2016-09-06 10:08:40,047 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,047 INFO: Done: Result Analysis
-2016-09-06 10:08:40,047 DEBUG: Done: Predicting
-2016-09-06 10:08:40,047 DEBUG: Start: Getting Results
-2016-09-06 10:08:40,049 DEBUG: Done: Getting Results
-2016-09-06 10:08:40,049 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,049 INFO: Done: Result Analysis
-2016-09-06 10:08:40,117 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,117 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,117 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:40,117 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:40,117 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:40,117 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:40,118 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,118 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,118 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,118 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,118 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:40,118 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:40,118 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,118 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,164 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:40,164 DEBUG: Start: Training
-2016-09-06 10:08:40,167 DEBUG: Info: Time for Training: 0.0504739284515[s]
-2016-09-06 10:08:40,167 DEBUG: Done: Training
-2016-09-06 10:08:40,167 DEBUG: Start: Predicting
-2016-09-06 10:08:40,170 DEBUG: Done: Predicting
-2016-09-06 10:08:40,170 DEBUG: Start: Getting Results
-2016-09-06 10:08:40,171 DEBUG: Done: Getting Results
-2016-09-06 10:08:40,171 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,171 INFO: Done: Result Analysis
-2016-09-06 10:08:40,175 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:40,175 DEBUG: Start: Training
-2016-09-06 10:08:40,180 DEBUG: Info: Time for Training: 0.0639250278473[s]
-2016-09-06 10:08:40,181 DEBUG: Done: Training
-2016-09-06 10:08:40,181 DEBUG: Start: Predicting
-2016-09-06 10:08:40,184 DEBUG: Done: Predicting
-2016-09-06 10:08:40,184 DEBUG: Start: Getting Results
-2016-09-06 10:08:40,186 DEBUG: Done: Getting Results
-2016-09-06 10:08:40,186 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,186 INFO: Done: Result Analysis
-2016-09-06 10:08:40,268 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,268 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,268 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:40,268 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:40,268 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:40,268 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:40,269 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,269 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,269 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,269 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,269 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:40,269 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:40,270 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,270 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,303 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:40,303 DEBUG: Start: Training
-2016-09-06 10:08:40,303 DEBUG: Info: Time for Training: 0.0363190174103[s]
-2016-09-06 10:08:40,303 DEBUG: Done: Training
-2016-09-06 10:08:40,304 DEBUG: Start: Predicting
-2016-09-06 10:08:40,312 DEBUG: Done: Predicting
-2016-09-06 10:08:40,312 DEBUG: Start: Getting Results
-2016-09-06 10:08:40,313 DEBUG: Done: Getting Results
-2016-09-06 10:08:40,313 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,314 INFO: Done: Result Analysis
-2016-09-06 10:08:40,540 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:40,540 DEBUG: Start: Training
-2016-09-06 10:08:40,580 DEBUG: Info: Time for Training: 0.312906980515[s]
-2016-09-06 10:08:40,580 DEBUG: Done: Training
-2016-09-06 10:08:40,580 DEBUG: Start: Predicting
-2016-09-06 10:08:40,586 DEBUG: Done: Predicting
-2016-09-06 10:08:40,586 DEBUG: Start: Getting Results
-2016-09-06 10:08:40,587 DEBUG: Done: Getting Results
-2016-09-06 10:08:40,587 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 14, max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,587 INFO: Done: Result Analysis
-2016-09-06 10:08:40,724 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,724 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,725 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:40,725 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:40,725 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:40,725 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:40,726 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,726 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,726 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,726 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,726 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:40,726 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:40,726 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,726 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,774 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:40,774 DEBUG: Start: Training
-2016-09-06 10:08:40,775 DEBUG: Info: Time for Training: 0.0510659217834[s]
-2016-09-06 10:08:40,775 DEBUG: Done: Training
-2016-09-06 10:08:40,775 DEBUG: Start: Predicting
-2016-09-06 10:08:40,779 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:40,779 DEBUG: Start: Training
-2016-09-06 10:08:40,801 DEBUG: Done: Predicting
-2016-09-06 10:08:40,801 DEBUG: Start: Getting Results
-2016-09-06 10:08:40,803 DEBUG: Done: Getting Results
-2016-09-06 10:08:40,804 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,804 INFO: Done: Result Analysis
-2016-09-06 10:08:40,810 DEBUG: Info: Time for Training: 0.0859551429749[s]
-2016-09-06 10:08:40,810 DEBUG: Done: Training
-2016-09-06 10:08:40,810 DEBUG: Start: Predicting
-2016-09-06 10:08:40,816 DEBUG: Done: Predicting
-2016-09-06 10:08:40,816 DEBUG: Start: Getting Results
-2016-09-06 10:08:40,818 DEBUG: Done: Getting Results
-2016-09-06 10:08:40,818 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:40,818 INFO: Done: Result Analysis
-2016-09-06 10:08:40,981 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,981 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:40,981 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:08:40,981 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:08:40,981 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:40,981 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:40,982 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,982 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:40,982 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,982 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:40,982 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:40,982 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:40,982 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:40,982 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,039 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:41,039 DEBUG: Start: Training
-2016-09-06 10:08:41,041 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:41,041 DEBUG: Start: Training
-2016-09-06 10:08:41,060 DEBUG: Info: Time for Training: 0.0792870521545[s]
-2016-09-06 10:08:41,060 DEBUG: Done: Training
-2016-09-06 10:08:41,060 DEBUG: Start: Predicting
-2016-09-06 10:08:41,062 DEBUG: Info: Time for Training: 0.0816400051117[s]
-2016-09-06 10:08:41,062 DEBUG: Done: Training
-2016-09-06 10:08:41,062 DEBUG: Start: Predicting
-2016-09-06 10:08:41,066 DEBUG: Done: Predicting
-2016-09-06 10:08:41,067 DEBUG: Start: Getting Results
-2016-09-06 10:08:41,067 DEBUG: Done: Predicting
-2016-09-06 10:08:41,067 DEBUG: Start: Getting Results
-2016-09-06 10:08:41,068 DEBUG: Done: Getting Results
-2016-09-06 10:08:41,068 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,068 INFO: Done: Result Analysis
-2016-09-06 10:08:41,069 DEBUG: Done: Getting Results
-2016-09-06 10:08:41,069 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,069 INFO: Done: Result Analysis
-2016-09-06 10:08:41,127 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,128 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:41,128 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:41,128 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,129 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,129 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:41,129 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,129 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,129 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:41,129 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:41,130 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,130 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,130 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:41,130 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,179 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:41,179 DEBUG: Start: Training
-2016-09-06 10:08:41,181 DEBUG: Info: Time for Training: 0.0534510612488[s]
-2016-09-06 10:08:41,182 DEBUG: Done: Training
-2016-09-06 10:08:41,182 DEBUG: Start: Predicting
-2016-09-06 10:08:41,186 DEBUG: Done: Predicting
-2016-09-06 10:08:41,186 DEBUG: Start: Getting Results
-2016-09-06 10:08:41,188 DEBUG: Done: Getting Results
-2016-09-06 10:08:41,188 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,188 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:41,188 DEBUG: Start: Training
-2016-09-06 10:08:41,188 INFO: Done: Result Analysis
-2016-09-06 10:08:41,193 DEBUG: Info: Time for Training: 0.0664410591125[s]
-2016-09-06 10:08:41,193 DEBUG: Done: Training
-2016-09-06 10:08:41,194 DEBUG: Start: Predicting
-2016-09-06 10:08:41,197 DEBUG: Done: Predicting
-2016-09-06 10:08:41,197 DEBUG: Start: Getting Results
-2016-09-06 10:08:41,199 DEBUG: Done: Getting Results
-2016-09-06 10:08:41,199 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,199 INFO: Done: Result Analysis
-2016-09-06 10:08:41,275 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,275 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,276 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:41,276 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:41,276 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:41,276 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:41,276 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,276 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,276 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,276 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,277 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:41,277 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:41,277 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,277 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,312 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:41,312 DEBUG: Start: Training
-2016-09-06 10:08:41,313 DEBUG: Info: Time for Training: 0.0382509231567[s]
-2016-09-06 10:08:41,313 DEBUG: Done: Training
-2016-09-06 10:08:41,313 DEBUG: Start: Predicting
-2016-09-06 10:08:41,320 DEBUG: Done: Predicting
-2016-09-06 10:08:41,321 DEBUG: Start: Getting Results
-2016-09-06 10:08:41,322 DEBUG: Done: Getting Results
-2016-09-06 10:08:41,322 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,322 INFO: Done: Result Analysis
-2016-09-06 10:08:41,539 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:41,539 DEBUG: Start: Training
-2016-09-06 10:08:41,581 DEBUG: Info: Time for Training: 0.306406974792[s]
-2016-09-06 10:08:41,581 DEBUG: Done: Training
-2016-09-06 10:08:41,581 DEBUG: Start: Predicting
-2016-09-06 10:08:41,587 DEBUG: Done: Predicting
-2016-09-06 10:08:41,587 DEBUG: Start: Getting Results
-2016-09-06 10:08:41,589 DEBUG: Done: Getting Results
-2016-09-06 10:08:41,589 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 14, max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,589 INFO: Done: Result Analysis
-2016-09-06 10:08:41,723 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,723 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:41,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:41,723 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:41,723 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:41,724 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,724 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,724 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,724 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,725 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:41,725 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:41,725 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,725 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,775 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:41,775 DEBUG: Start: Training
-2016-09-06 10:08:41,776 DEBUG: Info: Time for Training: 0.0535860061646[s]
-2016-09-06 10:08:41,776 DEBUG: Done: Training
-2016-09-06 10:08:41,776 DEBUG: Start: Predicting
-2016-09-06 10:08:41,780 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:41,780 DEBUG: Start: Training
-2016-09-06 10:08:41,790 DEBUG: Done: Predicting
-2016-09-06 10:08:41,790 DEBUG: Start: Getting Results
-2016-09-06 10:08:41,793 DEBUG: Done: Getting Results
-2016-09-06 10:08:41,793 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,794 INFO: Done: Result Analysis
-2016-09-06 10:08:41,807 DEBUG: Info: Time for Training: 0.0841271877289[s]
-2016-09-06 10:08:41,807 DEBUG: Done: Training
-2016-09-06 10:08:41,807 DEBUG: Start: Predicting
-2016-09-06 10:08:41,810 DEBUG: Done: Predicting
-2016-09-06 10:08:41,810 DEBUG: Start: Getting Results
-2016-09-06 10:08:41,812 DEBUG: Done: Getting Results
-2016-09-06 10:08:41,812 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,812 INFO: Done: Result Analysis
-2016-09-06 10:08:41,874 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,874 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:41,874 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:08:41,874 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:08:41,874 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:41,874 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:41,875 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,875 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:08:41,875 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,875 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:08:41,875 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:41,875 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:41,876 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,876 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:41,927 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:41,927 DEBUG: Start: Training
-2016-09-06 10:08:41,935 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:41,935 DEBUG: Start: Training
-2016-09-06 10:08:41,945 DEBUG: Info: Time for Training: 0.0712940692902[s]
-2016-09-06 10:08:41,945 DEBUG: Done: Training
-2016-09-06 10:08:41,945 DEBUG: Start: Predicting
-2016-09-06 10:08:41,951 DEBUG: Done: Predicting
-2016-09-06 10:08:41,951 DEBUG: Start: Getting Results
-2016-09-06 10:08:41,952 DEBUG: Done: Getting Results
-2016-09-06 10:08:41,952 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,953 INFO: Done: Result Analysis
-2016-09-06 10:08:41,953 DEBUG: Info: Time for Training: 0.079491853714[s]
-2016-09-06 10:08:41,953 DEBUG: Done: Training
-2016-09-06 10:08:41,953 DEBUG: Start: Predicting
-2016-09-06 10:08:41,957 DEBUG: Done: Predicting
-2016-09-06 10:08:41,957 DEBUG: Start: Getting Results
-2016-09-06 10:08:41,959 DEBUG: Done: Getting Results
-2016-09-06 10:08:41,959 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:41,959 INFO: Done: Result Analysis
-2016-09-06 10:08:42,026 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,026 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,026 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:42,026 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:42,027 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:42,027 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:42,027 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,028 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,028 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,028 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,028 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:42,028 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,028 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:42,028 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,066 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:42,066 DEBUG: Start: Training
-2016-09-06 10:08:42,067 DEBUG: Info: Time for Training: 0.0417790412903[s]
-2016-09-06 10:08:42,068 DEBUG: Done: Training
-2016-09-06 10:08:42,068 DEBUG: Start: Predicting
-2016-09-06 10:08:42,071 DEBUG: Done: Predicting
-2016-09-06 10:08:42,071 DEBUG: Start: Getting Results
-2016-09-06 10:08:42,072 DEBUG: Done: Getting Results
-2016-09-06 10:08:42,072 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,073 INFO: Done: Result Analysis
-2016-09-06 10:08:42,084 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:42,084 DEBUG: Start: Training
-2016-09-06 10:08:42,088 DEBUG: Info: Time for Training: 0.0621321201324[s]
-2016-09-06 10:08:42,088 DEBUG: Done: Training
-2016-09-06 10:08:42,088 DEBUG: Start: Predicting
-2016-09-06 10:08:42,091 DEBUG: Done: Predicting
-2016-09-06 10:08:42,091 DEBUG: Start: Getting Results
-2016-09-06 10:08:42,093 DEBUG: Done: Getting Results
-2016-09-06 10:08:42,093 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,093 INFO: Done: Result Analysis
-2016-09-06 10:08:42,174 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,174 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,174 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:42,174 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:42,174 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:42,174 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:42,175 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,175 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,175 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,175 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,175 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:42,175 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:42,175 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,175 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,208 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:42,208 DEBUG: Start: Training
-2016-09-06 10:08:42,209 DEBUG: Info: Time for Training: 0.0357630252838[s]
-2016-09-06 10:08:42,209 DEBUG: Done: Training
-2016-09-06 10:08:42,209 DEBUG: Start: Predicting
-2016-09-06 10:08:42,215 DEBUG: Done: Predicting
-2016-09-06 10:08:42,215 DEBUG: Start: Getting Results
-2016-09-06 10:08:42,216 DEBUG: Done: Getting Results
-2016-09-06 10:08:42,216 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,217 INFO: Done: Result Analysis
-2016-09-06 10:08:42,450 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:42,451 DEBUG: Start: Training
-2016-09-06 10:08:42,492 DEBUG: Info: Time for Training: 0.318718194962[s]
-2016-09-06 10:08:42,492 DEBUG: Done: Training
-2016-09-06 10:08:42,492 DEBUG: Start: Predicting
-2016-09-06 10:08:42,498 DEBUG: Done: Predicting
-2016-09-06 10:08:42,498 DEBUG: Start: Getting Results
-2016-09-06 10:08:42,500 DEBUG: Done: Getting Results
-2016-09-06 10:08:42,500 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 14, max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.980952380952
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,500 INFO: Done: Result Analysis
-2016-09-06 10:08:42,623 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,624 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:42,624 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:42,625 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,625 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,625 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:42,625 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:42,625 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,626 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:42,626 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:42,627 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:08:42,627 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:08:42,627 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:42,627 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:42,673 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:42,674 DEBUG: Start: Training
-2016-09-06 10:08:42,674 DEBUG: Info: Time for Training: 0.0515320301056[s]
-2016-09-06 10:08:42,675 DEBUG: Done: Training
-2016-09-06 10:08:42,675 DEBUG: Start: Predicting
-2016-09-06 10:08:42,682 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:42,683 DEBUG: Start: Training
-2016-09-06 10:08:42,690 DEBUG: Done: Predicting
-2016-09-06 10:08:42,690 DEBUG: Start: Getting Results
-2016-09-06 10:08:42,692 DEBUG: Done: Getting Results
-2016-09-06 10:08:42,692 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,693 INFO: Done: Result Analysis
-2016-09-06 10:08:42,707 DEBUG: Info: Time for Training: 0.0825428962708[s]
-2016-09-06 10:08:42,707 DEBUG: Done: Training
-2016-09-06 10:08:42,707 DEBUG: Start: Predicting
-2016-09-06 10:08:42,710 DEBUG: Done: Predicting
-2016-09-06 10:08:42,710 DEBUG: Start: Getting Results
-2016-09-06 10:08:42,711 DEBUG: Done: Getting Results
-2016-09-06 10:08:42,712 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:42,712 INFO: Done: Result Analysis
-2016-09-06 10:08:42,917 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:08:42,918 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:08:42,919 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 10:08:42,919 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:08:42,920 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:08:42,920 INFO: Info: Shape of View1 :(300, 19)
-2016-09-06 10:08:42,921 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 10:08:42,921 INFO: Info: Shape of View2 :(300, 15)
-2016-09-06 10:08:42,921 INFO: Info: Shape of View1 :(300, 19)
-2016-09-06 10:08:42,922 INFO: Info: Shape of View3 :(300, 9)
-2016-09-06 10:08:42,922 INFO: Done: Read Database Files
-2016-09-06 10:08:42,922 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:08:42,922 INFO: Info: Shape of View2 :(300, 15)
-2016-09-06 10:08:42,923 INFO: Info: Shape of View3 :(300, 9)
-2016-09-06 10:08:42,923 INFO: Done: Read Database Files
-2016-09-06 10:08:42,924 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:08:42,931 INFO: Done: Determine validation split
-2016-09-06 10:08:42,931 INFO: Start: Determine 5 folds
-2016-09-06 10:08:42,932 INFO: Done: Determine validation split
-2016-09-06 10:08:42,932 INFO: Start: Determine 5 folds
-2016-09-06 10:08:42,944 INFO: Info: Length of Learning Sets: 168
-2016-09-06 10:08:42,945 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:08:42,945 INFO: Info: Length of Validation Set: 90
-2016-09-06 10:08:42,945 INFO: Done: Determine folds
-2016-09-06 10:08:42,945 INFO: Info: Length of Learning Sets: 168
-2016-09-06 10:08:42,945 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:08:42,945 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:08:42,945 INFO: Info: Length of Validation Set: 90
-2016-09-06 10:08:42,945 INFO: Start: Classification
-2016-09-06 10:08:42,945 INFO: Done: Determine folds
-2016-09-06 10:08:42,945 INFO: Start: Fold number 1
-2016-09-06 10:08:42,946 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:08:42,946 INFO: Start: Classification
-2016-09-06 10:08:42,946 INFO: Start: Fold number 1
-2016-09-06 10:08:43,005 DEBUG: Start: Iteration 1
-2016-09-06 10:08:43,014 DEBUG: View 0 : 0.463687150838
-2016-09-06 10:08:43,022 DEBUG: View 1 : 0.530726256983
-2016-09-06 10:08:43,029 DEBUG: View 2 : 0.541899441341
-2016-09-06 10:08:43,037 DEBUG: View 3 : 0.513966480447
-2016-09-06 10:08:43,071 DEBUG: Best view : View3
-2016-09-06 10:08:43,154 DEBUG: Start: Iteration 2
-2016-09-06 10:08:43,161 DEBUG: View 0 : 0.653631284916
-2016-09-06 10:08:43,170 DEBUG: View 1 : 0.720670391061
-2016-09-06 10:08:43,178 DEBUG: View 2 : 0.720670391061
-2016-09-06 10:08:43,186 DEBUG: View 3 : 0.698324022346
-2016-09-06 10:08:43,226 DEBUG: Best view : View1
-2016-09-06 10:08:43,378 DEBUG: Start: Iteration 3
-2016-09-06 10:08:43,394 DEBUG: View 0 : 0.653631284916
-2016-09-06 10:08:43,405 DEBUG: View 1 : 0.720670391061
-2016-09-06 10:08:43,413 DEBUG: View 2 : 0.720670391061
-2016-09-06 10:08:43,420 DEBUG: View 3 : 0.698324022346
-2016-09-06 10:08:43,462 DEBUG: Best view : View1
-2016-09-06 10:08:43,721 INFO: Start: Classification
-2016-09-06 10:08:44,142 INFO: Done: Fold number 1
-2016-09-06 10:08:44,143 INFO: Start: Fold number 2
-2016-09-06 10:08:44,175 DEBUG: Start: Iteration 1
-2016-09-06 10:08:44,183 DEBUG: View 0 : 0.530386740331
-2016-09-06 10:08:44,191 DEBUG: View 1 : 0.591160220994
-2016-09-06 10:08:44,199 DEBUG: View 2 : 0.53591160221
-2016-09-06 10:08:44,206 DEBUG: View 3 : 0.491712707182
-2016-09-06 10:08:44,241 DEBUG: Best view : View1
-2016-09-06 10:08:44,326 DEBUG: Start: Iteration 2
-2016-09-06 10:08:44,334 DEBUG: View 0 : 0.718232044199
-2016-09-06 10:08:44,342 DEBUG: View 1 : 0.696132596685
-2016-09-06 10:08:44,350 DEBUG: View 2 : 0.674033149171
-2016-09-06 10:08:44,358 DEBUG: View 3 : 0.635359116022
-2016-09-06 10:08:44,410 DEBUG: Best view : View0
-2016-09-06 10:08:44,581 DEBUG: Start: Iteration 3
-2016-09-06 10:08:44,589 DEBUG: View 0 : 0.718232044199
-2016-09-06 10:08:44,596 DEBUG: View 1 : 0.696132596685
-2016-09-06 10:08:44,604 DEBUG: View 2 : 0.674033149171
-2016-09-06 10:08:44,611 DEBUG: View 3 : 0.635359116022
-2016-09-06 10:08:44,652 DEBUG: Best view : View0
-2016-09-06 10:08:44,871 INFO: Start: Classification
-2016-09-06 10:08:45,230 INFO: Done: Fold number 2
-2016-09-06 10:08:45,230 INFO: Start: Fold number 3
-2016-09-06 10:08:45,259 DEBUG: Start: Iteration 1
-2016-09-06 10:08:45,267 DEBUG: View 0 : 0.505617977528
-2016-09-06 10:08:45,274 DEBUG: View 1 : 0.522471910112
-2016-09-06 10:08:45,281 DEBUG: View 2 : 0.516853932584
-2016-09-06 10:08:45,288 DEBUG: View 3 : 0.52808988764
-2016-09-06 10:08:45,320 DEBUG: Best view : View1
-2016-09-06 10:08:45,399 DEBUG: Start: Iteration 2
-2016-09-06 10:08:45,406 DEBUG: View 0 : 0.629213483146
-2016-09-06 10:08:45,414 DEBUG: View 1 : 0.724719101124
-2016-09-06 10:08:45,421 DEBUG: View 2 : 0.691011235955
-2016-09-06 10:08:45,429 DEBUG: View 3 : 0.629213483146
-2016-09-06 10:08:45,467 DEBUG: Best view : View1
-2016-09-06 10:08:45,616 DEBUG: Start: Iteration 3
-2016-09-06 10:08:45,624 DEBUG: View 0 : 0.629213483146
-2016-09-06 10:08:45,631 DEBUG: View 1 : 0.724719101124
-2016-09-06 10:08:45,639 DEBUG: View 2 : 0.691011235955
-2016-09-06 10:08:45,646 DEBUG: View 3 : 0.629213483146
-2016-09-06 10:08:45,686 DEBUG: Best view : View1
-2016-09-06 10:08:45,910 DEBUG: Start: Iteration 4
-2016-09-06 10:08:45,918 DEBUG: View 0 : 0.623595505618
-2016-09-06 10:08:45,926 DEBUG: View 1 : 0.662921348315
-2016-09-06 10:08:45,934 DEBUG: View 2 : 0.651685393258
-2016-09-06 10:08:45,942 DEBUG: View 3 : 0.623595505618
-2016-09-06 10:08:45,986 DEBUG: Best view : View0
-2016-09-06 10:08:46,283 INFO: Start: Classification
-2016-09-06 10:08:46,944 INFO: Done: Fold number 3
-2016-09-06 10:08:46,944 INFO: Start: Fold number 4
-2016-09-06 10:08:46,983 DEBUG: Start: Iteration 1
-2016-09-06 10:08:46,993 DEBUG: View 0 : 0.56043956044
-2016-09-06 10:08:47,001 DEBUG: View 1 : 0.521978021978
-2016-09-06 10:08:47,010 DEBUG: View 2 : 0.554945054945
-2016-09-06 10:08:47,018 DEBUG: View 3 : 0.521978021978
-2016-09-06 10:08:47,055 DEBUG: Best view : View1
-2016-09-06 10:08:47,142 DEBUG: Start: Iteration 2
-2016-09-06 10:08:47,150 DEBUG: View 0 : 0.67032967033
-2016-09-06 10:08:47,158 DEBUG: View 1 : 0.736263736264
-2016-09-06 10:08:47,167 DEBUG: View 2 : 0.681318681319
-2016-09-06 10:08:47,175 DEBUG: View 3 : 0.714285714286
-2016-09-06 10:08:47,218 DEBUG: Best view : View1
-2016-09-06 10:08:47,372 DEBUG: Start: Iteration 3
-2016-09-06 10:08:47,380 DEBUG: View 0 : 0.67032967033
-2016-09-06 10:08:47,388 DEBUG: View 1 : 0.736263736264
-2016-09-06 10:08:47,395 DEBUG: View 2 : 0.681318681319
-2016-09-06 10:08:47,402 DEBUG: View 3 : 0.714285714286
-2016-09-06 10:08:47,445 DEBUG: Best view : View1
-2016-09-06 10:08:47,666 DEBUG: Start: Iteration 4
-2016-09-06 10:08:47,674 DEBUG: View 0 : 0.549450549451
-2016-09-06 10:08:47,682 DEBUG: View 1 : 0.763736263736
-2016-09-06 10:08:47,690 DEBUG: View 2 : 0.598901098901
-2016-09-06 10:08:47,697 DEBUG: View 3 : 0.637362637363
-2016-09-06 10:08:47,741 DEBUG: Best view : View1
-2016-09-06 10:08:48,048 INFO: Start: Classification
-2016-09-06 10:08:48,536 INFO: Done: Fold number 4
-2016-09-06 10:08:48,536 INFO: Start: Fold number 5
-2016-09-06 10:08:48,569 DEBUG: Start: Iteration 1
-2016-09-06 10:08:48,577 DEBUG: View 0 : 0.491712707182
-2016-09-06 10:08:48,584 DEBUG: View 1 : 0.491712707182
-2016-09-06 10:08:48,594 DEBUG: View 2 : 0.491712707182
-2016-09-06 10:08:48,606 DEBUG: View 3 : 0.491712707182
-2016-09-06 10:08:48,606 WARNING: WARNING: All bad for iteration 0
-2016-09-06 10:08:48,657 DEBUG: Best view : View0
-2016-09-06 10:08:48,759 DEBUG: Start: Iteration 2
-2016-09-06 10:08:48,767 DEBUG: View 0 : 0.651933701657
-2016-09-06 10:08:48,777 DEBUG: View 1 : 0.646408839779
-2016-09-06 10:08:48,789 DEBUG: View 2 : 0.662983425414
-2016-09-06 10:08:48,803 DEBUG: View 3 : 0.591160220994
-2016-09-06 10:08:48,867 DEBUG: Best view : View2
-2016-09-06 10:08:49,051 DEBUG: Start: Iteration 3
-2016-09-06 10:08:49,059 DEBUG: View 0 : 0.651933701657
-2016-09-06 10:08:49,068 DEBUG: View 1 : 0.646408839779
-2016-09-06 10:08:49,077 DEBUG: View 2 : 0.662983425414
-2016-09-06 10:08:49,085 DEBUG: View 3 : 0.591160220994
-2016-09-06 10:08:49,135 DEBUG: Best view : View2
-2016-09-06 10:08:49,386 INFO: Start: Classification
-2016-09-06 10:08:49,796 INFO: Done: Fold number 5
-2016-09-06 10:08:49,796 INFO: Done: Classification
-2016-09-06 10:08:49,796 INFO: Info: Time for Classification: 6[s]
-2016-09-06 10:08:49,796 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ddea2b4fd39bb6c4c2ed80705d636b25beadb050..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e1d148804213849085da1aad85b70d19bf641ce0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 87eef941fba29b1e81351b75aca71e8b7f56c161..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 57e7471b5e7e2aae0d180ea8b3846ca91aa80232..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 14, max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cbd7b4f18687c43ea0be068574094b718ecce10f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b91d1651b9be39d33df68268f06916acc1a1c4fa..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100839Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d413cf8788eac148163d882e40a015d2ffebd79c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c7edd45ac0c7f27aa769f2b69822dcf8ffc91f9f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d0be9dd2d9652aa66eba0f8d4bf645b7a06a335d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 20848fbcef792a8f17937d0e92496645b82a553f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 14, max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0da527101bbbf8d9166eebb0292bd0caf9a415ab..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d1c231bdd58b9af485d57d92f22b0a4c6b9d218a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1d1835abba7246701b78e9d33af4f9a38922da80..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3fd73c43b060e332169de31ecc2dab0adec0d7c2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100840Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 45bc125e72aaaf1f3981e925df55124e7ac96a84..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e933492b16bc199a5b6fd23e87752c75e57f360e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 30c528bc06c44cbd5c61ce2c679a9c24515e989f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b47c31af98d014c1fc237c271700817666145c83..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 14, max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7e8ec93402628b196bbcf64625e7344d5b008d41..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ea49c27fc2bbb6252f777a1cd8aa779e86a2762..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ac93c75fdce8c35872239a20aec519bdb8b1cfcd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 47e6566c7f7e441a25d33f0d9b72a57613494038..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100841Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 77221a3d959b55d9ff0b4531881776d371bb87ee..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e0f6b680f800db194027d6c94788cf62282a9bfe..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 917a2d1cbb0ac34c5491ca033dde9fad7b64eadf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 84b2e632565bc27430a792444f0a2d46da20265d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.980952380952
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 14, max_depth : 28
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.980952380952
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0fabbd85581ca3c41f967ff2673e1965fd6ffe67..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ba1f9386cc8241bec5d15a96e570be70dbe559c0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100842Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.509523809524
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7580
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 47a28cd4aca7bb1731207a34e8af2fdbee230354..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100853-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1363 +0,0 @@
-2016-09-06 10:08:53,165 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:08:53,165 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.0001193125 Gbytes /!\
-2016-09-06 10:08:58,177 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:08:58,179 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:08:58,234 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,234 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,234 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:58,234 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:58,234 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:58,234 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:58,235 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,235 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,235 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,235 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,235 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:58,235 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:58,235 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,235 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,274 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:58,274 DEBUG: Start: Training
-2016-09-06 10:08:58,276 DEBUG: Info: Time for Training: 0.0428988933563[s]
-2016-09-06 10:08:58,276 DEBUG: Done: Training
-2016-09-06 10:08:58,276 DEBUG: Start: Predicting
-2016-09-06 10:08:58,279 DEBUG: Done: Predicting
-2016-09-06 10:08:58,279 DEBUG: Start: Getting Results
-2016-09-06 10:08:58,280 DEBUG: Done: Getting Results
-2016-09-06 10:08:58,280 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,281 INFO: Done: Result Analysis
-2016-09-06 10:08:58,288 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:58,288 DEBUG: Start: Training
-2016-09-06 10:08:58,293 DEBUG: Info: Time for Training: 0.0592339038849[s]
-2016-09-06 10:08:58,293 DEBUG: Done: Training
-2016-09-06 10:08:58,293 DEBUG: Start: Predicting
-2016-09-06 10:08:58,295 DEBUG: Done: Predicting
-2016-09-06 10:08:58,296 DEBUG: Start: Getting Results
-2016-09-06 10:08:58,298 DEBUG: Done: Getting Results
-2016-09-06 10:08:58,298 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,298 INFO: Done: Result Analysis
-2016-09-06 10:08:58,378 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,378 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,379 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:58,379 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:58,379 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:58,379 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:58,380 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,380 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,380 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,380 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,380 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:58,380 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:58,380 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,380 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,418 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:58,418 DEBUG: Start: Training
-2016-09-06 10:08:58,419 DEBUG: Info: Time for Training: 0.0408041477203[s]
-2016-09-06 10:08:58,419 DEBUG: Done: Training
-2016-09-06 10:08:58,419 DEBUG: Start: Predicting
-2016-09-06 10:08:58,426 DEBUG: Done: Predicting
-2016-09-06 10:08:58,426 DEBUG: Start: Getting Results
-2016-09-06 10:08:58,428 DEBUG: Done: Getting Results
-2016-09-06 10:08:58,428 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 35
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,428 INFO: Done: Result Analysis
-2016-09-06 10:08:58,469 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:58,469 DEBUG: Start: Training
-2016-09-06 10:08:58,478 DEBUG: Info: Time for Training: 0.0997009277344[s]
-2016-09-06 10:08:58,478 DEBUG: Done: Training
-2016-09-06 10:08:58,478 DEBUG: Start: Predicting
-2016-09-06 10:08:58,481 DEBUG: Done: Predicting
-2016-09-06 10:08:58,481 DEBUG: Start: Getting Results
-2016-09-06 10:08:58,483 DEBUG: Done: Getting Results
-2016-09-06 10:08:58,483 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,483 INFO: Done: Result Analysis
-2016-09-06 10:08:58,633 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,633 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,634 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:58,634 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:58,634 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:58,634 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:58,635 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,635 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,635 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,635 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,635 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:58,635 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:58,635 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,635 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,686 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:58,686 DEBUG: Start: Training
-2016-09-06 10:08:58,687 DEBUG: Info: Time for Training: 0.0547561645508[s]
-2016-09-06 10:08:58,687 DEBUG: Done: Training
-2016-09-06 10:08:58,687 DEBUG: Start: Predicting
-2016-09-06 10:08:58,692 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:58,692 DEBUG: Start: Training
-2016-09-06 10:08:58,713 DEBUG: Info: Time for Training: 0.0805191993713[s]
-2016-09-06 10:08:58,713 DEBUG: Done: Training
-2016-09-06 10:08:58,713 DEBUG: Done: Predicting
-2016-09-06 10:08:58,713 DEBUG: Start: Predicting
-2016-09-06 10:08:58,713 DEBUG: Start: Getting Results
-2016-09-06 10:08:58,714 DEBUG: Done: Getting Results
-2016-09-06 10:08:58,715 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.661904761905
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,715 INFO: Done: Result Analysis
-2016-09-06 10:08:58,717 DEBUG: Done: Predicting
-2016-09-06 10:08:58,717 DEBUG: Start: Getting Results
-2016-09-06 10:08:58,718 DEBUG: Done: Getting Results
-2016-09-06 10:08:58,719 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,719 INFO: Done: Result Analysis
-2016-09-06 10:08:58,777 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,777 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,777 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:08:58,777 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:58,777 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:08:58,777 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:58,778 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,778 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:08:58,778 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,778 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:08:58,778 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:58,778 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:58,778 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,778 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,829 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:58,829 DEBUG: Start: Training
-2016-09-06 10:08:58,838 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:58,838 DEBUG: Start: Training
-2016-09-06 10:08:58,847 DEBUG: Info: Time for Training: 0.0710601806641[s]
-2016-09-06 10:08:58,847 DEBUG: Done: Training
-2016-09-06 10:08:58,847 DEBUG: Start: Predicting
-2016-09-06 10:08:58,854 DEBUG: Done: Predicting
-2016-09-06 10:08:58,854 DEBUG: Start: Getting Results
-2016-09-06 10:08:58,855 DEBUG: Done: Getting Results
-2016-09-06 10:08:58,855 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,856 INFO: Done: Result Analysis
-2016-09-06 10:08:58,861 DEBUG: Info: Time for Training: 0.0853440761566[s]
-2016-09-06 10:08:58,861 DEBUG: Done: Training
-2016-09-06 10:08:58,862 DEBUG: Start: Predicting
-2016-09-06 10:08:58,866 DEBUG: Done: Predicting
-2016-09-06 10:08:58,866 DEBUG: Start: Getting Results
-2016-09-06 10:08:58,867 DEBUG: Done: Getting Results
-2016-09-06 10:08:58,867 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,867 INFO: Done: Result Analysis
-2016-09-06 10:08:58,923 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,923 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:58,923 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:58,924 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:58,923 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:58,924 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:58,924 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:58,924 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:58,924 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,924 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:58,925 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:58,925 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:58,925 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:58,925 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:58,960 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:58,961 DEBUG: Start: Training
-2016-09-06 10:08:58,963 DEBUG: Info: Time for Training: 0.0409200191498[s]
-2016-09-06 10:08:58,963 DEBUG: Done: Training
-2016-09-06 10:08:58,963 DEBUG: Start: Predicting
-2016-09-06 10:08:58,966 DEBUG: Done: Predicting
-2016-09-06 10:08:58,966 DEBUG: Start: Getting Results
-2016-09-06 10:08:58,967 DEBUG: Done: Getting Results
-2016-09-06 10:08:58,967 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,968 INFO: Done: Result Analysis
-2016-09-06 10:08:58,981 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:58,981 DEBUG: Start: Training
-2016-09-06 10:08:58,986 DEBUG: Info: Time for Training: 0.0627229213715[s]
-2016-09-06 10:08:58,986 DEBUG: Done: Training
-2016-09-06 10:08:58,986 DEBUG: Start: Predicting
-2016-09-06 10:08:58,989 DEBUG: Done: Predicting
-2016-09-06 10:08:58,989 DEBUG: Start: Getting Results
-2016-09-06 10:08:58,992 DEBUG: Done: Getting Results
-2016-09-06 10:08:58,992 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:58,992 INFO: Done: Result Analysis
-2016-09-06 10:08:59,074 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,074 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,075 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:59,075 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:59,075 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:59,075 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:59,075 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,075 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,076 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,076 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,076 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:59,076 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:59,076 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,076 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,108 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:59,108 DEBUG: Start: Training
-2016-09-06 10:08:59,109 DEBUG: Info: Time for Training: 0.0350739955902[s]
-2016-09-06 10:08:59,109 DEBUG: Done: Training
-2016-09-06 10:08:59,109 DEBUG: Start: Predicting
-2016-09-06 10:08:59,116 DEBUG: Done: Predicting
-2016-09-06 10:08:59,116 DEBUG: Start: Getting Results
-2016-09-06 10:08:59,117 DEBUG: Done: Getting Results
-2016-09-06 10:08:59,117 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 35
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,117 INFO: Done: Result Analysis
-2016-09-06 10:08:59,170 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:59,170 DEBUG: Start: Training
-2016-09-06 10:08:59,178 DEBUG: Info: Time for Training: 0.10413312912[s]
-2016-09-06 10:08:59,178 DEBUG: Done: Training
-2016-09-06 10:08:59,178 DEBUG: Start: Predicting
-2016-09-06 10:08:59,182 DEBUG: Done: Predicting
-2016-09-06 10:08:59,182 DEBUG: Start: Getting Results
-2016-09-06 10:08:59,184 DEBUG: Done: Getting Results
-2016-09-06 10:08:59,184 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,184 INFO: Done: Result Analysis
-2016-09-06 10:08:59,324 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,324 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,324 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:08:59,324 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:08:59,325 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:59,325 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:59,326 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,326 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,326 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,326 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,326 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:59,326 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:59,326 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,326 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,396 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:59,396 DEBUG: Start: Training
-2016-09-06 10:08:59,397 DEBUG: Info: Time for Training: 0.074168920517[s]
-2016-09-06 10:08:59,397 DEBUG: Done: Training
-2016-09-06 10:08:59,398 DEBUG: Start: Predicting
-2016-09-06 10:08:59,403 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:59,403 DEBUG: Start: Training
-2016-09-06 10:08:59,421 DEBUG: Done: Predicting
-2016-09-06 10:08:59,421 DEBUG: Start: Getting Results
-2016-09-06 10:08:59,424 DEBUG: Done: Getting Results
-2016-09-06 10:08:59,424 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,424 INFO: Done: Result Analysis
-2016-09-06 10:08:59,431 DEBUG: Info: Time for Training: 0.107607841492[s]
-2016-09-06 10:08:59,431 DEBUG: Done: Training
-2016-09-06 10:08:59,431 DEBUG: Start: Predicting
-2016-09-06 10:08:59,434 DEBUG: Done: Predicting
-2016-09-06 10:08:59,435 DEBUG: Start: Getting Results
-2016-09-06 10:08:59,436 DEBUG: Done: Getting Results
-2016-09-06 10:08:59,436 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,436 INFO: Done: Result Analysis
-2016-09-06 10:08:59,571 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,571 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,571 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:08:59,571 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:08:59,571 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:59,571 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:59,571 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,571 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:08:59,572 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,572 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:08:59,572 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:59,572 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:59,572 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,572 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,620 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:59,620 DEBUG: Start: Training
-2016-09-06 10:08:59,624 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:59,624 DEBUG: Start: Training
-2016-09-06 10:08:59,637 DEBUG: Info: Time for Training: 0.0667371749878[s]
-2016-09-06 10:08:59,637 DEBUG: Done: Training
-2016-09-06 10:08:59,637 DEBUG: Start: Predicting
-2016-09-06 10:08:59,643 DEBUG: Done: Predicting
-2016-09-06 10:08:59,643 DEBUG: Start: Getting Results
-2016-09-06 10:08:59,643 DEBUG: Info: Time for Training: 0.0726878643036[s]
-2016-09-06 10:08:59,643 DEBUG: Done: Training
-2016-09-06 10:08:59,643 DEBUG: Start: Predicting
-2016-09-06 10:08:59,644 DEBUG: Done: Getting Results
-2016-09-06 10:08:59,644 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,645 INFO: Done: Result Analysis
-2016-09-06 10:08:59,647 DEBUG: Done: Predicting
-2016-09-06 10:08:59,647 DEBUG: Start: Getting Results
-2016-09-06 10:08:59,649 DEBUG: Done: Getting Results
-2016-09-06 10:08:59,649 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,649 INFO: Done: Result Analysis
-2016-09-06 10:08:59,723 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,723 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:08:59,723 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:59,724 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,724 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:08:59,724 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:59,724 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:08:59,724 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:08:59,724 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:59,725 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:08:59,725 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,725 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:08:59,725 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:59,725 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,756 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:59,756 DEBUG: Start: Training
-2016-09-06 10:08:59,757 DEBUG: Info: Time for Training: 0.0341780185699[s]
-2016-09-06 10:08:59,757 DEBUG: Done: Training
-2016-09-06 10:08:59,757 DEBUG: Start: Predicting
-2016-09-06 10:08:59,760 DEBUG: Done: Predicting
-2016-09-06 10:08:59,760 DEBUG: Start: Getting Results
-2016-09-06 10:08:59,761 DEBUG: Done: Getting Results
-2016-09-06 10:08:59,761 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.344444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.344444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,762 INFO: Done: Result Analysis
-2016-09-06 10:08:59,771 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:59,771 DEBUG: Start: Training
-2016-09-06 10:08:59,774 DEBUG: Info: Time for Training: 0.0514109134674[s]
-2016-09-06 10:08:59,774 DEBUG: Done: Training
-2016-09-06 10:08:59,774 DEBUG: Start: Predicting
-2016-09-06 10:08:59,777 DEBUG: Done: Predicting
-2016-09-06 10:08:59,777 DEBUG: Start: Getting Results
-2016-09-06 10:08:59,779 DEBUG: Done: Getting Results
-2016-09-06 10:08:59,779 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.366666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.366666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,779 INFO: Done: Result Analysis
-2016-09-06 10:08:59,875 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,875 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:08:59,876 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:08:59,876 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:08:59,877 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:59,877 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:08:59,878 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:08:59,878 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:08:59,878 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:08:59,878 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:08:59,878 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:59,878 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:08:59,878 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,878 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:08:59,909 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:59,910 DEBUG: Start: Training
-2016-09-06 10:08:59,910 DEBUG: Info: Time for Training: 0.0356111526489[s]
-2016-09-06 10:08:59,910 DEBUG: Done: Training
-2016-09-06 10:08:59,910 DEBUG: Start: Predicting
-2016-09-06 10:08:59,916 DEBUG: Done: Predicting
-2016-09-06 10:08:59,916 DEBUG: Start: Getting Results
-2016-09-06 10:08:59,918 DEBUG: Done: Getting Results
-2016-09-06 10:08:59,918 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 35
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,918 INFO: Done: Result Analysis
-2016-09-06 10:08:59,962 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:08:59,962 DEBUG: Start: Training
-2016-09-06 10:08:59,971 DEBUG: Info: Time for Training: 0.0960199832916[s]
-2016-09-06 10:08:59,971 DEBUG: Done: Training
-2016-09-06 10:08:59,971 DEBUG: Start: Predicting
-2016-09-06 10:08:59,974 DEBUG: Done: Predicting
-2016-09-06 10:08:59,974 DEBUG: Start: Getting Results
-2016-09-06 10:08:59,975 DEBUG: Done: Getting Results
-2016-09-06 10:08:59,976 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:08:59,976 INFO: Done: Result Analysis
-2016-09-06 10:09:00,126 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,126 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,127 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:09:00,127 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:09:00,127 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:09:00,127 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:09:00,128 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,128 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,128 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,128 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,128 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:09:00,128 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:09:00,128 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,128 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,198 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:09:00,198 DEBUG: Start: Training
-2016-09-06 10:09:00,199 DEBUG: Info: Time for Training: 0.073979139328[s]
-2016-09-06 10:09:00,199 DEBUG: Done: Training
-2016-09-06 10:09:00,199 DEBUG: Start: Predicting
-2016-09-06 10:09:00,201 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:09:00,201 DEBUG: Start: Training
-2016-09-06 10:09:00,217 DEBUG: Done: Predicting
-2016-09-06 10:09:00,217 DEBUG: Start: Getting Results
-2016-09-06 10:09:00,217 DEBUG: Info: Time for Training: 0.09197306633[s]
-2016-09-06 10:09:00,217 DEBUG: Done: Training
-2016-09-06 10:09:00,217 DEBUG: Start: Predicting
-2016-09-06 10:09:00,219 DEBUG: Done: Getting Results
-2016-09-06 10:09:00,219 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,219 INFO: Done: Result Analysis
-2016-09-06 10:09:00,220 DEBUG: Done: Predicting
-2016-09-06 10:09:00,220 DEBUG: Start: Getting Results
-2016-09-06 10:09:00,221 DEBUG: Done: Getting Results
-2016-09-06 10:09:00,222 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,222 INFO: Done: Result Analysis
-2016-09-06 10:09:00,375 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,375 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,376 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:09:00,376 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:09:00,376 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:09:00,376 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:09:00,377 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,377 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,377 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,377 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,378 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:09:00,378 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:09:00,378 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,378 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,452 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:09:00,452 DEBUG: Start: Training
-2016-09-06 10:09:00,454 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:09:00,454 DEBUG: Start: Training
-2016-09-06 10:09:00,472 DEBUG: Info: Time for Training: 0.0980281829834[s]
-2016-09-06 10:09:00,472 DEBUG: Done: Training
-2016-09-06 10:09:00,472 DEBUG: Start: Predicting
-2016-09-06 10:09:00,474 DEBUG: Info: Time for Training: 0.100123167038[s]
-2016-09-06 10:09:00,474 DEBUG: Done: Training
-2016-09-06 10:09:00,474 DEBUG: Start: Predicting
-2016-09-06 10:09:00,475 DEBUG: Done: Predicting
-2016-09-06 10:09:00,475 DEBUG: Start: Getting Results
-2016-09-06 10:09:00,477 DEBUG: Done: Getting Results
-2016-09-06 10:09:00,477 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,477 INFO: Done: Result Analysis
-2016-09-06 10:09:00,479 DEBUG: Done: Predicting
-2016-09-06 10:09:00,479 DEBUG: Start: Getting Results
-2016-09-06 10:09:00,481 DEBUG: Done: Getting Results
-2016-09-06 10:09:00,481 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,481 INFO: Done: Result Analysis
-2016-09-06 10:09:00,627 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,627 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,627 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:09:00,627 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:09:00,627 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:09:00,627 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:09:00,628 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,628 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,629 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,629 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,629 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:09:00,629 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:09:00,629 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,629 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,678 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:09:00,679 DEBUG: Start: Training
-2016-09-06 10:09:00,680 DEBUG: Info: Time for Training: 0.054713010788[s]
-2016-09-06 10:09:00,681 DEBUG: Done: Training
-2016-09-06 10:09:00,681 DEBUG: Start: Predicting
-2016-09-06 10:09:00,684 DEBUG: Done: Predicting
-2016-09-06 10:09:00,684 DEBUG: Start: Getting Results
-2016-09-06 10:09:00,687 DEBUG: Done: Getting Results
-2016-09-06 10:09:00,687 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,687 INFO: Done: Result Analysis
-2016-09-06 10:09:00,696 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:09:00,696 DEBUG: Start: Training
-2016-09-06 10:09:00,700 DEBUG: Info: Time for Training: 0.0740790367126[s]
-2016-09-06 10:09:00,700 DEBUG: Done: Training
-2016-09-06 10:09:00,700 DEBUG: Start: Predicting
-2016-09-06 10:09:00,703 DEBUG: Done: Predicting
-2016-09-06 10:09:00,703 DEBUG: Start: Getting Results
-2016-09-06 10:09:00,705 DEBUG: Done: Getting Results
-2016-09-06 10:09:00,705 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,705 INFO: Done: Result Analysis
-2016-09-06 10:09:00,775 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,775 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:00,775 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:09:00,775 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:09:00,775 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:09:00,775 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:09:00,776 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,776 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:00,776 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,776 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:00,776 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:09:00,776 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:09:00,777 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,777 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:09:00,824 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:09:00,824 DEBUG: Start: Training
-2016-09-06 10:09:00,825 DEBUG: Info: Time for Training: 0.0511150360107[s]
-2016-09-06 10:09:00,825 DEBUG: Done: Training
-2016-09-06 10:09:00,825 DEBUG: Start: Predicting
-2016-09-06 10:09:00,834 DEBUG: Done: Predicting
-2016-09-06 10:09:00,834 DEBUG: Start: Getting Results
-2016-09-06 10:09:00,836 DEBUG: Done: Getting Results
-2016-09-06 10:09:00,836 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 35
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,836 INFO: Done: Result Analysis
-2016-09-06 10:09:00,882 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:09:00,882 DEBUG: Start: Training
-2016-09-06 10:09:00,891 DEBUG: Info: Time for Training: 0.116482019424[s]
-2016-09-06 10:09:00,891 DEBUG: Done: Training
-2016-09-06 10:09:00,891 DEBUG: Start: Predicting
-2016-09-06 10:09:00,894 DEBUG: Done: Predicting
-2016-09-06 10:09:00,894 DEBUG: Start: Getting Results
-2016-09-06 10:09:00,895 DEBUG: Done: Getting Results
-2016-09-06 10:09:00,895 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.355555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.355555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:09:00,896 INFO: Done: Result Analysis
-2016-09-06 10:09:01,024 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:01,024 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:09:01,025 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:09:01,025 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:09:01,025 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:09:01,025 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:09:01,026 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:01,026 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:09:01,026 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:01,026 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:09:01,026 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:09:01,026 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:09:01,026 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:09:01,026 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:09:01,095 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:09:01,095 DEBUG: Start: Training
-2016-09-06 10:09:01,096 DEBUG: Info: Time for Training: 0.072772026062[s]
-2016-09-06 10:09:01,096 DEBUG: Done: Training
-2016-09-06 10:09:01,096 DEBUG: Start: Predicting
-2016-09-06 10:09:01,098 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:09:01,098 DEBUG: Start: Training
-2016-09-06 10:09:01,109 DEBUG: Done: Predicting
-2016-09-06 10:09:01,110 DEBUG: Start: Getting Results
-2016-09-06 10:09:01,112 DEBUG: Done: Getting Results
-2016-09-06 10:09:01,112 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:09:01,112 INFO: Done: Result Analysis
-2016-09-06 10:09:01,118 DEBUG: Info: Time for Training: 0.0949590206146[s]
-2016-09-06 10:09:01,119 DEBUG: Done: Training
-2016-09-06 10:09:01,119 DEBUG: Start: Predicting
-2016-09-06 10:09:01,123 DEBUG: Done: Predicting
-2016-09-06 10:09:01,123 DEBUG: Start: Getting Results
-2016-09-06 10:09:01,124 DEBUG: Done: Getting Results
-2016-09-06 10:09:01,125 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:09:01,125 INFO: Done: Result Analysis
-2016-09-06 10:09:01,423 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:09:01,424 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:09:01,425 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 10:09:01,425 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:09:01,425 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:09:01,426 INFO: Info: Shape of View1 :(300, 14)
-2016-09-06 10:09:01,426 INFO: Info: Shape of View0 :(300, 19)
-2016-09-06 10:09:01,426 INFO: Info: Shape of View2 :(300, 5)
-2016-09-06 10:09:01,427 INFO: Info: Shape of View1 :(300, 14)
-2016-09-06 10:09:01,427 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 10:09:01,427 INFO: Done: Read Database Files
-2016-09-06 10:09:01,428 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:09:01,428 INFO: Info: Shape of View2 :(300, 5)
-2016-09-06 10:09:01,429 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 10:09:01,429 INFO: Done: Read Database Files
-2016-09-06 10:09:01,429 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:09:01,436 INFO: Done: Determine validation split
-2016-09-06 10:09:01,436 INFO: Start: Determine 5 folds
-2016-09-06 10:09:01,436 INFO: Done: Determine validation split
-2016-09-06 10:09:01,437 INFO: Start: Determine 5 folds
-2016-09-06 10:09:01,450 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:09:01,450 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:09:01,450 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:09:01,450 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:09:01,450 INFO: Done: Determine folds
-2016-09-06 10:09:01,450 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:09:01,450 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:09:01,450 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:09:01,450 INFO: Start: Classification
-2016-09-06 10:09:01,450 INFO: Done: Determine folds
-2016-09-06 10:09:01,451 INFO: Start: Fold number 1
-2016-09-06 10:09:01,451 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:09:01,451 INFO: Start: Classification
-2016-09-06 10:09:01,451 INFO: Start: Fold number 1
-2016-09-06 10:09:01,514 DEBUG: Start: Iteration 1
-2016-09-06 10:09:01,528 DEBUG: View 0 : 0.433333333333
-2016-09-06 10:09:01,541 DEBUG: View 1 : 0.577777777778
-2016-09-06 10:09:01,554 DEBUG: View 2 : 0.444444444444
-2016-09-06 10:09:01,566 DEBUG: View 3 : 0.433333333333
-2016-09-06 10:09:01,621 DEBUG: Best view : View0
-2016-09-06 10:09:01,751 DEBUG: Start: Iteration 2
-2016-09-06 10:09:01,765 DEBUG: View 0 : 0.75
-2016-09-06 10:09:01,779 DEBUG: View 1 : 0.622222222222
-2016-09-06 10:09:01,792 DEBUG: View 2 : 0.711111111111
-2016-09-06 10:09:01,805 DEBUG: View 3 : 0.683333333333
-2016-09-06 10:09:01,851 DEBUG: Best view : View0
-2016-09-06 10:09:02,004 DEBUG: Start: Iteration 3
-2016-09-06 10:09:02,013 DEBUG: View 0 : 0.75
-2016-09-06 10:09:02,022 DEBUG: View 1 : 0.622222222222
-2016-09-06 10:09:02,030 DEBUG: View 2 : 0.711111111111
-2016-09-06 10:09:02,037 DEBUG: View 3 : 0.683333333333
-2016-09-06 10:09:02,077 DEBUG: Best view : View0
-2016-09-06 10:09:02,295 DEBUG: Start: Iteration 4
-2016-09-06 10:09:02,303 DEBUG: View 0 : 0.738888888889
-2016-09-06 10:09:02,310 DEBUG: View 1 : 0.666666666667
-2016-09-06 10:09:02,317 DEBUG: View 2 : 0.677777777778
-2016-09-06 10:09:02,324 DEBUG: View 3 : 0.666666666667
-2016-09-06 10:09:02,367 DEBUG: Best view : View0
-2016-09-06 10:09:02,653 INFO: Start: Classification
-2016-09-06 10:09:03,124 INFO: Done: Fold number 1
-2016-09-06 10:09:03,124 INFO: Start: Fold number 2
-2016-09-06 10:09:03,155 DEBUG: Start: Iteration 1
-2016-09-06 10:09:03,162 DEBUG: View 0 : 0.472826086957
-2016-09-06 10:09:03,169 DEBUG: View 1 : 0.559782608696
-2016-09-06 10:09:03,176 DEBUG: View 2 : 0.527173913043
-2016-09-06 10:09:03,183 DEBUG: View 3 : 0.570652173913
-2016-09-06 10:09:03,216 DEBUG: Best view : View2
-2016-09-06 10:09:03,297 DEBUG: Start: Iteration 2
-2016-09-06 10:09:03,305 DEBUG: View 0 : 0.695652173913
-2016-09-06 10:09:03,313 DEBUG: View 1 : 0.625
-2016-09-06 10:09:03,320 DEBUG: View 2 : 0.657608695652
-2016-09-06 10:09:03,327 DEBUG: View 3 : 0.657608695652
-2016-09-06 10:09:03,366 DEBUG: Best view : View0
-2016-09-06 10:09:03,518 DEBUG: Start: Iteration 3
-2016-09-06 10:09:03,526 DEBUG: View 0 : 0.695652173913
-2016-09-06 10:09:03,533 DEBUG: View 1 : 0.625
-2016-09-06 10:09:03,540 DEBUG: View 2 : 0.657608695652
-2016-09-06 10:09:03,548 DEBUG: View 3 : 0.657608695652
-2016-09-06 10:09:03,589 DEBUG: Best view : View0
-2016-09-06 10:09:03,812 INFO: Start: Classification
-2016-09-06 10:09:04,173 INFO: Done: Fold number 2
-2016-09-06 10:09:04,173 INFO: Start: Fold number 3
-2016-09-06 10:09:04,204 DEBUG: Start: Iteration 1
-2016-09-06 10:09:04,211 DEBUG: View 0 : 0.513812154696
-2016-09-06 10:09:04,218 DEBUG: View 1 : 0.480662983425
-2016-09-06 10:09:04,225 DEBUG: View 2 : 0.441988950276
-2016-09-06 10:09:04,232 DEBUG: View 3 : 0.453038674033
-2016-09-06 10:09:04,264 DEBUG: Best view : View0
-2016-09-06 10:09:04,345 DEBUG: Start: Iteration 2
-2016-09-06 10:09:04,353 DEBUG: View 0 : 0.685082872928
-2016-09-06 10:09:04,360 DEBUG: View 1 : 0.662983425414
-2016-09-06 10:09:04,367 DEBUG: View 2 : 0.685082872928
-2016-09-06 10:09:04,374 DEBUG: View 3 : 0.707182320442
-2016-09-06 10:09:04,413 DEBUG: Best view : View3
-2016-09-06 10:09:04,562 DEBUG: Start: Iteration 3
-2016-09-06 10:09:04,569 DEBUG: View 0 : 0.685082872928
-2016-09-06 10:09:04,577 DEBUG: View 1 : 0.662983425414
-2016-09-06 10:09:04,584 DEBUG: View 2 : 0.685082872928
-2016-09-06 10:09:04,591 DEBUG: View 3 : 0.707182320442
-2016-09-06 10:09:04,631 DEBUG: Best view : View3
-2016-09-06 10:09:04,849 INFO: Start: Classification
-2016-09-06 10:09:05,203 INFO: Done: Fold number 3
-2016-09-06 10:09:05,203 INFO: Start: Fold number 4
-2016-09-06 10:09:05,235 DEBUG: Start: Iteration 1
-2016-09-06 10:09:05,242 DEBUG: View 0 : 0.491891891892
-2016-09-06 10:09:05,249 DEBUG: View 1 : 0.47027027027
-2016-09-06 10:09:05,256 DEBUG: View 2 : 0.491891891892
-2016-09-06 10:09:05,263 DEBUG: View 3 : 0.513513513514
-2016-09-06 10:09:05,296 DEBUG: Best view : View1
-2016-09-06 10:09:05,378 DEBUG: Start: Iteration 2
-2016-09-06 10:09:05,385 DEBUG: View 0 : 0.675675675676
-2016-09-06 10:09:05,393 DEBUG: View 1 : 0.686486486486
-2016-09-06 10:09:05,400 DEBUG: View 2 : 0.681081081081
-2016-09-06 10:09:05,407 DEBUG: View 3 : 0.686486486486
-2016-09-06 10:09:05,447 DEBUG: Best view : View1
-2016-09-06 10:09:05,600 DEBUG: Start: Iteration 3
-2016-09-06 10:09:05,608 DEBUG: View 0 : 0.675675675676
-2016-09-06 10:09:05,615 DEBUG: View 1 : 0.686486486486
-2016-09-06 10:09:05,622 DEBUG: View 2 : 0.681081081081
-2016-09-06 10:09:05,629 DEBUG: View 3 : 0.686486486486
-2016-09-06 10:09:05,671 DEBUG: Best view : View1
-2016-09-06 10:09:05,894 INFO: Start: Classification
-2016-09-06 10:09:06,257 INFO: Done: Fold number 4
-2016-09-06 10:09:06,257 INFO: Start: Fold number 5
-2016-09-06 10:09:06,287 DEBUG: Start: Iteration 1
-2016-09-06 10:09:06,295 DEBUG: View 0 : 0.538888888889
-2016-09-06 10:09:06,302 DEBUG: View 1 : 0.461111111111
-2016-09-06 10:09:06,309 DEBUG: View 2 : 0.511111111111
-2016-09-06 10:09:06,316 DEBUG: View 3 : 0.561111111111
-2016-09-06 10:09:06,348 DEBUG: Best view : View0
-2016-09-06 10:09:06,428 DEBUG: Start: Iteration 2
-2016-09-06 10:09:06,435 DEBUG: View 0 : 0.722222222222
-2016-09-06 10:09:06,443 DEBUG: View 1 : 0.638888888889
-2016-09-06 10:09:06,450 DEBUG: View 2 : 0.644444444444
-2016-09-06 10:09:06,457 DEBUG: View 3 : 0.611111111111
-2016-09-06 10:09:06,496 DEBUG: Best view : View0
-2016-09-06 10:09:06,646 DEBUG: Start: Iteration 3
-2016-09-06 10:09:06,654 DEBUG: View 0 : 0.722222222222
-2016-09-06 10:09:06,662 DEBUG: View 1 : 0.638888888889
-2016-09-06 10:09:06,669 DEBUG: View 2 : 0.644444444444
-2016-09-06 10:09:06,676 DEBUG: View 3 : 0.572222222222
-2016-09-06 10:09:06,717 DEBUG: Best view : View0
-2016-09-06 10:09:06,935 INFO: Start: Classification
-2016-09-06 10:09:07,291 INFO: Done: Fold number 5
-2016-09-06 10:09:07,291 INFO: Done: Classification
-2016-09-06 10:09:07,291 INFO: Info: Time for Classification: 5[s]
-2016-09-06 10:09:07,291 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5340a4ef639fe2995cf7705c0bf13deb6ab05e8e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 401659eecc6936ba15b4ca39f423576b42d93da6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2b148b8aad4023703cabd1448ac4cf01ddff3e3d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 35
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4370daf7770a9f32ca076844c5b91efdf55d95d8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0f779050eebcc976235b4a74528946db0de884c5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.661904761905
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bbd4f050e2e82ae726b176ef3492cb13f291679b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ea7ee5a0f2fea10f2251cf4f5711561b6b76c594..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 238e7f55e34780d1fa64e10dfb522beb89dab71a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100858Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a919948fddde516c41a22bfff839e855ebd97954..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.366666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.366666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 53b3252bcec8b29327bce446534f0943bba7c4f2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.344444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.344444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 71631804fdd66539c6a272b5e240ce601ee90301..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 35
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ffc954ff50d55ecf9782cbc87c84301216342d5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4a7d3a5e0bd2bfb31c94c971e62e0132c392a503..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 848051bce5bd883118230d62c21e8aa6752b65b9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f77cf58d354c088c8b04133fdb11208c6dde3730..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2282cfc96c6a5e1797718634aaf009f744935826..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100859Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7e19a21ff2758f9872633375f16819a3bff5fa94..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0cc72c434373182c1701b24f83794b048379580f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ad6d2c4fcbbfbbacf012c70de103999c16d45682..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 35
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 02ce1d849a422da19ca504d71d46b408bd4b85db..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.355555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.355555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 013f27903cb4bfc07ba1c9b9687821054bc84f44..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2c1b8cde16e70359a02061a983f424c51a9af7de..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 52bd8c30fa9e7afb48ecea23c87f86e96e960b0e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 291885b02da0a103e9096dea90eb4cba10351cdb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100900Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 722f6d1c680c217c27a42f057ee8f87bf1d7f286..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 36d5aa61f82bfa35c91c4ee0c386352a79cc9fd3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-100901Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 830
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 17ec1f4723b259b9197d17aa811170c3d9005928..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101330-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1375 +0,0 @@
-2016-09-06 10:13:30,568 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:13:30,568 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000133375 Gbytes /!\
-2016-09-06 10:13:35,578 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:13:35,581 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:13:35,639 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:35,640 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:35,640 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:13:35,640 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:13:35,640 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:35,640 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:35,641 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:35,641 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:35,641 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:35,641 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:35,641 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:35,641 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:35,641 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:35,641 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:35,678 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:35,678 DEBUG: Start: Training
-2016-09-06 10:13:35,680 DEBUG: Info: Time for Training: 0.0411851406097[s]
-2016-09-06 10:13:35,680 DEBUG: Done: Training
-2016-09-06 10:13:35,680 DEBUG: Start: Predicting
-2016-09-06 10:13:35,683 DEBUG: Done: Predicting
-2016-09-06 10:13:35,683 DEBUG: Start: Getting Results
-2016-09-06 10:13:35,684 DEBUG: Done: Getting Results
-2016-09-06 10:13:35,684 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:13:35,685 INFO: Done: Result Analysis
-2016-09-06 10:13:35,692 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:35,692 DEBUG: Start: Training
-2016-09-06 10:13:35,696 DEBUG: Info: Time for Training: 0.0574531555176[s]
-2016-09-06 10:13:35,696 DEBUG: Done: Training
-2016-09-06 10:13:35,696 DEBUG: Start: Predicting
-2016-09-06 10:13:35,699 DEBUG: Done: Predicting
-2016-09-06 10:13:35,700 DEBUG: Start: Getting Results
-2016-09-06 10:13:35,701 DEBUG: Done: Getting Results
-2016-09-06 10:13:35,701 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:13:35,702 INFO: Done: Result Analysis
-2016-09-06 10:13:35,786 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:35,786 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:35,786 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:13:35,786 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:35,786 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:13:35,786 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:35,787 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:35,787 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:35,787 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:35,787 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:35,787 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:35,788 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:35,788 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:35,788 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:35,820 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:35,820 DEBUG: Start: Training
-2016-09-06 10:13:35,821 DEBUG: Info: Time for Training: 0.0358920097351[s]
-2016-09-06 10:13:35,821 DEBUG: Done: Training
-2016-09-06 10:13:35,821 DEBUG: Start: Predicting
-2016-09-06 10:13:35,828 DEBUG: Done: Predicting
-2016-09-06 10:13:35,829 DEBUG: Start: Getting Results
-2016-09-06 10:13:35,831 DEBUG: Done: Getting Results
-2016-09-06 10:13:35,831 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:13:35,831 INFO: Done: Result Analysis
-2016-09-06 10:13:35,871 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:35,871 DEBUG: Start: Training
-2016-09-06 10:13:35,880 DEBUG: Info: Time for Training: 0.094918012619[s]
-2016-09-06 10:13:35,880 DEBUG: Done: Training
-2016-09-06 10:13:35,880 DEBUG: Start: Predicting
-2016-09-06 10:13:35,883 DEBUG: Done: Predicting
-2016-09-06 10:13:35,883 DEBUG: Start: Getting Results
-2016-09-06 10:13:35,885 DEBUG: Done: Getting Results
-2016-09-06 10:13:35,885 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:13:35,885 INFO: Done: Result Analysis
-2016-09-06 10:13:36,032 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,032 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,032 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:13:36,032 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:13:36,033 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,033 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,033 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:36,033 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:36,033 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:36,033 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:36,033 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,033 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,034 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,034 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,079 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,079 DEBUG: Start: Training
-2016-09-06 10:13:36,080 DEBUG: Info: Time for Training: 0.0481998920441[s]
-2016-09-06 10:13:36,080 DEBUG: Done: Training
-2016-09-06 10:13:36,080 DEBUG: Start: Predicting
-2016-09-06 10:13:36,084 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,084 DEBUG: Start: Training
-2016-09-06 10:13:36,102 DEBUG: Info: Time for Training: 0.070839881897[s]
-2016-09-06 10:13:36,103 DEBUG: Done: Training
-2016-09-06 10:13:36,103 DEBUG: Start: Predicting
-2016-09-06 10:13:36,106 DEBUG: Done: Predicting
-2016-09-06 10:13:36,106 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,108 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,108 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,108 INFO: Done: Result Analysis
-2016-09-06 10:13:36,110 DEBUG: Done: Predicting
-2016-09-06 10:13:36,111 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,114 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,114 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,115 INFO: Done: Result Analysis
-2016-09-06 10:13:36,177 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,177 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,178 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:13:36,178 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:13:36,178 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,178 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,178 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:36,178 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:13:36,179 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:36,179 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:13:36,179 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,179 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,179 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,179 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,233 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,233 DEBUG: Start: Training
-2016-09-06 10:13:36,235 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,236 DEBUG: Start: Training
-2016-09-06 10:13:36,250 DEBUG: Info: Time for Training: 0.0732500553131[s]
-2016-09-06 10:13:36,250 DEBUG: Done: Training
-2016-09-06 10:13:36,250 DEBUG: Start: Predicting
-2016-09-06 10:13:36,254 DEBUG: Info: Time for Training: 0.0775668621063[s]
-2016-09-06 10:13:36,254 DEBUG: Done: Training
-2016-09-06 10:13:36,255 DEBUG: Start: Predicting
-2016-09-06 10:13:36,256 DEBUG: Done: Predicting
-2016-09-06 10:13:36,257 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,258 DEBUG: Done: Predicting
-2016-09-06 10:13:36,258 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,259 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,259 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,259 INFO: Done: Result Analysis
-2016-09-06 10:13:36,260 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,260 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,261 INFO: Done: Result Analysis
-2016-09-06 10:13:36,328 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,328 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:13:36,329 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,330 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,330 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,330 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,330 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,330 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,331 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:13:36,331 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,331 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,332 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,332 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,332 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,372 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,372 DEBUG: Start: Training
-2016-09-06 10:13:36,374 DEBUG: Info: Time for Training: 0.0452740192413[s]
-2016-09-06 10:13:36,375 DEBUG: Done: Training
-2016-09-06 10:13:36,375 DEBUG: Start: Predicting
-2016-09-06 10:13:36,378 DEBUG: Done: Predicting
-2016-09-06 10:13:36,378 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,380 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,380 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,381 INFO: Done: Result Analysis
-2016-09-06 10:13:36,383 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,383 DEBUG: Start: Training
-2016-09-06 10:13:36,387 DEBUG: Info: Time for Training: 0.0596179962158[s]
-2016-09-06 10:13:36,387 DEBUG: Done: Training
-2016-09-06 10:13:36,387 DEBUG: Start: Predicting
-2016-09-06 10:13:36,390 DEBUG: Done: Predicting
-2016-09-06 10:13:36,390 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,392 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,392 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,392 INFO: Done: Result Analysis
-2016-09-06 10:13:36,471 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,471 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,471 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:13:36,471 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:13:36,471 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,471 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,472 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,472 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,472 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,472 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,472 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,472 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,472 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,472 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,505 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,505 DEBUG: Start: Training
-2016-09-06 10:13:36,506 DEBUG: Info: Time for Training: 0.0356941223145[s]
-2016-09-06 10:13:36,506 DEBUG: Done: Training
-2016-09-06 10:13:36,506 DEBUG: Start: Predicting
-2016-09-06 10:13:36,513 DEBUG: Done: Predicting
-2016-09-06 10:13:36,513 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,515 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,515 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,515 INFO: Done: Result Analysis
-2016-09-06 10:13:36,557 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,557 DEBUG: Start: Training
-2016-09-06 10:13:36,565 DEBUG: Info: Time for Training: 0.0949969291687[s]
-2016-09-06 10:13:36,565 DEBUG: Done: Training
-2016-09-06 10:13:36,566 DEBUG: Start: Predicting
-2016-09-06 10:13:36,569 DEBUG: Done: Predicting
-2016-09-06 10:13:36,569 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,570 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,571 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.904761904762
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.904761904762
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,571 INFO: Done: Result Analysis
-2016-09-06 10:13:36,718 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,718 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,718 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:13:36,718 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:13:36,718 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,718 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,719 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,719 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,719 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,719 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,719 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,719 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,719 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,719 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,771 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,771 DEBUG: Start: Training
-2016-09-06 10:13:36,779 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,779 DEBUG: Start: Training
-2016-09-06 10:13:36,780 DEBUG: Info: Time for Training: 0.063157081604[s]
-2016-09-06 10:13:36,780 DEBUG: Done: Training
-2016-09-06 10:13:36,781 DEBUG: Start: Predicting
-2016-09-06 10:13:36,792 DEBUG: Info: Time for Training: 0.0748438835144[s]
-2016-09-06 10:13:36,792 DEBUG: Done: Training
-2016-09-06 10:13:36,792 DEBUG: Start: Predicting
-2016-09-06 10:13:36,796 DEBUG: Done: Predicting
-2016-09-06 10:13:36,796 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,797 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,797 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,797 INFO: Done: Result Analysis
-2016-09-06 10:13:36,801 DEBUG: Done: Predicting
-2016-09-06 10:13:36,801 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,804 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,804 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,804 INFO: Done: Result Analysis
-2016-09-06 10:13:36,869 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,870 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:13:36,870 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,870 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:36,870 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:13:36,870 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:36,871 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,871 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,871 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,871 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,872 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:13:36,872 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:13:36,872 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:36,872 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:36,918 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,919 DEBUG: Start: Training
-2016-09-06 10:13:36,930 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:36,930 DEBUG: Start: Training
-2016-09-06 10:13:36,935 DEBUG: Info: Time for Training: 0.0670058727264[s]
-2016-09-06 10:13:36,936 DEBUG: Done: Training
-2016-09-06 10:13:36,936 DEBUG: Start: Predicting
-2016-09-06 10:13:36,941 DEBUG: Done: Predicting
-2016-09-06 10:13:36,941 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,943 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,943 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,943 INFO: Done: Result Analysis
-2016-09-06 10:13:36,947 DEBUG: Info: Time for Training: 0.0786368846893[s]
-2016-09-06 10:13:36,947 DEBUG: Done: Training
-2016-09-06 10:13:36,947 DEBUG: Start: Predicting
-2016-09-06 10:13:36,952 DEBUG: Done: Predicting
-2016-09-06 10:13:36,952 DEBUG: Start: Getting Results
-2016-09-06 10:13:36,954 DEBUG: Done: Getting Results
-2016-09-06 10:13:36,954 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:36,954 INFO: Done: Result Analysis
-2016-09-06 10:13:37,012 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,012 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,013 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:13:37,013 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:13:37,013 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,013 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,013 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,013 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,014 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,014 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,014 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,014 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,014 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,014 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,046 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,046 DEBUG: Start: Training
-2016-09-06 10:13:37,048 DEBUG: Info: Time for Training: 0.035749912262[s]
-2016-09-06 10:13:37,048 DEBUG: Done: Training
-2016-09-06 10:13:37,048 DEBUG: Start: Predicting
-2016-09-06 10:13:37,051 DEBUG: Done: Predicting
-2016-09-06 10:13:37,051 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,052 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,052 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,052 INFO: Done: Result Analysis
-2016-09-06 10:13:37,061 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,061 DEBUG: Start: Training
-2016-09-06 10:13:37,064 DEBUG: Info: Time for Training: 0.0523209571838[s]
-2016-09-06 10:13:37,064 DEBUG: Done: Training
-2016-09-06 10:13:37,065 DEBUG: Start: Predicting
-2016-09-06 10:13:37,067 DEBUG: Done: Predicting
-2016-09-06 10:13:37,068 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,069 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,070 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,078 INFO: Done: Result Analysis
-2016-09-06 10:13:37,160 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,160 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,161 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:13:37,161 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:13:37,162 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,162 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,163 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,163 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,163 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,163 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,163 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,163 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,163 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,163 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,195 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,195 DEBUG: Start: Training
-2016-09-06 10:13:37,196 DEBUG: Info: Time for Training: 0.0357730388641[s]
-2016-09-06 10:13:37,196 DEBUG: Done: Training
-2016-09-06 10:13:37,196 DEBUG: Start: Predicting
-2016-09-06 10:13:37,202 DEBUG: Done: Predicting
-2016-09-06 10:13:37,202 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,204 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,204 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,204 INFO: Done: Result Analysis
-2016-09-06 10:13:37,251 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,252 DEBUG: Start: Training
-2016-09-06 10:13:37,260 DEBUG: Info: Time for Training: 0.0996959209442[s]
-2016-09-06 10:13:37,260 DEBUG: Done: Training
-2016-09-06 10:13:37,260 DEBUG: Start: Predicting
-2016-09-06 10:13:37,263 DEBUG: Done: Predicting
-2016-09-06 10:13:37,263 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,265 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,265 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,265 INFO: Done: Result Analysis
-2016-09-06 10:13:37,416 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,416 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,417 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:13:37,417 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:13:37,417 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,417 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,417 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,417 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,418 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,418 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,418 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,418 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,418 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,418 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,462 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,463 DEBUG: Start: Training
-2016-09-06 10:13:37,463 DEBUG: Info: Time for Training: 0.0475831031799[s]
-2016-09-06 10:13:37,463 DEBUG: Done: Training
-2016-09-06 10:13:37,463 DEBUG: Start: Predicting
-2016-09-06 10:13:37,467 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,468 DEBUG: Start: Training
-2016-09-06 10:13:37,476 DEBUG: Done: Predicting
-2016-09-06 10:13:37,477 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,478 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,478 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,479 INFO: Done: Result Analysis
-2016-09-06 10:13:37,492 DEBUG: Info: Time for Training: 0.0758819580078[s]
-2016-09-06 10:13:37,492 DEBUG: Done: Training
-2016-09-06 10:13:37,492 DEBUG: Start: Predicting
-2016-09-06 10:13:37,495 DEBUG: Done: Predicting
-2016-09-06 10:13:37,495 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,496 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,496 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,497 INFO: Done: Result Analysis
-2016-09-06 10:13:37,558 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,558 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,559 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:13:37,559 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:13:37,559 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,559 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,559 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,559 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:13:37,559 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,560 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:13:37,560 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,560 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,560 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,560 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,610 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,610 DEBUG: Start: Training
-2016-09-06 10:13:37,614 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,614 DEBUG: Start: Training
-2016-09-06 10:13:37,627 DEBUG: Info: Time for Training: 0.0695948600769[s]
-2016-09-06 10:13:37,627 DEBUG: Done: Training
-2016-09-06 10:13:37,628 DEBUG: Start: Predicting
-2016-09-06 10:13:37,632 DEBUG: Info: Time for Training: 0.0741398334503[s]
-2016-09-06 10:13:37,632 DEBUG: Done: Training
-2016-09-06 10:13:37,632 DEBUG: Start: Predicting
-2016-09-06 10:13:37,633 DEBUG: Done: Predicting
-2016-09-06 10:13:37,633 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,634 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,634 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,634 INFO: Done: Result Analysis
-2016-09-06 10:13:37,635 DEBUG: Done: Predicting
-2016-09-06 10:13:37,635 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,637 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,637 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.628571428571
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,637 INFO: Done: Result Analysis
-2016-09-06 10:13:37,708 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,708 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:13:37,708 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,708 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,709 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:13:37,709 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:37,709 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,709 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:37,709 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,709 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,710 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:37,710 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:37,710 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,710 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,749 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,749 DEBUG: Start: Training
-2016-09-06 10:13:37,751 DEBUG: Info: Time for Training: 0.0440349578857[s]
-2016-09-06 10:13:37,751 DEBUG: Done: Training
-2016-09-06 10:13:37,752 DEBUG: Start: Predicting
-2016-09-06 10:13:37,754 DEBUG: Done: Predicting
-2016-09-06 10:13:37,754 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,755 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,756 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,756 INFO: Done: Result Analysis
-2016-09-06 10:13:37,768 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,768 DEBUG: Start: Training
-2016-09-06 10:13:37,773 DEBUG: Info: Time for Training: 0.0648808479309[s]
-2016-09-06 10:13:37,773 DEBUG: Done: Training
-2016-09-06 10:13:37,773 DEBUG: Start: Predicting
-2016-09-06 10:13:37,776 DEBUG: Done: Predicting
-2016-09-06 10:13:37,776 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,778 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,778 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,778 INFO: Done: Result Analysis
-2016-09-06 10:13:37,859 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,859 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:37,860 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:13:37,860 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:13:37,860 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,860 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:37,861 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:37,861 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:37,862 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:37,862 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:37,862 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,862 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:37,862 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,862 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:37,914 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,915 DEBUG: Start: Training
-2016-09-06 10:13:37,915 DEBUG: Info: Time for Training: 0.0569260120392[s]
-2016-09-06 10:13:37,915 DEBUG: Done: Training
-2016-09-06 10:13:37,916 DEBUG: Start: Predicting
-2016-09-06 10:13:37,927 DEBUG: Done: Predicting
-2016-09-06 10:13:37,927 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,929 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,929 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,930 INFO: Done: Result Analysis
-2016-09-06 10:13:37,973 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:37,974 DEBUG: Start: Training
-2016-09-06 10:13:37,982 DEBUG: Info: Time for Training: 0.123476028442[s]
-2016-09-06 10:13:37,982 DEBUG: Done: Training
-2016-09-06 10:13:37,982 DEBUG: Start: Predicting
-2016-09-06 10:13:37,985 DEBUG: Done: Predicting
-2016-09-06 10:13:37,985 DEBUG: Start: Getting Results
-2016-09-06 10:13:37,987 DEBUG: Done: Getting Results
-2016-09-06 10:13:37,987 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:13:37,987 INFO: Done: Result Analysis
-2016-09-06 10:13:38,103 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:38,103 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:13:38,104 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:13:38,104 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:13:38,104 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:38,104 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:13:38,105 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:38,105 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:13:38,105 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:38,105 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:13:38,105 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:38,105 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:13:38,105 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:38,105 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:13:38,152 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:38,152 DEBUG: Start: Training
-2016-09-06 10:13:38,153 DEBUG: Info: Time for Training: 0.0500018596649[s]
-2016-09-06 10:13:38,153 DEBUG: Done: Training
-2016-09-06 10:13:38,153 DEBUG: Start: Predicting
-2016-09-06 10:13:38,160 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:13:38,160 DEBUG: Start: Training
-2016-09-06 10:13:38,170 DEBUG: Done: Predicting
-2016-09-06 10:13:38,170 DEBUG: Start: Getting Results
-2016-09-06 10:13:38,172 DEBUG: Done: Getting Results
-2016-09-06 10:13:38,172 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:38,172 INFO: Done: Result Analysis
-2016-09-06 10:13:38,182 DEBUG: Info: Time for Training: 0.0787019729614[s]
-2016-09-06 10:13:38,182 DEBUG: Done: Training
-2016-09-06 10:13:38,182 DEBUG: Start: Predicting
-2016-09-06 10:13:38,185 DEBUG: Done: Predicting
-2016-09-06 10:13:38,186 DEBUG: Start: Getting Results
-2016-09-06 10:13:38,187 DEBUG: Done: Getting Results
-2016-09-06 10:13:38,187 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:13:38,187 INFO: Done: Result Analysis
-2016-09-06 10:13:38,404 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:13:38,404 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:13:38,405 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:13:38,405 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:13:38,405 INFO: Info: Shape of View0 :(300, 10)
-2016-09-06 10:13:38,406 INFO: Info: Shape of View0 :(300, 10)
-2016-09-06 10:13:38,406 INFO: Info: Shape of View1 :(300, 13)
-2016-09-06 10:13:38,407 INFO: Info: Shape of View1 :(300, 13)
-2016-09-06 10:13:38,407 INFO: Info: Shape of View2 :(300, 7)
-2016-09-06 10:13:38,407 INFO: Info: Shape of View2 :(300, 7)
-2016-09-06 10:13:38,407 INFO: Info: Shape of View3 :(300, 19)
-2016-09-06 10:13:38,407 INFO: Done: Read Database Files
-2016-09-06 10:13:38,408 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:13:38,408 INFO: Info: Shape of View3 :(300, 19)
-2016-09-06 10:13:38,408 INFO: Done: Read Database Files
-2016-09-06 10:13:38,408 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:13:38,412 INFO: Done: Determine validation split
-2016-09-06 10:13:38,412 INFO: Start: Determine 5 folds
-2016-09-06 10:13:38,412 INFO: Done: Determine validation split
-2016-09-06 10:13:38,413 INFO: Start: Determine 5 folds
-2016-09-06 10:13:38,422 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:13:38,422 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:13:38,422 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:13:38,422 INFO: Done: Determine folds
-2016-09-06 10:13:38,423 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:13:38,423 INFO: Start: Classification
-2016-09-06 10:13:38,423 INFO: Start: Fold number 1
-2016-09-06 10:13:38,425 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:13:38,425 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:13:38,425 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:13:38,425 INFO: Done: Determine folds
-2016-09-06 10:13:38,425 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:13:38,425 INFO: Start: Classification
-2016-09-06 10:13:38,426 INFO: Start: Fold number 1
-2016-09-06 10:13:38,463 DEBUG: Start: Iteration 1
-2016-09-06 10:13:38,473 DEBUG: View 0 : 0.535135135135
-2016-09-06 10:13:38,481 DEBUG: View 1 : 0.524324324324
-2016-09-06 10:13:38,491 DEBUG: View 2 : 0.437837837838
-2016-09-06 10:13:38,500 DEBUG: View 3 : 0.535135135135
-2016-09-06 10:13:38,541 DEBUG: Best view : View1
-2016-09-06 10:13:38,635 DEBUG: Start: Iteration 2
-2016-09-06 10:13:38,645 DEBUG: View 0 : 0.756756756757
-2016-09-06 10:13:38,652 DEBUG: View 1 : 0.735135135135
-2016-09-06 10:13:38,662 DEBUG: View 2 : 0.697297297297
-2016-09-06 10:13:38,669 DEBUG: View 3 : 0.697297297297
-2016-09-06 10:13:38,719 DEBUG: Best view : View0
-2016-09-06 10:13:38,877 DEBUG: Start: Iteration 3
-2016-09-06 10:13:38,885 DEBUG: View 0 : 0.756756756757
-2016-09-06 10:13:38,892 DEBUG: View 1 : 0.735135135135
-2016-09-06 10:13:38,899 DEBUG: View 2 : 0.697297297297
-2016-09-06 10:13:38,907 DEBUG: View 3 : 0.697297297297
-2016-09-06 10:13:38,949 DEBUG: Best view : View0
-2016-09-06 10:13:39,172 DEBUG: Start: Iteration 4
-2016-09-06 10:13:39,179 DEBUG: View 0 : 0.702702702703
-2016-09-06 10:13:39,187 DEBUG: View 1 : 0.67027027027
-2016-09-06 10:13:39,194 DEBUG: View 2 : 0.708108108108
-2016-09-06 10:13:39,202 DEBUG: View 3 : 0.67027027027
-2016-09-06 10:13:39,246 DEBUG: Best view : View0
-2016-09-06 10:13:39,538 INFO: Start: Classification
-2016-09-06 10:13:40,019 INFO: Done: Fold number 1
-2016-09-06 10:13:40,019 INFO: Start: Fold number 2
-2016-09-06 10:13:40,049 DEBUG: Start: Iteration 1
-2016-09-06 10:13:40,056 DEBUG: View 0 : 0.522222222222
-2016-09-06 10:13:40,063 DEBUG: View 1 : 0.516666666667
-2016-09-06 10:13:40,069 DEBUG: View 2 : 0.477777777778
-2016-09-06 10:13:40,077 DEBUG: View 3 : 0.516666666667
-2016-09-06 10:13:40,109 DEBUG: Best view : View0
-2016-09-06 10:13:40,187 DEBUG: Start: Iteration 2
-2016-09-06 10:13:40,195 DEBUG: View 0 : 0.622222222222
-2016-09-06 10:13:40,202 DEBUG: View 1 : 0.672222222222
-2016-09-06 10:13:40,209 DEBUG: View 2 : 0.677777777778
-2016-09-06 10:13:40,217 DEBUG: View 3 : 0.672222222222
-2016-09-06 10:13:40,256 DEBUG: Best view : View2
-2016-09-06 10:13:40,404 DEBUG: Start: Iteration 3
-2016-09-06 10:13:40,411 DEBUG: View 0 : 0.622222222222
-2016-09-06 10:13:40,419 DEBUG: View 1 : 0.672222222222
-2016-09-06 10:13:40,426 DEBUG: View 2 : 0.677777777778
-2016-09-06 10:13:40,433 DEBUG: View 3 : 0.672222222222
-2016-09-06 10:13:40,475 DEBUG: Best view : View2
-2016-09-06 10:13:40,691 INFO: Start: Classification
-2016-09-06 10:13:41,043 INFO: Done: Fold number 2
-2016-09-06 10:13:41,043 INFO: Start: Fold number 3
-2016-09-06 10:13:41,073 DEBUG: Start: Iteration 1
-2016-09-06 10:13:41,079 DEBUG: View 0 : 0.544444444444
-2016-09-06 10:13:41,087 DEBUG: View 1 : 0.522222222222
-2016-09-06 10:13:41,093 DEBUG: View 2 : 0.466666666667
-2016-09-06 10:13:41,101 DEBUG: View 3 : 0.511111111111
-2016-09-06 10:13:41,133 DEBUG: Best view : View1
-2016-09-06 10:13:41,212 DEBUG: Start: Iteration 2
-2016-09-06 10:13:41,220 DEBUG: View 0 : 0.688888888889
-2016-09-06 10:13:41,227 DEBUG: View 1 : 0.627777777778
-2016-09-06 10:13:41,234 DEBUG: View 2 : 0.716666666667
-2016-09-06 10:13:41,242 DEBUG: View 3 : 0.733333333333
-2016-09-06 10:13:41,280 DEBUG: Best view : View3
-2016-09-06 10:13:41,428 DEBUG: Start: Iteration 3
-2016-09-06 10:13:41,435 DEBUG: View 0 : 0.688888888889
-2016-09-06 10:13:41,443 DEBUG: View 1 : 0.627777777778
-2016-09-06 10:13:41,450 DEBUG: View 2 : 0.716666666667
-2016-09-06 10:13:41,457 DEBUG: View 3 : 0.733333333333
-2016-09-06 10:13:41,498 DEBUG: Best view : View3
-2016-09-06 10:13:41,716 DEBUG: Start: Iteration 4
-2016-09-06 10:13:41,723 DEBUG: View 0 : 0.666666666667
-2016-09-06 10:13:41,730 DEBUG: View 1 : 0.611111111111
-2016-09-06 10:13:41,737 DEBUG: View 2 : 0.705555555556
-2016-09-06 10:13:41,745 DEBUG: View 3 : 0.688888888889
-2016-09-06 10:13:41,789 DEBUG: Best view : View2
-2016-09-06 10:13:42,070 INFO: Start: Classification
-2016-09-06 10:13:42,538 INFO: Done: Fold number 3
-2016-09-06 10:13:42,538 INFO: Start: Fold number 4
-2016-09-06 10:13:42,568 DEBUG: Start: Iteration 1
-2016-09-06 10:13:42,575 DEBUG: View 0 : 0.527472527473
-2016-09-06 10:13:42,582 DEBUG: View 1 : 0.582417582418
-2016-09-06 10:13:42,588 DEBUG: View 2 : 0.516483516484
-2016-09-06 10:13:42,596 DEBUG: View 3 : 0.516483516484
-2016-09-06 10:13:42,628 DEBUG: Best view : View0
-2016-09-06 10:13:42,708 DEBUG: Start: Iteration 2
-2016-09-06 10:13:42,716 DEBUG: View 0 : 0.697802197802
-2016-09-06 10:13:42,723 DEBUG: View 1 : 0.675824175824
-2016-09-06 10:13:42,730 DEBUG: View 2 : 0.675824175824
-2016-09-06 10:13:42,738 DEBUG: View 3 : 0.686813186813
-2016-09-06 10:13:42,776 DEBUG: Best view : View0
-2016-09-06 10:13:42,926 DEBUG: Start: Iteration 3
-2016-09-06 10:13:42,933 DEBUG: View 0 : 0.697802197802
-2016-09-06 10:13:42,940 DEBUG: View 1 : 0.675824175824
-2016-09-06 10:13:42,947 DEBUG: View 2 : 0.675824175824
-2016-09-06 10:13:42,955 DEBUG: View 3 : 0.686813186813
-2016-09-06 10:13:42,996 DEBUG: Best view : View0
-2016-09-06 10:13:43,215 INFO: Start: Classification
-2016-09-06 10:13:43,569 INFO: Done: Fold number 4
-2016-09-06 10:13:43,570 INFO: Start: Fold number 5
-2016-09-06 10:13:43,600 DEBUG: Start: Iteration 1
-2016-09-06 10:13:43,607 DEBUG: View 0 : 0.497297297297
-2016-09-06 10:13:43,615 DEBUG: View 1 : 0.486486486486
-2016-09-06 10:13:43,621 DEBUG: View 2 : 0.491891891892
-2016-09-06 10:13:43,629 DEBUG: View 3 : 0.502702702703
-2016-09-06 10:13:43,662 DEBUG: Best view : View3
-2016-09-06 10:13:43,743 DEBUG: Start: Iteration 2
-2016-09-06 10:13:43,751 DEBUG: View 0 : 0.702702702703
-2016-09-06 10:13:43,758 DEBUG: View 1 : 0.702702702703
-2016-09-06 10:13:43,765 DEBUG: View 2 : 0.605405405405
-2016-09-06 10:13:43,773 DEBUG: View 3 : 0.718918918919
-2016-09-06 10:13:43,812 DEBUG: Best view : View3
-2016-09-06 10:13:43,965 DEBUG: Start: Iteration 3
-2016-09-06 10:13:43,972 DEBUG: View 0 : 0.702702702703
-2016-09-06 10:13:43,980 DEBUG: View 1 : 0.702702702703
-2016-09-06 10:13:43,987 DEBUG: View 2 : 0.605405405405
-2016-09-06 10:13:43,995 DEBUG: View 3 : 0.718918918919
-2016-09-06 10:13:44,037 DEBUG: Best view : View3
-2016-09-06 10:13:44,260 DEBUG: Start: Iteration 4
-2016-09-06 10:13:44,268 DEBUG: View 0 : 0.724324324324
-2016-09-06 10:13:44,275 DEBUG: View 1 : 0.627027027027
-2016-09-06 10:13:44,283 DEBUG: View 2 : 0.681081081081
-2016-09-06 10:13:44,290 DEBUG: View 3 : 0.648648648649
-2016-09-06 10:13:44,335 DEBUG: Best view : View0
-2016-09-06 10:13:44,628 INFO: Start: Classification
-2016-09-06 10:13:45,105 INFO: Done: Fold number 5
-2016-09-06 10:13:45,106 INFO: Done: Classification
-2016-09-06 10:13:45,106 INFO: Info: Time for Classification: 6[s]
-2016-09-06 10:13:45,106 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d594434166a146d3f494ca2c20fab70800090ff2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 41319f9a9f3ebffe8d077fd30a358df14240f387..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ffd9cc63fc5b1ad1c7daf34cb4d7e8e8cd756643..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 61f7d8649463de1c263f3b9d3ab07a2cac99614d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101335Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5e68fb883ac17dfd5d6210e4375e439e31615a56..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 73356fd51695a723319d40daeac7d2afc5ae6419..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1a9bd291268ae15c9a9d1ba00ec0f9484459229c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7ece7302181bbc22ada0c2dc3571a028bfff24ec..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.904761904762
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.904761904762
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 23e4c20cb46a53fea41beacfabcfa51a0c4ea031..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a6ee90732ab60d714cd21319c61f07ccabd6a223..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 736ada899fb022ef8ba2503b09a6723398d5c9b4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 749dc0afe3142c94dc5a247c6c0a6b04cd19c6e6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101336Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 05b31d532cb9d42d7f108cba47b4846bd33a83d2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 3, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0e7c951d0a5f47fee7f6b6922f86ef8a17af692d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 58caffdda4255d1341b8e1260d1601d02eb45c2f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 48
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fffde9a8b265f2315fc6bed616f802872619a798..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 3, max_depth : 16
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 35e2c637bc2fbd3b09799e55301b194a1b4095b4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fc1d9bb0429aaf686a8f5f1ede033cbc09251ab2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3b5c3c54184735dc3bf8b27aaa6240a75cc3ca02..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.628571428571
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 785b35119368d7ad397bf94b6366cd41a012941e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101337Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5be944065b4ac4026e6568b017952bc45473e35e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7668376b7f21ff7c34aeba050aee3bde3f4c5468..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-101338Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 9584
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 5867a8bf1b4cfc28d3fca0c46d6369be80d05aef..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102343-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1381 +0,0 @@
-2016-09-06 10:23:43,264 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:23:43,264 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 9.821875e-05 Gbytes /!\
-2016-09-06 10:23:48,275 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:23:48,277 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:23:48,332 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:48,332 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:48,332 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:23:48,332 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:23:48,332 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:48,332 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:48,333 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:48,333 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:48,333 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:48,333 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:48,333 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:48,333 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:48,333 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:48,333 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:48,366 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:48,367 DEBUG: Start: Training
-2016-09-06 10:23:48,368 DEBUG: Info: Time for Training: 0.0366399288177[s]
-2016-09-06 10:23:48,368 DEBUG: Done: Training
-2016-09-06 10:23:48,368 DEBUG: Start: Predicting
-2016-09-06 10:23:48,371 DEBUG: Done: Predicting
-2016-09-06 10:23:48,371 DEBUG: Start: Getting Results
-2016-09-06 10:23:48,372 DEBUG: Done: Getting Results
-2016-09-06 10:23:48,372 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.919047619048
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:23:48,372 INFO: Done: Result Analysis
-2016-09-06 10:23:48,382 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:48,382 DEBUG: Start: Training
-2016-09-06 10:23:48,385 DEBUG: Info: Time for Training: 0.0539109706879[s]
-2016-09-06 10:23:48,385 DEBUG: Done: Training
-2016-09-06 10:23:48,385 DEBUG: Start: Predicting
-2016-09-06 10:23:48,388 DEBUG: Done: Predicting
-2016-09-06 10:23:48,388 DEBUG: Start: Getting Results
-2016-09-06 10:23:48,390 DEBUG: Done: Getting Results
-2016-09-06 10:23:48,390 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:48,390 INFO: Done: Result Analysis
-2016-09-06 10:23:48,477 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:48,477 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:48,477 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:23:48,477 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:23:48,477 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:48,477 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:48,478 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:48,478 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:48,478 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:48,478 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:48,478 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:48,478 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:48,478 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:48,478 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:48,509 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:48,509 DEBUG: Start: Training
-2016-09-06 10:23:48,510 DEBUG: Info: Time for Training: 0.0335669517517[s]
-2016-09-06 10:23:48,510 DEBUG: Done: Training
-2016-09-06 10:23:48,510 DEBUG: Start: Predicting
-2016-09-06 10:23:48,514 DEBUG: Done: Predicting
-2016-09-06 10:23:48,514 DEBUG: Start: Getting Results
-2016-09-06 10:23:48,516 DEBUG: Done: Getting Results
-2016-09-06 10:23:48,516 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.67619047619
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:23:48,516 INFO: Done: Result Analysis
-2016-09-06 10:23:48,820 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:48,820 DEBUG: Start: Training
-2016-09-06 10:23:48,870 DEBUG: Info: Time for Training: 0.393655061722[s]
-2016-09-06 10:23:48,870 DEBUG: Done: Training
-2016-09-06 10:23:48,870 DEBUG: Start: Predicting
-2016-09-06 10:23:48,876 DEBUG: Done: Predicting
-2016-09-06 10:23:48,876 DEBUG: Start: Getting Results
-2016-09-06 10:23:48,878 DEBUG: Done: Getting Results
-2016-09-06 10:23:48,878 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 20, max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:48,878 INFO: Done: Result Analysis
-2016-09-06 10:23:49,036 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,036 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:23:49,037 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:23:49,037 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:49,037 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:49,038 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:49,038 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:49,038 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:49,038 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:49,038 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:49,038 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:49,038 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,038 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,100 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:49,100 DEBUG: Start: Training
-2016-09-06 10:23:49,122 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:49,122 DEBUG: Start: Training
-2016-09-06 10:23:49,123 DEBUG: Info: Time for Training: 0.0881431102753[s]
-2016-09-06 10:23:49,124 DEBUG: Done: Training
-2016-09-06 10:23:49,124 DEBUG: Start: Predicting
-2016-09-06 10:23:49,125 DEBUG: Info: Time for Training: 0.0898480415344[s]
-2016-09-06 10:23:49,125 DEBUG: Done: Training
-2016-09-06 10:23:49,125 DEBUG: Start: Predicting
-2016-09-06 10:23:49,129 DEBUG: Done: Predicting
-2016-09-06 10:23:49,129 DEBUG: Start: Getting Results
-2016-09-06 10:23:49,130 DEBUG: Done: Getting Results
-2016-09-06 10:23:49,130 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.480952380952
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.480952380952
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,130 INFO: Done: Result Analysis
-2016-09-06 10:23:49,138 DEBUG: Done: Predicting
-2016-09-06 10:23:49,138 DEBUG: Start: Getting Results
-2016-09-06 10:23:49,140 DEBUG: Done: Getting Results
-2016-09-06 10:23:49,140 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,140 INFO: Done: Result Analysis
-2016-09-06 10:23:49,278 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,279 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:23:49,279 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:49,280 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:49,280 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,280 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:49,280 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:23:49,280 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:49,280 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,280 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:49,281 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:23:49,281 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:23:49,282 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:49,282 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,329 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:49,329 DEBUG: Start: Training
-2016-09-06 10:23:49,340 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:49,340 DEBUG: Start: Training
-2016-09-06 10:23:49,345 DEBUG: Info: Time for Training: 0.0676798820496[s]
-2016-09-06 10:23:49,346 DEBUG: Done: Training
-2016-09-06 10:23:49,346 DEBUG: Start: Predicting
-2016-09-06 10:23:49,351 DEBUG: Done: Predicting
-2016-09-06 10:23:49,351 DEBUG: Start: Getting Results
-2016-09-06 10:23:49,352 DEBUG: Done: Getting Results
-2016-09-06 10:23:49,353 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,353 INFO: Done: Result Analysis
-2016-09-06 10:23:49,358 DEBUG: Info: Time for Training: 0.0790169239044[s]
-2016-09-06 10:23:49,358 DEBUG: Done: Training
-2016-09-06 10:23:49,358 DEBUG: Start: Predicting
-2016-09-06 10:23:49,361 DEBUG: Done: Predicting
-2016-09-06 10:23:49,361 DEBUG: Start: Getting Results
-2016-09-06 10:23:49,363 DEBUG: Done: Getting Results
-2016-09-06 10:23:49,364 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.752380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.752380952381
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,364 INFO: Done: Result Analysis
-2016-09-06 10:23:49,427 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,427 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,427 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:23:49,427 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:23:49,427 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:49,427 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:49,428 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:49,428 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:49,428 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:49,428 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:49,428 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:49,428 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:49,428 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,428 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,470 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:49,470 DEBUG: Start: Training
-2016-09-06 10:23:49,472 DEBUG: Info: Time for Training: 0.0461599826813[s]
-2016-09-06 10:23:49,472 DEBUG: Done: Training
-2016-09-06 10:23:49,473 DEBUG: Start: Predicting
-2016-09-06 10:23:49,475 DEBUG: Done: Predicting
-2016-09-06 10:23:49,475 DEBUG: Start: Getting Results
-2016-09-06 10:23:49,476 DEBUG: Done: Getting Results
-2016-09-06 10:23:49,477 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.780952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.780952380952
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,477 INFO: Done: Result Analysis
-2016-09-06 10:23:49,479 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:49,479 DEBUG: Start: Training
-2016-09-06 10:23:49,483 DEBUG: Info: Time for Training: 0.057247877121[s]
-2016-09-06 10:23:49,484 DEBUG: Done: Training
-2016-09-06 10:23:49,484 DEBUG: Start: Predicting
-2016-09-06 10:23:49,487 DEBUG: Done: Predicting
-2016-09-06 10:23:49,487 DEBUG: Start: Getting Results
-2016-09-06 10:23:49,489 DEBUG: Done: Getting Results
-2016-09-06 10:23:49,489 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,489 INFO: Done: Result Analysis
-2016-09-06 10:23:49,575 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,575 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:49,575 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:23:49,575 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:23:49,575 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:49,575 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:49,576 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:49,576 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:49,576 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:49,576 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:49,576 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:49,576 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:49,576 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,576 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:49,606 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:49,607 DEBUG: Start: Training
-2016-09-06 10:23:49,607 DEBUG: Info: Time for Training: 0.0329051017761[s]
-2016-09-06 10:23:49,607 DEBUG: Done: Training
-2016-09-06 10:23:49,607 DEBUG: Start: Predicting
-2016-09-06 10:23:49,612 DEBUG: Done: Predicting
-2016-09-06 10:23:49,613 DEBUG: Start: Getting Results
-2016-09-06 10:23:49,614 DEBUG: Done: Getting Results
-2016-09-06 10:23:49,614 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.657142857143
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.657142857143
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,614 INFO: Done: Result Analysis
-2016-09-06 10:23:49,922 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:49,922 DEBUG: Start: Training
-2016-09-06 10:23:49,974 DEBUG: Info: Time for Training: 0.399342060089[s]
-2016-09-06 10:23:49,974 DEBUG: Done: Training
-2016-09-06 10:23:49,974 DEBUG: Start: Predicting
-2016-09-06 10:23:49,981 DEBUG: Done: Predicting
-2016-09-06 10:23:49,981 DEBUG: Start: Getting Results
-2016-09-06 10:23:49,982 DEBUG: Done: Getting Results
-2016-09-06 10:23:49,982 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 20, max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:23:49,982 INFO: Done: Result Analysis
-2016-09-06 10:23:50,127 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,127 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,128 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:23:50,128 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:23:50,128 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:50,128 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:50,129 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:50,129 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:50,129 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:50,129 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:50,129 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:50,129 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:50,129 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,129 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,173 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:50,173 DEBUG: Start: Training
-2016-09-06 10:23:50,174 DEBUG: Info: Time for Training: 0.0477719306946[s]
-2016-09-06 10:23:50,174 DEBUG: Done: Training
-2016-09-06 10:23:50,174 DEBUG: Start: Predicting
-2016-09-06 10:23:50,179 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:50,179 DEBUG: Start: Training
-2016-09-06 10:23:50,187 DEBUG: Done: Predicting
-2016-09-06 10:23:50,188 DEBUG: Start: Getting Results
-2016-09-06 10:23:50,190 DEBUG: Done: Getting Results
-2016-09-06 10:23:50,190 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,190 INFO: Done: Result Analysis
-2016-09-06 10:23:50,203 DEBUG: Info: Time for Training: 0.0768611431122[s]
-2016-09-06 10:23:50,204 DEBUG: Done: Training
-2016-09-06 10:23:50,204 DEBUG: Start: Predicting
-2016-09-06 10:23:50,207 DEBUG: Done: Predicting
-2016-09-06 10:23:50,207 DEBUG: Start: Getting Results
-2016-09-06 10:23:50,208 DEBUG: Done: Getting Results
-2016-09-06 10:23:50,208 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,209 INFO: Done: Result Analysis
-2016-09-06 10:23:50,271 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,271 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:23:50,271 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,271 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:50,272 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:23:50,272 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:50,272 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:50,273 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:50,273 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:50,273 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,273 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:23:50,273 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:23:50,274 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:50,274 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,323 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:50,323 DEBUG: Start: Training
-2016-09-06 10:23:50,325 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:50,325 DEBUG: Start: Training
-2016-09-06 10:23:50,341 DEBUG: Info: Time for Training: 0.0708639621735[s]
-2016-09-06 10:23:50,341 DEBUG: Done: Training
-2016-09-06 10:23:50,341 DEBUG: Start: Predicting
-2016-09-06 10:23:50,344 DEBUG: Info: Time for Training: 0.0738418102264[s]
-2016-09-06 10:23:50,344 DEBUG: Done: Training
-2016-09-06 10:23:50,344 DEBUG: Start: Predicting
-2016-09-06 10:23:50,347 DEBUG: Done: Predicting
-2016-09-06 10:23:50,347 DEBUG: Start: Getting Results
-2016-09-06 10:23:50,347 DEBUG: Done: Predicting
-2016-09-06 10:23:50,348 DEBUG: Start: Getting Results
-2016-09-06 10:23:50,348 DEBUG: Done: Getting Results
-2016-09-06 10:23:50,348 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,348 INFO: Done: Result Analysis
-2016-09-06 10:23:50,349 DEBUG: Done: Getting Results
-2016-09-06 10:23:50,349 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,349 INFO: Done: Result Analysis
-2016-09-06 10:23:50,420 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,420 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,421 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:23:50,421 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:23:50,421 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:50,421 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:50,422 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:50,422 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:50,422 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:50,422 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:50,423 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:50,423 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:50,423 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,423 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,474 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:50,474 DEBUG: Start: Training
-2016-09-06 10:23:50,476 DEBUG: Info: Time for Training: 0.0563578605652[s]
-2016-09-06 10:23:50,476 DEBUG: Done: Training
-2016-09-06 10:23:50,476 DEBUG: Start: Predicting
-2016-09-06 10:23:50,480 DEBUG: Done: Predicting
-2016-09-06 10:23:50,480 DEBUG: Start: Getting Results
-2016-09-06 10:23:50,482 DEBUG: Done: Getting Results
-2016-09-06 10:23:50,482 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.904761904762
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.904761904762
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,483 INFO: Done: Result Analysis
-2016-09-06 10:23:50,493 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:50,493 DEBUG: Start: Training
-2016-09-06 10:23:50,496 DEBUG: Info: Time for Training: 0.0766098499298[s]
-2016-09-06 10:23:50,496 DEBUG: Done: Training
-2016-09-06 10:23:50,496 DEBUG: Start: Predicting
-2016-09-06 10:23:50,499 DEBUG: Done: Predicting
-2016-09-06 10:23:50,499 DEBUG: Start: Getting Results
-2016-09-06 10:23:50,501 DEBUG: Done: Getting Results
-2016-09-06 10:23:50,501 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,501 INFO: Done: Result Analysis
-2016-09-06 10:23:50,564 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,564 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:50,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:23:50,564 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:23:50,564 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:50,565 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:50,565 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:50,565 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:50,565 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:50,565 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:50,565 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:50,565 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,565 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:50,566 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:50,596 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:50,596 DEBUG: Start: Training
-2016-09-06 10:23:50,597 DEBUG: Info: Time for Training: 0.0331511497498[s]
-2016-09-06 10:23:50,597 DEBUG: Done: Training
-2016-09-06 10:23:50,597 DEBUG: Start: Predicting
-2016-09-06 10:23:50,602 DEBUG: Done: Predicting
-2016-09-06 10:23:50,602 DEBUG: Start: Getting Results
-2016-09-06 10:23:50,603 DEBUG: Done: Getting Results
-2016-09-06 10:23:50,603 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,603 INFO: Done: Result Analysis
-2016-09-06 10:23:50,908 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:50,908 DEBUG: Start: Training
-2016-09-06 10:23:50,959 DEBUG: Info: Time for Training: 0.395528078079[s]
-2016-09-06 10:23:50,959 DEBUG: Done: Training
-2016-09-06 10:23:50,959 DEBUG: Start: Predicting
-2016-09-06 10:23:50,966 DEBUG: Done: Predicting
-2016-09-06 10:23:50,966 DEBUG: Start: Getting Results
-2016-09-06 10:23:50,967 DEBUG: Done: Getting Results
-2016-09-06 10:23:50,968 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 20, max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:23:50,968 INFO: Done: Result Analysis
-2016-09-06 10:23:51,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,112 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,113 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:23:51,113 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:23:51,113 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:51,113 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:51,113 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:51,113 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:51,113 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:51,113 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:51,113 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:51,113 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:51,114 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,114 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,158 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:51,159 DEBUG: Start: Training
-2016-09-06 10:23:51,159 DEBUG: Info: Time for Training: 0.0475599765778[s]
-2016-09-06 10:23:51,160 DEBUG: Done: Training
-2016-09-06 10:23:51,160 DEBUG: Start: Predicting
-2016-09-06 10:23:51,163 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:51,163 DEBUG: Start: Training
-2016-09-06 10:23:51,180 DEBUG: Info: Time for Training: 0.0679569244385[s]
-2016-09-06 10:23:51,180 DEBUG: Done: Training
-2016-09-06 10:23:51,180 DEBUG: Start: Predicting
-2016-09-06 10:23:51,184 DEBUG: Done: Predicting
-2016-09-06 10:23:51,184 DEBUG: Done: Predicting
-2016-09-06 10:23:51,184 DEBUG: Start: Getting Results
-2016-09-06 10:23:51,184 DEBUG: Start: Getting Results
-2016-09-06 10:23:51,186 DEBUG: Done: Getting Results
-2016-09-06 10:23:51,187 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,187 DEBUG: Done: Getting Results
-2016-09-06 10:23:51,187 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,187 INFO: Done: Result Analysis
-2016-09-06 10:23:51,187 INFO: Done: Result Analysis
-2016-09-06 10:23:51,265 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,265 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,265 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:23:51,265 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:23:51,265 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:51,265 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:51,266 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:51,266 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:23:51,266 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:51,266 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:23:51,266 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:51,266 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:51,266 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,266 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,315 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:51,315 DEBUG: Start: Training
-2016-09-06 10:23:51,321 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:51,321 DEBUG: Start: Training
-2016-09-06 10:23:51,333 DEBUG: Info: Time for Training: 0.0680320262909[s]
-2016-09-06 10:23:51,333 DEBUG: Done: Training
-2016-09-06 10:23:51,333 DEBUG: Start: Predicting
-2016-09-06 10:23:51,338 DEBUG: Done: Predicting
-2016-09-06 10:23:51,338 DEBUG: Start: Getting Results
-2016-09-06 10:23:51,339 DEBUG: Done: Getting Results
-2016-09-06 10:23:51,340 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,340 INFO: Done: Result Analysis
-2016-09-06 10:23:51,340 DEBUG: Info: Time for Training: 0.075443983078[s]
-2016-09-06 10:23:51,340 DEBUG: Done: Training
-2016-09-06 10:23:51,340 DEBUG: Start: Predicting
-2016-09-06 10:23:51,343 DEBUG: Done: Predicting
-2016-09-06 10:23:51,343 DEBUG: Start: Getting Results
-2016-09-06 10:23:51,345 DEBUG: Done: Getting Results
-2016-09-06 10:23:51,345 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,345 INFO: Done: Result Analysis
-2016-09-06 10:23:51,420 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,420 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,420 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:23:51,420 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:23:51,420 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:51,420 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:51,421 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:51,421 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:51,421 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:51,421 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:51,421 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:51,422 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,422 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:51,422 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,462 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:51,462 DEBUG: Start: Training
-2016-09-06 10:23:51,463 DEBUG: Info: Time for Training: 0.0440928936005[s]
-2016-09-06 10:23:51,463 DEBUG: Done: Training
-2016-09-06 10:23:51,463 DEBUG: Start: Predicting
-2016-09-06 10:23:51,466 DEBUG: Done: Predicting
-2016-09-06 10:23:51,466 DEBUG: Start: Getting Results
-2016-09-06 10:23:51,467 DEBUG: Done: Getting Results
-2016-09-06 10:23:51,467 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.880952380952
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.880952380952
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,467 INFO: Done: Result Analysis
-2016-09-06 10:23:51,470 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:51,470 DEBUG: Start: Training
-2016-09-06 10:23:51,473 DEBUG: Info: Time for Training: 0.0543620586395[s]
-2016-09-06 10:23:51,473 DEBUG: Done: Training
-2016-09-06 10:23:51,473 DEBUG: Start: Predicting
-2016-09-06 10:23:51,476 DEBUG: Done: Predicting
-2016-09-06 10:23:51,476 DEBUG: Start: Getting Results
-2016-09-06 10:23:51,478 DEBUG: Done: Getting Results
-2016-09-06 10:23:51,479 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,479 INFO: Done: Result Analysis
-2016-09-06 10:23:51,574 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,574 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:51,574 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:23:51,574 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:23:51,574 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:51,574 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:51,575 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:51,575 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:51,575 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:51,575 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:51,576 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:51,576 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:51,576 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,576 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:51,623 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:51,623 DEBUG: Start: Training
-2016-09-06 10:23:51,624 DEBUG: Info: Time for Training: 0.0511050224304[s]
-2016-09-06 10:23:51,624 DEBUG: Done: Training
-2016-09-06 10:23:51,624 DEBUG: Start: Predicting
-2016-09-06 10:23:51,631 DEBUG: Done: Predicting
-2016-09-06 10:23:51,631 DEBUG: Start: Getting Results
-2016-09-06 10:23:51,633 DEBUG: Done: Getting Results
-2016-09-06 10:23:51,633 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.695238095238
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.695238095238
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,634 INFO: Done: Result Analysis
-2016-09-06 10:23:51,937 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:51,937 DEBUG: Start: Training
-2016-09-06 10:23:51,987 DEBUG: Info: Time for Training: 0.414555072784[s]
-2016-09-06 10:23:51,988 DEBUG: Done: Training
-2016-09-06 10:23:51,988 DEBUG: Start: Predicting
-2016-09-06 10:23:51,994 DEBUG: Done: Predicting
-2016-09-06 10:23:51,994 DEBUG: Start: Getting Results
-2016-09-06 10:23:51,995 DEBUG: Done: Getting Results
-2016-09-06 10:23:51,996 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 20, max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.952380952381
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:23:51,996 INFO: Done: Result Analysis
-2016-09-06 10:23:52,119 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:52,119 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:23:52,120 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:23:52,120 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:23:52,120 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:52,120 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:23:52,120 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:52,120 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 10:23:52,121 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:52,121 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 10:23:52,121 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:52,121 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:23:52,121 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:52,121 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:23:52,167 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:52,167 DEBUG: Start: Training
-2016-09-06 10:23:52,167 DEBUG: Info: Time for Training: 0.0486381053925[s]
-2016-09-06 10:23:52,168 DEBUG: Done: Training
-2016-09-06 10:23:52,168 DEBUG: Start: Predicting
-2016-09-06 10:23:52,170 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:23:52,170 DEBUG: Start: Training
-2016-09-06 10:23:52,186 DEBUG: Done: Predicting
-2016-09-06 10:23:52,186 DEBUG: Start: Getting Results
-2016-09-06 10:23:52,187 DEBUG: Info: Time for Training: 0.0685579776764[s]
-2016-09-06 10:23:52,188 DEBUG: Done: Training
-2016-09-06 10:23:52,188 DEBUG: Done: Getting Results
-2016-09-06 10:23:52,188 DEBUG: Start: Predicting
-2016-09-06 10:23:52,188 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:23:52,188 INFO: Done: Result Analysis
-2016-09-06 10:23:52,191 DEBUG: Done: Predicting
-2016-09-06 10:23:52,191 DEBUG: Start: Getting Results
-2016-09-06 10:23:52,192 DEBUG: Done: Getting Results
-2016-09-06 10:23:52,192 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:23:52,193 INFO: Done: Result Analysis
-2016-09-06 10:23:52,415 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:23:52,415 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:23:52,416 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 10:23:52,417 INFO: Info: Shape of View1 :(300, 13)
-2016-09-06 10:23:52,417 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:23:52,417 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:23:52,417 INFO: Info: Shape of View2 :(300, 7)
-2016-09-06 10:23:52,418 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 10:23:52,418 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 10:23:52,419 INFO: Done: Read Database Files
-2016-09-06 10:23:52,419 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:23:52,419 INFO: Info: Shape of View1 :(300, 13)
-2016-09-06 10:23:52,420 INFO: Info: Shape of View2 :(300, 7)
-2016-09-06 10:23:52,421 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 10:23:52,421 INFO: Done: Read Database Files
-2016-09-06 10:23:52,421 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:23:52,426 INFO: Done: Determine validation split
-2016-09-06 10:23:52,426 INFO: Start: Determine 5 folds
-2016-09-06 10:23:52,427 INFO: Done: Determine validation split
-2016-09-06 10:23:52,427 INFO: Start: Determine 5 folds
-2016-09-06 10:23:52,435 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:23:52,435 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:23:52,435 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:23:52,435 INFO: Done: Determine folds
-2016-09-06 10:23:52,435 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:23:52,435 INFO: Start: Classification
-2016-09-06 10:23:52,435 INFO: Start: Fold number 1
-2016-09-06 10:23:52,436 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:23:52,436 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:23:52,436 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:23:52,436 INFO: Done: Determine folds
-2016-09-06 10:23:52,436 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:23:52,436 INFO: Start: Classification
-2016-09-06 10:23:52,437 INFO: Start: Fold number 1
-2016-09-06 10:23:52,457 DEBUG: Start: Iteration 1
-2016-09-06 10:23:52,463 DEBUG: View 0 : 0.516666666667
-2016-09-06 10:23:52,469 DEBUG: View 1 : 0.475
-2016-09-06 10:23:52,475 DEBUG: View 2 : 0.525
-2016-09-06 10:23:52,480 DEBUG: View 3 : 0.525
-2016-09-06 10:23:52,503 DEBUG: Best view : View2
-2016-09-06 10:23:52,559 DEBUG: Start: Iteration 2
-2016-09-06 10:23:52,565 DEBUG: View 0 : 0.733333333333
-2016-09-06 10:23:52,572 DEBUG: View 1 : 0.741666666667
-2016-09-06 10:23:52,577 DEBUG: View 2 : 0.741666666667
-2016-09-06 10:23:52,583 DEBUG: View 3 : 0.658333333333
-2016-09-06 10:23:52,610 DEBUG: Best view : View1
-2016-09-06 10:23:52,727 DEBUG: Start: Iteration 3
-2016-09-06 10:23:52,737 DEBUG: View 0 : 0.733333333333
-2016-09-06 10:23:52,748 DEBUG: View 1 : 0.741666666667
-2016-09-06 10:23:52,758 DEBUG: View 2 : 0.741666666667
-2016-09-06 10:23:52,768 DEBUG: View 3 : 0.658333333333
-2016-09-06 10:23:52,803 DEBUG: Best view : View1
-2016-09-06 10:23:52,947 DEBUG: Start: Iteration 4
-2016-09-06 10:23:52,953 DEBUG: View 0 : 0.675
-2016-09-06 10:23:52,958 DEBUG: View 1 : 0.725
-2016-09-06 10:23:52,964 DEBUG: View 2 : 0.691666666667
-2016-09-06 10:23:52,969 DEBUG: View 3 : 0.641666666667
-2016-09-06 10:23:52,998 DEBUG: Best view : View1
-2016-09-06 10:23:53,188 INFO: Start: Classification
-2016-09-06 10:23:53,569 INFO: Done: Fold number 1
-2016-09-06 10:23:53,569 INFO: Start: Fold number 2
-2016-09-06 10:23:53,589 DEBUG: Start: Iteration 1
-2016-09-06 10:23:53,594 DEBUG: View 0 : 0.603448275862
-2016-09-06 10:23:53,600 DEBUG: View 1 : 0.568965517241
-2016-09-06 10:23:53,605 DEBUG: View 2 : 0.413793103448
-2016-09-06 10:23:53,609 DEBUG: View 3 : 0.48275862069
-2016-09-06 10:23:53,631 DEBUG: Best view : View1
-2016-09-06 10:23:53,682 DEBUG: Start: Iteration 2
-2016-09-06 10:23:53,687 DEBUG: View 0 : 0.741379310345
-2016-09-06 10:23:53,693 DEBUG: View 1 : 0.724137931034
-2016-09-06 10:23:53,698 DEBUG: View 2 : 0.741379310345
-2016-09-06 10:23:53,703 DEBUG: View 3 : 0.741379310345
-2016-09-06 10:23:53,728 DEBUG: Best view : View0
-2016-09-06 10:23:53,823 DEBUG: Start: Iteration 3
-2016-09-06 10:23:53,828 DEBUG: View 0 : 0.741379310345
-2016-09-06 10:23:53,833 DEBUG: View 1 : 0.724137931034
-2016-09-06 10:23:53,839 DEBUG: View 2 : 0.741379310345
-2016-09-06 10:23:53,844 DEBUG: View 3 : 0.741379310345
-2016-09-06 10:23:53,870 DEBUG: Best view : View0
-2016-09-06 10:23:54,011 INFO: Start: Classification
-2016-09-06 10:23:54,292 INFO: Done: Fold number 2
-2016-09-06 10:23:54,292 INFO: Start: Fold number 3
-2016-09-06 10:23:54,313 DEBUG: Start: Iteration 1
-2016-09-06 10:23:54,319 DEBUG: View 0 : 0.471074380165
-2016-09-06 10:23:54,324 DEBUG: View 1 : 0.504132231405
-2016-09-06 10:23:54,329 DEBUG: View 2 : 0.504132231405
-2016-09-06 10:23:54,334 DEBUG: View 3 : 0.528925619835
-2016-09-06 10:23:54,356 DEBUG: Best view : View0
-2016-09-06 10:23:54,410 DEBUG: Start: Iteration 2
-2016-09-06 10:23:54,415 DEBUG: View 0 : 0.685950413223
-2016-09-06 10:23:54,421 DEBUG: View 1 : 0.619834710744
-2016-09-06 10:23:54,426 DEBUG: View 2 : 0.685950413223
-2016-09-06 10:23:54,431 DEBUG: View 3 : 0.760330578512
-2016-09-06 10:23:54,457 DEBUG: Best view : View3
-2016-09-06 10:23:54,556 DEBUG: Start: Iteration 3
-2016-09-06 10:23:54,562 DEBUG: View 0 : 0.685950413223
-2016-09-06 10:23:54,567 DEBUG: View 1 : 0.619834710744
-2016-09-06 10:23:54,573 DEBUG: View 2 : 0.702479338843
-2016-09-06 10:23:54,578 DEBUG: View 3 : 0.760330578512
-2016-09-06 10:23:54,605 DEBUG: Best view : View3
-2016-09-06 10:23:54,750 DEBUG: Start: Iteration 4
-2016-09-06 10:23:54,756 DEBUG: View 0 : 0.702479338843
-2016-09-06 10:23:54,761 DEBUG: View 1 : 0.669421487603
-2016-09-06 10:23:54,766 DEBUG: View 2 : 0.735537190083
-2016-09-06 10:23:54,772 DEBUG: View 3 : 0.669421487603
-2016-09-06 10:23:54,801 DEBUG: Best view : View2
-2016-09-06 10:23:54,990 INFO: Start: Classification
-2016-09-06 10:23:55,369 INFO: Done: Fold number 3
-2016-09-06 10:23:55,369 INFO: Start: Fold number 4
-2016-09-06 10:23:55,389 DEBUG: Start: Iteration 1
-2016-09-06 10:23:55,394 DEBUG: View 0 : 0.44347826087
-2016-09-06 10:23:55,399 DEBUG: View 1 : 0.547826086957
-2016-09-06 10:23:55,404 DEBUG: View 2 : 0.565217391304
-2016-09-06 10:23:55,409 DEBUG: View 3 : 0.582608695652
-2016-09-06 10:23:55,430 DEBUG: Best view : View0
-2016-09-06 10:23:55,481 DEBUG: Start: Iteration 2
-2016-09-06 10:23:55,487 DEBUG: View 0 : 0.713043478261
-2016-09-06 10:23:55,492 DEBUG: View 1 : 0.75652173913
-2016-09-06 10:23:55,497 DEBUG: View 2 : 0.747826086957
-2016-09-06 10:23:55,502 DEBUG: View 3 : 0.704347826087
-2016-09-06 10:23:55,527 DEBUG: Best view : View1
-2016-09-06 10:23:55,624 DEBUG: Start: Iteration 3
-2016-09-06 10:23:55,629 DEBUG: View 0 : 0.713043478261
-2016-09-06 10:23:55,634 DEBUG: View 1 : 0.75652173913
-2016-09-06 10:23:55,639 DEBUG: View 2 : 0.747826086957
-2016-09-06 10:23:55,644 DEBUG: View 3 : 0.704347826087
-2016-09-06 10:23:55,671 DEBUG: Best view : View1
-2016-09-06 10:23:55,811 DEBUG: Start: Iteration 4
-2016-09-06 10:23:55,817 DEBUG: View 0 : 0.730434782609
-2016-09-06 10:23:55,822 DEBUG: View 1 : 0.704347826087
-2016-09-06 10:23:55,827 DEBUG: View 2 : 0.747826086957
-2016-09-06 10:23:55,832 DEBUG: View 3 : 0.704347826087
-2016-09-06 10:23:55,860 DEBUG: Best view : View0
-2016-09-06 10:23:56,042 INFO: Start: Classification
-2016-09-06 10:23:56,413 INFO: Done: Fold number 4
-2016-09-06 10:23:56,413 INFO: Start: Fold number 5
-2016-09-06 10:23:56,433 DEBUG: Start: Iteration 1
-2016-09-06 10:23:56,438 DEBUG: View 0 : 0.529914529915
-2016-09-06 10:23:56,444 DEBUG: View 1 : 0.581196581197
-2016-09-06 10:23:56,449 DEBUG: View 2 : 0.512820512821
-2016-09-06 10:23:56,454 DEBUG: View 3 : 0.470085470085
-2016-09-06 10:23:56,475 DEBUG: Best view : View1
-2016-09-06 10:23:56,527 DEBUG: Start: Iteration 2
-2016-09-06 10:23:56,533 DEBUG: View 0 : 0.717948717949
-2016-09-06 10:23:56,538 DEBUG: View 1 : 0.752136752137
-2016-09-06 10:23:56,543 DEBUG: View 2 : 0.735042735043
-2016-09-06 10:23:56,548 DEBUG: View 3 : 0.769230769231
-2016-09-06 10:23:56,573 DEBUG: Best view : View3
-2016-09-06 10:23:56,669 DEBUG: Start: Iteration 3
-2016-09-06 10:23:56,675 DEBUG: View 0 : 0.717948717949
-2016-09-06 10:23:56,680 DEBUG: View 1 : 0.752136752137
-2016-09-06 10:23:56,686 DEBUG: View 2 : 0.735042735043
-2016-09-06 10:23:56,691 DEBUG: View 3 : 0.769230769231
-2016-09-06 10:23:56,718 DEBUG: Best view : View3
-2016-09-06 10:23:56,859 DEBUG: Start: Iteration 4
-2016-09-06 10:23:56,864 DEBUG: View 0 : 0.683760683761
-2016-09-06 10:23:56,869 DEBUG: View 1 : 0.709401709402
-2016-09-06 10:23:56,875 DEBUG: View 2 : 0.683760683761
-2016-09-06 10:23:56,879 DEBUG: View 3 : 0.735042735043
-2016-09-06 10:23:56,908 DEBUG: Best view : View0
-2016-09-06 10:23:57,092 INFO: Start: Classification
-2016-09-06 10:23:57,464 INFO: Done: Fold number 5
-2016-09-06 10:23:57,464 INFO: Done: Classification
-2016-09-06 10:23:57,464 INFO: Info: Time for Classification: 5[s]
-2016-09-06 10:23:57,464 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2b8d6b9f3c21d818349a4c0669a58802ca77b29b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7eddcdd01f2da52a31dc7fed912cbd795d584829..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.919047619048
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a458111b3453d54e7c1b486bda26f584e9ed1df1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.67619047619
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 64b9eb5de6f311b0e451413e8bbe4fdf520e8952..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102348Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 20, max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3cfc707216bde1ec9e643ad720fe8c79f3711d5d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1253f669baccf968a48dc46e2210abcc2dff2197..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.780952380952
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.780952380952
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8df47bbac622443819233ff9907d5dc04f8aa153..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.657142857143
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.657142857143
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 87a46146d4e788f4795d39708d56454ba990212f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 20, max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 87eb9ae88058508f4930eb63a4a930f3116ae210..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 464f736bea7422296e85d86c6942a303a077a245..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.480952380952
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.480952380952
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 353cb868ae1ab30c7d3b4c67bb9d730f2cc77f75..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.752380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.752380952381
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e442b3e4ad373da4e6a288bff71afc3488258573..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102349Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f728e30391572e475b16ad3ff23757f582780454..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 50877a2de51bcf4d0aa78e6ed043e9794392bd24..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.904761904762
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.904761904762
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b10d74270d9fe596fd3b26a4b2e91ca029aee3a0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 49d16072f3170f2c7b2563639b834e6a937b1237..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 20, max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 19aa2ae143577682e96519d209be9779f08d367e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ff73da4153a55c69ef7aeeca57811002784e352e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c05ffbde6f8fde0e3b2aa6e96a57ed6a7f03ebd9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a611a167f0b1ed63be7d17601cc7fb77081d5046..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102350Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f3a2e850c0cf6bfc29a980f2017d9cc57bdb72fe..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d2adb74a8fa2b0ebdba69777cb1865078cf2fc33..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.880952380952
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.880952380952
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f4bdfa1df43b7776915f7b68674fa23d1a2c18a5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.695238095238
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.695238095238
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 463c74e7d44898801d6966f64fa4437f4878ab09..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 20, max_depth : 7
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.952380952381
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d2530167cd09777b5b90f8d7edc12b7b89e16bd5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6564b15fe83a611e304c081175986beb65905e1f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ffe6f80e387462997efb7430db5cccb835b3f4ce..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dbdf7554100a303f29009ca1a729a7a567dfa35b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102351Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 624e000799b54bf18f80a0e499249ef8442ef9b1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 56a9a0dc1e83e199e5f0cd8a6bc7a4ec551cf42c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102352Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3508
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index aa39260af52aa30c0981a78d563d344973da0220..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102440-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1369 +0,0 @@
-2016-09-06 10:24:40,231 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:24:40,231 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00012634375 Gbytes /!\
-2016-09-06 10:24:45,246 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:24:45,248 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:24:45,303 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:45,303 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:45,303 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:24:45,303 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:24:45,303 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:45,303 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:45,304 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:45,304 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:45,305 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:45,305 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:45,305 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:45,305 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:45,305 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:45,305 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:45,348 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:45,348 DEBUG: Start: Training
-2016-09-06 10:24:45,350 DEBUG: Info: Time for Training: 0.0480980873108[s]
-2016-09-06 10:24:45,350 DEBUG: Done: Training
-2016-09-06 10:24:45,350 DEBUG: Start: Predicting
-2016-09-06 10:24:45,352 DEBUG: Done: Predicting
-2016-09-06 10:24:45,353 DEBUG: Start: Getting Results
-2016-09-06 10:24:45,354 DEBUG: Done: Getting Results
-2016-09-06 10:24:45,354 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:24:45,354 INFO: Done: Result Analysis
-2016-09-06 10:24:45,362 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:45,362 DEBUG: Start: Training
-2016-09-06 10:24:45,366 DEBUG: Info: Time for Training: 0.0647637844086[s]
-2016-09-06 10:24:45,366 DEBUG: Done: Training
-2016-09-06 10:24:45,366 DEBUG: Start: Predicting
-2016-09-06 10:24:45,369 DEBUG: Done: Predicting
-2016-09-06 10:24:45,369 DEBUG: Start: Getting Results
-2016-09-06 10:24:45,371 DEBUG: Done: Getting Results
-2016-09-06 10:24:45,371 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:24:45,372 INFO: Done: Result Analysis
-2016-09-06 10:24:45,442 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:45,442 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:24:45,443 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:24:45,443 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:45,443 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:45,444 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:45,444 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:45,444 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:45,444 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:45,444 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:45,444 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:45,444 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:45,444 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:45,476 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:45,476 DEBUG: Start: Training
-2016-09-06 10:24:45,476 DEBUG: Info: Time for Training: 0.0347051620483[s]
-2016-09-06 10:24:45,476 DEBUG: Done: Training
-2016-09-06 10:24:45,476 DEBUG: Start: Predicting
-2016-09-06 10:24:45,482 DEBUG: Done: Predicting
-2016-09-06 10:24:45,482 DEBUG: Start: Getting Results
-2016-09-06 10:24:45,484 DEBUG: Done: Getting Results
-2016-09-06 10:24:45,484 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.642857142857
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:24:45,484 INFO: Done: Result Analysis
-2016-09-06 10:24:45,890 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:45,890 DEBUG: Start: Training
-2016-09-06 10:24:45,958 DEBUG: Info: Time for Training: 0.516037940979[s]
-2016-09-06 10:24:45,958 DEBUG: Done: Training
-2016-09-06 10:24:45,958 DEBUG: Start: Predicting
-2016-09-06 10:24:45,965 DEBUG: Done: Predicting
-2016-09-06 10:24:45,966 DEBUG: Start: Getting Results
-2016-09-06 10:24:45,967 DEBUG: Done: Getting Results
-2016-09-06 10:24:45,967 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:24:45,967 INFO: Done: Result Analysis
-2016-09-06 10:24:46,099 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,099 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,099 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:24:46,099 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:24:46,099 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:46,099 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:46,100 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:46,100 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:46,100 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:46,100 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:46,100 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:46,100 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:46,100 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,100 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,144 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:46,144 DEBUG: Start: Training
-2016-09-06 10:24:46,144 DEBUG: Info: Time for Training: 0.0459389686584[s]
-2016-09-06 10:24:46,144 DEBUG: Done: Training
-2016-09-06 10:24:46,145 DEBUG: Start: Predicting
-2016-09-06 10:24:46,149 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:46,149 DEBUG: Start: Training
-2016-09-06 10:24:46,162 DEBUG: Done: Predicting
-2016-09-06 10:24:46,162 DEBUG: Start: Getting Results
-2016-09-06 10:24:46,164 DEBUG: Done: Getting Results
-2016-09-06 10:24:46,164 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,164 INFO: Done: Result Analysis
-2016-09-06 10:24:46,168 DEBUG: Info: Time for Training: 0.0690729618073[s]
-2016-09-06 10:24:46,168 DEBUG: Done: Training
-2016-09-06 10:24:46,168 DEBUG: Start: Predicting
-2016-09-06 10:24:46,171 DEBUG: Done: Predicting
-2016-09-06 10:24:46,171 DEBUG: Start: Getting Results
-2016-09-06 10:24:46,172 DEBUG: Done: Getting Results
-2016-09-06 10:24:46,172 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,173 INFO: Done: Result Analysis
-2016-09-06 10:24:46,250 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,250 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,251 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:24:46,251 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:24:46,251 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:46,251 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:46,252 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:46,252 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:24:46,252 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:46,252 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:24:46,253 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:46,253 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:46,253 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,253 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,325 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:46,325 DEBUG: Start: Training
-2016-09-06 10:24:46,329 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:46,330 DEBUG: Start: Training
-2016-09-06 10:24:46,348 DEBUG: Info: Time for Training: 0.0991640090942[s]
-2016-09-06 10:24:46,349 DEBUG: Done: Training
-2016-09-06 10:24:46,349 DEBUG: Start: Predicting
-2016-09-06 10:24:46,354 DEBUG: Info: Time for Training: 0.104753017426[s]
-2016-09-06 10:24:46,354 DEBUG: Done: Training
-2016-09-06 10:24:46,354 DEBUG: Start: Predicting
-2016-09-06 10:24:46,356 DEBUG: Done: Predicting
-2016-09-06 10:24:46,357 DEBUG: Start: Getting Results
-2016-09-06 10:24:46,359 DEBUG: Done: Getting Results
-2016-09-06 10:24:46,359 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,359 DEBUG: Done: Predicting
-2016-09-06 10:24:46,359 INFO: Done: Result Analysis
-2016-09-06 10:24:46,359 DEBUG: Start: Getting Results
-2016-09-06 10:24:46,361 DEBUG: Done: Getting Results
-2016-09-06 10:24:46,361 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,361 INFO: Done: Result Analysis
-2016-09-06 10:24:46,499 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,499 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,500 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:24:46,500 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:24:46,500 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:46,500 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:46,500 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:46,500 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:46,501 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:46,501 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:46,501 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:46,501 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:46,501 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,501 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,540 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:46,541 DEBUG: Start: Training
-2016-09-06 10:24:46,543 DEBUG: Info: Time for Training: 0.0447499752045[s]
-2016-09-06 10:24:46,543 DEBUG: Done: Training
-2016-09-06 10:24:46,544 DEBUG: Start: Predicting
-2016-09-06 10:24:46,546 DEBUG: Done: Predicting
-2016-09-06 10:24:46,546 DEBUG: Start: Getting Results
-2016-09-06 10:24:46,548 DEBUG: Done: Getting Results
-2016-09-06 10:24:46,548 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,548 INFO: Done: Result Analysis
-2016-09-06 10:24:46,555 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:46,555 DEBUG: Start: Training
-2016-09-06 10:24:46,560 DEBUG: Info: Time for Training: 0.0609669685364[s]
-2016-09-06 10:24:46,560 DEBUG: Done: Training
-2016-09-06 10:24:46,560 DEBUG: Start: Predicting
-2016-09-06 10:24:46,563 DEBUG: Done: Predicting
-2016-09-06 10:24:46,563 DEBUG: Start: Getting Results
-2016-09-06 10:24:46,565 DEBUG: Done: Getting Results
-2016-09-06 10:24:46,565 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,565 INFO: Done: Result Analysis
-2016-09-06 10:24:46,647 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,647 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:46,647 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:24:46,647 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:24:46,647 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:46,647 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:46,648 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:46,648 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:46,648 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:46,648 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:46,649 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:46,649 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:46,649 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,649 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:46,681 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:46,681 DEBUG: Start: Training
-2016-09-06 10:24:46,681 DEBUG: Info: Time for Training: 0.0352909564972[s]
-2016-09-06 10:24:46,681 DEBUG: Done: Training
-2016-09-06 10:24:46,682 DEBUG: Start: Predicting
-2016-09-06 10:24:46,688 DEBUG: Done: Predicting
-2016-09-06 10:24:46,688 DEBUG: Start: Getting Results
-2016-09-06 10:24:46,689 DEBUG: Done: Getting Results
-2016-09-06 10:24:46,689 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:46,690 INFO: Done: Result Analysis
-2016-09-06 10:24:47,105 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:47,106 DEBUG: Start: Training
-2016-09-06 10:24:47,176 DEBUG: Info: Time for Training: 0.529899120331[s]
-2016-09-06 10:24:47,176 DEBUG: Done: Training
-2016-09-06 10:24:47,176 DEBUG: Start: Predicting
-2016-09-06 10:24:47,184 DEBUG: Done: Predicting
-2016-09-06 10:24:47,184 DEBUG: Start: Getting Results
-2016-09-06 10:24:47,185 DEBUG: Done: Getting Results
-2016-09-06 10:24:47,185 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,186 INFO: Done: Result Analysis
-2016-09-06 10:24:47,297 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,297 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,297 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:24:47,297 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:24:47,297 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:47,297 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:47,298 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:47,298 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:47,298 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:47,298 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:47,299 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:47,299 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:47,299 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,299 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,369 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:47,369 DEBUG: Start: Training
-2016-09-06 10:24:47,370 DEBUG: Info: Time for Training: 0.0735681056976[s]
-2016-09-06 10:24:47,370 DEBUG: Done: Training
-2016-09-06 10:24:47,370 DEBUG: Start: Predicting
-2016-09-06 10:24:47,376 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:47,376 DEBUG: Start: Training
-2016-09-06 10:24:47,387 DEBUG: Done: Predicting
-2016-09-06 10:24:47,388 DEBUG: Start: Getting Results
-2016-09-06 10:24:47,390 DEBUG: Done: Getting Results
-2016-09-06 10:24:47,390 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.377777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,390 INFO: Done: Result Analysis
-2016-09-06 10:24:47,402 DEBUG: Info: Time for Training: 0.106291055679[s]
-2016-09-06 10:24:47,403 DEBUG: Done: Training
-2016-09-06 10:24:47,403 DEBUG: Start: Predicting
-2016-09-06 10:24:47,406 DEBUG: Done: Predicting
-2016-09-06 10:24:47,406 DEBUG: Start: Getting Results
-2016-09-06 10:24:47,408 DEBUG: Done: Getting Results
-2016-09-06 10:24:47,408 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,408 INFO: Done: Result Analysis
-2016-09-06 10:24:47,547 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,547 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,547 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:24:47,547 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:24:47,548 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:47,548 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:47,549 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:47,549 DEBUG: Info: Shape X_train:(210, 16), Length of y_train:210
-2016-09-06 10:24:47,549 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:47,549 DEBUG: Info: Shape X_test:(90, 16), Length of y_test:90
-2016-09-06 10:24:47,549 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:47,549 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:47,549 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,549 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,623 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:47,623 DEBUG: Start: Training
-2016-09-06 10:24:47,631 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:47,631 DEBUG: Start: Training
-2016-09-06 10:24:47,648 DEBUG: Info: Time for Training: 0.102329015732[s]
-2016-09-06 10:24:47,648 DEBUG: Done: Training
-2016-09-06 10:24:47,649 DEBUG: Start: Predicting
-2016-09-06 10:24:47,657 DEBUG: Done: Predicting
-2016-09-06 10:24:47,657 DEBUG: Start: Getting Results
-2016-09-06 10:24:47,658 DEBUG: Info: Time for Training: 0.112071990967[s]
-2016-09-06 10:24:47,658 DEBUG: Done: Training
-2016-09-06 10:24:47,658 DEBUG: Start: Predicting
-2016-09-06 10:24:47,659 DEBUG: Done: Getting Results
-2016-09-06 10:24:47,659 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,660 INFO: Done: Result Analysis
-2016-09-06 10:24:47,663 DEBUG: Done: Predicting
-2016-09-06 10:24:47,663 DEBUG: Start: Getting Results
-2016-09-06 10:24:47,664 DEBUG: Done: Getting Results
-2016-09-06 10:24:47,665 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,665 INFO: Done: Result Analysis
-2016-09-06 10:24:47,798 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,798 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,798 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:24:47,798 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:24:47,799 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:47,799 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:47,799 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:47,799 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:47,800 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:47,800 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:47,800 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:47,800 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:47,800 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,800 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,837 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:47,837 DEBUG: Start: Training
-2016-09-06 10:24:47,839 DEBUG: Info: Time for Training: 0.0416760444641[s]
-2016-09-06 10:24:47,839 DEBUG: Done: Training
-2016-09-06 10:24:47,839 DEBUG: Start: Predicting
-2016-09-06 10:24:47,842 DEBUG: Done: Predicting
-2016-09-06 10:24:47,842 DEBUG: Start: Getting Results
-2016-09-06 10:24:47,843 DEBUG: Done: Getting Results
-2016-09-06 10:24:47,843 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,844 INFO: Done: Result Analysis
-2016-09-06 10:24:47,852 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:47,852 DEBUG: Start: Training
-2016-09-06 10:24:47,856 DEBUG: Info: Time for Training: 0.0591020584106[s]
-2016-09-06 10:24:47,856 DEBUG: Done: Training
-2016-09-06 10:24:47,857 DEBUG: Start: Predicting
-2016-09-06 10:24:47,860 DEBUG: Done: Predicting
-2016-09-06 10:24:47,860 DEBUG: Start: Getting Results
-2016-09-06 10:24:47,862 DEBUG: Done: Getting Results
-2016-09-06 10:24:47,862 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,862 INFO: Done: Result Analysis
-2016-09-06 10:24:47,951 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,951 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:47,952 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:24:47,952 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:24:47,952 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:47,952 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:47,953 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:47,953 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:47,953 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:47,953 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:47,953 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:47,953 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:47,953 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,953 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:47,985 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:47,985 DEBUG: Start: Training
-2016-09-06 10:24:47,986 DEBUG: Info: Time for Training: 0.0345377922058[s]
-2016-09-06 10:24:47,986 DEBUG: Done: Training
-2016-09-06 10:24:47,986 DEBUG: Start: Predicting
-2016-09-06 10:24:47,992 DEBUG: Done: Predicting
-2016-09-06 10:24:47,992 DEBUG: Start: Getting Results
-2016-09-06 10:24:47,994 DEBUG: Done: Getting Results
-2016-09-06 10:24:47,994 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:24:47,994 INFO: Done: Result Analysis
-2016-09-06 10:24:48,411 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:48,411 DEBUG: Start: Training
-2016-09-06 10:24:48,481 DEBUG: Info: Time for Training: 0.529783964157[s]
-2016-09-06 10:24:48,481 DEBUG: Done: Training
-2016-09-06 10:24:48,481 DEBUG: Start: Predicting
-2016-09-06 10:24:48,489 DEBUG: Done: Predicting
-2016-09-06 10:24:48,489 DEBUG: Start: Getting Results
-2016-09-06 10:24:48,490 DEBUG: Done: Getting Results
-2016-09-06 10:24:48,490 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:24:48,491 INFO: Done: Result Analysis
-2016-09-06 10:24:48,607 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:48,607 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:48,608 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:24:48,608 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:24:48,608 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:48,608 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:48,609 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:48,609 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:48,609 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:48,609 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:48,609 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:48,609 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:48,609 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:48,609 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:48,677 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:48,677 DEBUG: Start: Training
-2016-09-06 10:24:48,678 DEBUG: Info: Time for Training: 0.0721070766449[s]
-2016-09-06 10:24:48,679 DEBUG: Done: Training
-2016-09-06 10:24:48,679 DEBUG: Start: Predicting
-2016-09-06 10:24:48,684 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:48,684 DEBUG: Start: Training
-2016-09-06 10:24:48,693 DEBUG: Done: Predicting
-2016-09-06 10:24:48,693 DEBUG: Start: Getting Results
-2016-09-06 10:24:48,694 DEBUG: Done: Getting Results
-2016-09-06 10:24:48,694 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.628571428571
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:24:48,695 INFO: Done: Result Analysis
-2016-09-06 10:24:48,703 DEBUG: Info: Time for Training: 0.0963509082794[s]
-2016-09-06 10:24:48,703 DEBUG: Done: Training
-2016-09-06 10:24:48,703 DEBUG: Start: Predicting
-2016-09-06 10:24:48,706 DEBUG: Done: Predicting
-2016-09-06 10:24:48,707 DEBUG: Start: Getting Results
-2016-09-06 10:24:48,708 DEBUG: Done: Getting Results
-2016-09-06 10:24:48,708 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.42380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.42380952381
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:48,708 INFO: Done: Result Analysis
-2016-09-06 10:24:48,851 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:48,851 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:48,851 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:24:48,851 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:24:48,852 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:48,852 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:48,852 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:48,852 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:48,852 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 10:24:48,852 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:48,853 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 10:24:48,853 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:48,853 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:48,853 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:48,902 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:48,902 DEBUG: Start: Training
-2016-09-06 10:24:48,903 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:48,903 DEBUG: Start: Training
-2016-09-06 10:24:48,918 DEBUG: Info: Time for Training: 0.066841840744[s]
-2016-09-06 10:24:48,918 DEBUG: Done: Training
-2016-09-06 10:24:48,918 DEBUG: Start: Predicting
-2016-09-06 10:24:48,921 DEBUG: Done: Predicting
-2016-09-06 10:24:48,922 DEBUG: Start: Getting Results
-2016-09-06 10:24:48,922 DEBUG: Info: Time for Training: 0.0710518360138[s]
-2016-09-06 10:24:48,922 DEBUG: Done: Training
-2016-09-06 10:24:48,922 DEBUG: Start: Predicting
-2016-09-06 10:24:48,923 DEBUG: Done: Getting Results
-2016-09-06 10:24:48,923 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:24:48,923 INFO: Done: Result Analysis
-2016-09-06 10:24:48,928 DEBUG: Done: Predicting
-2016-09-06 10:24:48,928 DEBUG: Start: Getting Results
-2016-09-06 10:24:48,929 DEBUG: Done: Getting Results
-2016-09-06 10:24:48,929 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:48,929 INFO: Done: Result Analysis
-2016-09-06 10:24:48,999 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,000 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:24:49,000 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:49,000 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,000 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:24:49,001 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:49,001 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,001 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,001 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,001 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:49,002 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,002 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,002 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:49,002 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,040 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:49,040 DEBUG: Start: Training
-2016-09-06 10:24:49,042 DEBUG: Info: Time for Training: 0.0424060821533[s]
-2016-09-06 10:24:49,042 DEBUG: Done: Training
-2016-09-06 10:24:49,042 DEBUG: Start: Predicting
-2016-09-06 10:24:49,045 DEBUG: Done: Predicting
-2016-09-06 10:24:49,046 DEBUG: Start: Getting Results
-2016-09-06 10:24:49,047 DEBUG: Done: Getting Results
-2016-09-06 10:24:49,047 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,048 INFO: Done: Result Analysis
-2016-09-06 10:24:49,051 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:49,051 DEBUG: Start: Training
-2016-09-06 10:24:49,054 DEBUG: Info: Time for Training: 0.0557050704956[s]
-2016-09-06 10:24:49,055 DEBUG: Done: Training
-2016-09-06 10:24:49,055 DEBUG: Start: Predicting
-2016-09-06 10:24:49,057 DEBUG: Done: Predicting
-2016-09-06 10:24:49,058 DEBUG: Start: Getting Results
-2016-09-06 10:24:49,060 DEBUG: Done: Getting Results
-2016-09-06 10:24:49,060 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,060 INFO: Done: Result Analysis
-2016-09-06 10:24:49,150 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,151 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:24:49,151 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:49,151 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,152 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,152 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:49,152 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,152 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,152 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:24:49,152 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:49,153 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,153 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,154 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:49,154 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,185 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:49,185 DEBUG: Start: Training
-2016-09-06 10:24:49,185 DEBUG: Info: Time for Training: 0.0358011722565[s]
-2016-09-06 10:24:49,185 DEBUG: Done: Training
-2016-09-06 10:24:49,186 DEBUG: Start: Predicting
-2016-09-06 10:24:49,192 DEBUG: Done: Predicting
-2016-09-06 10:24:49,192 DEBUG: Start: Getting Results
-2016-09-06 10:24:49,193 DEBUG: Done: Getting Results
-2016-09-06 10:24:49,193 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,194 INFO: Done: Result Analysis
-2016-09-06 10:24:49,647 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:49,647 DEBUG: Start: Training
-2016-09-06 10:24:49,717 DEBUG: Info: Time for Training: 0.565485954285[s]
-2016-09-06 10:24:49,717 DEBUG: Done: Training
-2016-09-06 10:24:49,717 DEBUG: Start: Predicting
-2016-09-06 10:24:49,725 DEBUG: Done: Predicting
-2016-09-06 10:24:49,725 DEBUG: Start: Getting Results
-2016-09-06 10:24:49,726 DEBUG: Done: Getting Results
-2016-09-06 10:24:49,726 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,727 INFO: Done: Result Analysis
-2016-09-06 10:24:49,800 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:24:49,801 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:24:49,801 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:49,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:24:49,801 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:24:49,802 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,802 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:24:49,802 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,802 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:24:49,802 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:49,802 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,802 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:24:49,802 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:24:49,847 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:49,847 DEBUG: Start: Training
-2016-09-06 10:24:49,848 DEBUG: Info: Time for Training: 0.0476620197296[s]
-2016-09-06 10:24:49,848 DEBUG: Done: Training
-2016-09-06 10:24:49,848 DEBUG: Start: Predicting
-2016-09-06 10:24:49,854 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:24:49,854 DEBUG: Start: Training
-2016-09-06 10:24:49,869 DEBUG: Done: Predicting
-2016-09-06 10:24:49,870 DEBUG: Start: Getting Results
-2016-09-06 10:24:49,872 DEBUG: Done: Getting Results
-2016-09-06 10:24:49,872 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,872 INFO: Done: Result Analysis
-2016-09-06 10:24:49,879 DEBUG: Info: Time for Training: 0.0796000957489[s]
-2016-09-06 10:24:49,879 DEBUG: Done: Training
-2016-09-06 10:24:49,879 DEBUG: Start: Predicting
-2016-09-06 10:24:49,883 DEBUG: Done: Predicting
-2016-09-06 10:24:49,883 DEBUG: Start: Getting Results
-2016-09-06 10:24:49,884 DEBUG: Done: Getting Results
-2016-09-06 10:24:49,884 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:24:49,884 INFO: Done: Result Analysis
-2016-09-06 10:24:50,099 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:24:50,100 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:24:50,100 INFO: Info: Shape of View0 :(300, 9)
-2016-09-06 10:24:50,101 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 10:24:50,101 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:24:50,101 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 10:24:50,101 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:24:50,102 INFO: Info: Shape of View3 :(300, 10)
-2016-09-06 10:24:50,102 INFO: Info: Shape of View0 :(300, 9)
-2016-09-06 10:24:50,102 INFO: Done: Read Database Files
-2016-09-06 10:24:50,102 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:24:50,102 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 10:24:50,103 INFO: Info: Shape of View2 :(300, 11)
-2016-09-06 10:24:50,104 INFO: Info: Shape of View3 :(300, 10)
-2016-09-06 10:24:50,104 INFO: Done: Read Database Files
-2016-09-06 10:24:50,104 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:24:50,106 INFO: Done: Determine validation split
-2016-09-06 10:24:50,106 INFO: Start: Determine 5 folds
-2016-09-06 10:24:50,108 INFO: Done: Determine validation split
-2016-09-06 10:24:50,108 INFO: Start: Determine 5 folds
-2016-09-06 10:24:50,116 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:24:50,116 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:24:50,116 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:24:50,117 INFO: Done: Determine folds
-2016-09-06 10:24:50,117 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:24:50,117 INFO: Start: Classification
-2016-09-06 10:24:50,117 INFO: Start: Fold number 1
-2016-09-06 10:24:50,118 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:24:50,118 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:24:50,118 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:24:50,118 INFO: Done: Determine folds
-2016-09-06 10:24:50,119 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:24:50,119 INFO: Start: Classification
-2016-09-06 10:24:50,119 INFO: Start: Fold number 1
-2016-09-06 10:24:50,149 DEBUG: Start: Iteration 1
-2016-09-06 10:24:50,158 DEBUG: View 0 : 0.479289940828
-2016-09-06 10:24:50,167 DEBUG: View 1 : 0.526627218935
-2016-09-06 10:24:50,175 DEBUG: View 2 : 0.538461538462
-2016-09-06 10:24:50,184 DEBUG: View 3 : 0.532544378698
-2016-09-06 10:24:50,217 DEBUG: Best view : View2
-2016-09-06 10:24:50,296 DEBUG: Start: Iteration 2
-2016-09-06 10:24:50,304 DEBUG: View 0 : 0.704142011834
-2016-09-06 10:24:50,312 DEBUG: View 1 : 0.644970414201
-2016-09-06 10:24:50,320 DEBUG: View 2 : 0.668639053254
-2016-09-06 10:24:50,328 DEBUG: View 3 : 0.739644970414
-2016-09-06 10:24:50,366 DEBUG: Best view : View3
-2016-09-06 10:24:50,510 DEBUG: Start: Iteration 3
-2016-09-06 10:24:50,521 DEBUG: View 0 : 0.704142011834
-2016-09-06 10:24:50,529 DEBUG: View 1 : 0.644970414201
-2016-09-06 10:24:50,537 DEBUG: View 2 : 0.668639053254
-2016-09-06 10:24:50,544 DEBUG: View 3 : 0.739644970414
-2016-09-06 10:24:50,582 DEBUG: Best view : View3
-2016-09-06 10:24:50,783 INFO: Start: Classification
-2016-09-06 10:24:51,128 INFO: Done: Fold number 1
-2016-09-06 10:24:51,128 INFO: Start: Fold number 2
-2016-09-06 10:24:51,157 DEBUG: Start: Iteration 1
-2016-09-06 10:24:51,164 DEBUG: View 0 : 0.502958579882
-2016-09-06 10:24:51,172 DEBUG: View 1 : 0.526627218935
-2016-09-06 10:24:51,179 DEBUG: View 2 : 0.479289940828
-2016-09-06 10:24:51,186 DEBUG: View 3 : 0.562130177515
-2016-09-06 10:24:51,216 DEBUG: Best view : View0
-2016-09-06 10:24:51,291 DEBUG: Start: Iteration 2
-2016-09-06 10:24:51,298 DEBUG: View 0 : 0.656804733728
-2016-09-06 10:24:51,306 DEBUG: View 1 : 0.710059171598
-2016-09-06 10:24:51,313 DEBUG: View 2 : 0.745562130178
-2016-09-06 10:24:51,321 DEBUG: View 3 : 0.757396449704
-2016-09-06 10:24:51,357 DEBUG: Best view : View3
-2016-09-06 10:24:51,497 DEBUG: Start: Iteration 3
-2016-09-06 10:24:51,505 DEBUG: View 0 : 0.656804733728
-2016-09-06 10:24:51,512 DEBUG: View 1 : 0.710059171598
-2016-09-06 10:24:51,520 DEBUG: View 2 : 0.745562130178
-2016-09-06 10:24:51,527 DEBUG: View 3 : 0.757396449704
-2016-09-06 10:24:51,566 DEBUG: Best view : View3
-2016-09-06 10:24:51,768 DEBUG: Start: Iteration 4
-2016-09-06 10:24:51,776 DEBUG: View 0 : 0.639053254438
-2016-09-06 10:24:51,783 DEBUG: View 1 : 0.627218934911
-2016-09-06 10:24:51,791 DEBUG: View 2 : 0.721893491124
-2016-09-06 10:24:51,798 DEBUG: View 3 : 0.692307692308
-2016-09-06 10:24:51,839 DEBUG: Best view : View2
-2016-09-06 10:24:52,104 INFO: Start: Classification
-2016-09-06 10:24:52,553 INFO: Done: Fold number 2
-2016-09-06 10:24:52,553 INFO: Start: Fold number 3
-2016-09-06 10:24:52,582 DEBUG: Start: Iteration 1
-2016-09-06 10:24:52,589 DEBUG: View 0 : 0.467455621302
-2016-09-06 10:24:52,596 DEBUG: View 1 : 0.550295857988
-2016-09-06 10:24:52,603 DEBUG: View 2 : 0.520710059172
-2016-09-06 10:24:52,609 DEBUG: View 3 : 0.479289940828
-2016-09-06 10:24:52,640 DEBUG: Best view : View0
-2016-09-06 10:24:52,714 DEBUG: Start: Iteration 2
-2016-09-06 10:24:52,722 DEBUG: View 0 : 0.721893491124
-2016-09-06 10:24:52,729 DEBUG: View 1 : 0.674556213018
-2016-09-06 10:24:52,737 DEBUG: View 2 : 0.656804733728
-2016-09-06 10:24:52,744 DEBUG: View 3 : 0.674556213018
-2016-09-06 10:24:52,780 DEBUG: Best view : View0
-2016-09-06 10:24:52,919 DEBUG: Start: Iteration 3
-2016-09-06 10:24:52,926 DEBUG: View 0 : 0.721893491124
-2016-09-06 10:24:52,933 DEBUG: View 1 : 0.674556213018
-2016-09-06 10:24:52,941 DEBUG: View 2 : 0.656804733728
-2016-09-06 10:24:52,948 DEBUG: View 3 : 0.674556213018
-2016-09-06 10:24:52,987 DEBUG: Best view : View0
-2016-09-06 10:24:53,188 INFO: Start: Classification
-2016-09-06 10:24:53,527 INFO: Done: Fold number 3
-2016-09-06 10:24:53,527 INFO: Start: Fold number 4
-2016-09-06 10:24:53,556 DEBUG: Start: Iteration 1
-2016-09-06 10:24:53,563 DEBUG: View 0 : 0.526627218935
-2016-09-06 10:24:53,570 DEBUG: View 1 : 0.485207100592
-2016-09-06 10:24:53,577 DEBUG: View 2 : 0.514792899408
-2016-09-06 10:24:53,584 DEBUG: View 3 : 0.497041420118
-2016-09-06 10:24:53,614 DEBUG: Best view : View1
-2016-09-06 10:24:53,689 DEBUG: Start: Iteration 2
-2016-09-06 10:24:53,696 DEBUG: View 0 : 0.692307692308
-2016-09-06 10:24:53,704 DEBUG: View 1 : 0.680473372781
-2016-09-06 10:24:53,711 DEBUG: View 2 : 0.739644970414
-2016-09-06 10:24:53,719 DEBUG: View 3 : 0.615384615385
-2016-09-06 10:24:53,755 DEBUG: Best view : View2
-2016-09-06 10:24:53,893 DEBUG: Start: Iteration 3
-2016-09-06 10:24:53,900 DEBUG: View 0 : 0.692307692308
-2016-09-06 10:24:53,908 DEBUG: View 1 : 0.615384615385
-2016-09-06 10:24:53,915 DEBUG: View 2 : 0.739644970414
-2016-09-06 10:24:53,923 DEBUG: View 3 : 0.615384615385
-2016-09-06 10:24:53,960 DEBUG: Best view : View2
-2016-09-06 10:24:54,163 INFO: Start: Classification
-2016-09-06 10:24:54,501 INFO: Done: Fold number 4
-2016-09-06 10:24:54,501 INFO: Start: Fold number 5
-2016-09-06 10:24:54,530 DEBUG: Start: Iteration 1
-2016-09-06 10:24:54,538 DEBUG: View 0 : 0.538461538462
-2016-09-06 10:24:54,545 DEBUG: View 1 : 0.556213017751
-2016-09-06 10:24:54,552 DEBUG: View 2 : 0.479289940828
-2016-09-06 10:24:54,560 DEBUG: View 3 : 0.550295857988
-2016-09-06 10:24:54,590 DEBUG: Best view : View2
-2016-09-06 10:24:54,665 DEBUG: Start: Iteration 2
-2016-09-06 10:24:54,672 DEBUG: View 0 : 0.686390532544
-2016-09-06 10:24:54,680 DEBUG: View 1 : 0.710059171598
-2016-09-06 10:24:54,687 DEBUG: View 2 : 0.745562130178
-2016-09-06 10:24:54,695 DEBUG: View 3 : 0.763313609467
-2016-09-06 10:24:54,731 DEBUG: Best view : View3
-2016-09-06 10:24:54,871 DEBUG: Start: Iteration 3
-2016-09-06 10:24:54,878 DEBUG: View 0 : 0.710059171598
-2016-09-06 10:24:54,886 DEBUG: View 1 : 0.710059171598
-2016-09-06 10:24:54,893 DEBUG: View 2 : 0.745562130178
-2016-09-06 10:24:54,900 DEBUG: View 3 : 0.763313609467
-2016-09-06 10:24:54,939 DEBUG: Best view : View3
-2016-09-06 10:24:55,142 DEBUG: Start: Iteration 4
-2016-09-06 10:24:55,149 DEBUG: View 0 : 0.680473372781
-2016-09-06 10:24:55,157 DEBUG: View 1 : 0.668639053254
-2016-09-06 10:24:55,164 DEBUG: View 2 : 0.775147928994
-2016-09-06 10:24:55,172 DEBUG: View 3 : 0.698224852071
-2016-09-06 10:24:55,213 DEBUG: Best view : View2
-2016-09-06 10:24:55,477 INFO: Start: Classification
-2016-09-06 10:24:55,929 INFO: Done: Fold number 5
-2016-09-06 10:24:55,930 INFO: Done: Classification
-2016-09-06 10:24:55,930 INFO: Info: Time for Classification: 5[s]
-2016-09-06 10:24:55,930 INFO: Start: Result Analysis for Mumbo
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0a29c62f3e32e23b6606421aa079b827c934d979..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 50d1ffc426c81b549e261486aba92e796abadc90..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ec4b6b244ea621fbbd2c9e35b97a7071e3e032d4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.642857142857
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f6c913520ecd71b9863e4ba56aa82083337a032e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102445Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 99221590745bb8c9c5fbfe598775de43496596b2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ff3894ab6513257fb8739eecf6b789e65e116e08..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d1109d9fb625d0c89a52880e5ae0e964b55c72db..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 68f5fddd55b9eb79dfe3605602826ad8eb27eeea..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.595238095238
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2ddf8a810a4f724be229872a570637dbb5a98ce8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8f0337470a622cce8d1e40718deca066ac70b21c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 417cdc1146fec8514d165717f1911019c6490ab6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102446Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 803d6342fe6f3366e9a977258b75c4f6c889fe3a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1bfba82f407e7d9fcfc31aba43fa118ba624a621..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7fbf2011cd3629064447a1e63db67afd753d2b49..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4792d66c67f3f3cf27bb115c07f5ae1096f1fd75..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 886be2013e2a96b1192f0998a12b2f09e139d234..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.377777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8612a084aa35a3416a20728aa22570233df8b6ee..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2421faf6a81d4b9a998547fd8da87efdf09285de..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bfb5e334cf003155dde149912bfb030e9532cec5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102447Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 16)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 90bc016315849eebfbd723a5967ae7e704558590..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5cf265c51a8b13b86719047a88d06c3e05806e5d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.628571428571
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.628571428571
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d333a548beb19b94e9a0ec49d870ba2f197543eb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.42380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.42380952381
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9bb3ab9502b26f86a0dff649d5b336283db18d46..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3985361b9097bbf774cf8a63fc5aac32b76e584b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102448Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 81641b03b13251625bf3108426bdabb188c77568..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b6a08cb0d94534e1fb6fdb548bccedbf64c80c4c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 774d9b323e609480cea44d712206ebc1dce8e8fc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 26
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ca851391b9530aaf1fa9ae5626dbd7d7fee8bb5b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 26, max_depth : 18
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4672960d9a77459d08cfec74f582ca76b108b6cc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7f7f53ac0c08482c08b5117b9eaa499e614a38b6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102449Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5274
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 5e79d40609d8f17f6157d51eb27c0e6249fe4b7e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102543-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1623 +0,0 @@
-2016-09-06 10:25:43,492 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:25:43,492 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00020134375 Gbytes /!\
-2016-09-06 10:25:48,507 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:25:48,510 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:25:48,559 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:48,559 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:48,560 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:25:48,560 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:25:48,560 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:48,560 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:48,560 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:48,560 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:48,561 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:48,561 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:48,561 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:48,561 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:48,561 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:48,561 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:48,604 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:48,604 DEBUG: Start: Training
-2016-09-06 10:25:48,607 DEBUG: Info: Time for Training: 0.0481219291687[s]
-2016-09-06 10:25:48,607 DEBUG: Done: Training
-2016-09-06 10:25:48,607 DEBUG: Start: Predicting
-2016-09-06 10:25:48,609 DEBUG: Done: Predicting
-2016-09-06 10:25:48,610 DEBUG: Start: Getting Results
-2016-09-06 10:25:48,611 DEBUG: Done: Getting Results
-2016-09-06 10:25:48,611 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:25:48,611 INFO: Done: Result Analysis
-2016-09-06 10:25:48,619 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:48,619 DEBUG: Start: Training
-2016-09-06 10:25:48,624 DEBUG: Info: Time for Training: 0.0653121471405[s]
-2016-09-06 10:25:48,624 DEBUG: Done: Training
-2016-09-06 10:25:48,624 DEBUG: Start: Predicting
-2016-09-06 10:25:48,627 DEBUG: Done: Predicting
-2016-09-06 10:25:48,627 DEBUG: Start: Getting Results
-2016-09-06 10:25:48,629 DEBUG: Done: Getting Results
-2016-09-06 10:25:48,629 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:25:48,629 INFO: Done: Result Analysis
-2016-09-06 10:25:48,709 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:48,709 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:48,710 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:25:48,710 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:25:48,710 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:48,710 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:48,710 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:48,710 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:48,711 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:48,711 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:48,711 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:48,711 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:48,711 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:48,711 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:48,767 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:48,767 DEBUG: Start: Training
-2016-09-06 10:25:48,768 DEBUG: Info: Time for Training: 0.0595369338989[s]
-2016-09-06 10:25:48,768 DEBUG: Done: Training
-2016-09-06 10:25:48,768 DEBUG: Start: Predicting
-2016-09-06 10:25:48,780 DEBUG: Done: Predicting
-2016-09-06 10:25:48,780 DEBUG: Start: Getting Results
-2016-09-06 10:25:48,782 DEBUG: Done: Getting Results
-2016-09-06 10:25:48,782 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:25:48,783 INFO: Done: Result Analysis
-2016-09-06 10:25:49,068 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:49,068 DEBUG: Start: Training
-2016-09-06 10:25:49,115 DEBUG: Info: Time for Training: 0.406928062439[s]
-2016-09-06 10:25:49,116 DEBUG: Done: Training
-2016-09-06 10:25:49,116 DEBUG: Start: Predicting
-2016-09-06 10:25:49,122 DEBUG: Done: Predicting
-2016-09-06 10:25:49,122 DEBUG: Start: Getting Results
-2016-09-06 10:25:49,123 DEBUG: Done: Getting Results
-2016-09-06 10:25:49,123 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 18, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,123 INFO: Done: Result Analysis
-2016-09-06 10:25:49,263 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,263 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,264 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:25:49,264 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:25:49,264 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:49,264 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:49,265 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,265 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,265 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,265 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,265 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:49,265 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:49,265 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,265 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,313 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:49,313 DEBUG: Start: Training
-2016-09-06 10:25:49,314 DEBUG: Info: Time for Training: 0.0512311458588[s]
-2016-09-06 10:25:49,314 DEBUG: Done: Training
-2016-09-06 10:25:49,314 DEBUG: Start: Predicting
-2016-09-06 10:25:49,320 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:49,320 DEBUG: Start: Training
-2016-09-06 10:25:49,330 DEBUG: Done: Predicting
-2016-09-06 10:25:49,330 DEBUG: Start: Getting Results
-2016-09-06 10:25:49,332 DEBUG: Done: Getting Results
-2016-09-06 10:25:49,332 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,332 INFO: Done: Result Analysis
-2016-09-06 10:25:49,344 DEBUG: Info: Time for Training: 0.0809261798859[s]
-2016-09-06 10:25:49,344 DEBUG: Done: Training
-2016-09-06 10:25:49,344 DEBUG: Start: Predicting
-2016-09-06 10:25:49,348 DEBUG: Done: Predicting
-2016-09-06 10:25:49,348 DEBUG: Start: Getting Results
-2016-09-06 10:25:49,349 DEBUG: Done: Getting Results
-2016-09-06 10:25:49,349 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,349 INFO: Done: Result Analysis
-2016-09-06 10:25:49,411 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,411 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,412 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:25:49,412 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:25:49,412 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:49,412 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:49,413 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,413 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,413 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,413 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:49,413 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,413 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,413 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:49,413 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,462 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:49,462 DEBUG: Start: Training
-2016-09-06 10:25:49,472 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:49,472 DEBUG: Start: Training
-2016-09-06 10:25:49,480 DEBUG: Info: Time for Training: 0.0694200992584[s]
-2016-09-06 10:25:49,480 DEBUG: Done: Training
-2016-09-06 10:25:49,480 DEBUG: Start: Predicting
-2016-09-06 10:25:49,486 DEBUG: Done: Predicting
-2016-09-06 10:25:49,486 DEBUG: Start: Getting Results
-2016-09-06 10:25:49,487 DEBUG: Done: Getting Results
-2016-09-06 10:25:49,488 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,488 INFO: Done: Result Analysis
-2016-09-06 10:25:49,494 DEBUG: Info: Time for Training: 0.0838282108307[s]
-2016-09-06 10:25:49,495 DEBUG: Done: Training
-2016-09-06 10:25:49,495 DEBUG: Start: Predicting
-2016-09-06 10:25:49,499 DEBUG: Done: Predicting
-2016-09-06 10:25:49,499 DEBUG: Start: Getting Results
-2016-09-06 10:25:49,500 DEBUG: Done: Getting Results
-2016-09-06 10:25:49,501 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,501 INFO: Done: Result Analysis
-2016-09-06 10:25:49,565 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,565 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,566 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:25:49,566 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:25:49,566 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:49,566 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:49,567 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,567 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,567 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,567 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,567 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:49,567 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:49,568 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,568 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,617 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:49,617 DEBUG: Start: Training
-2016-09-06 10:25:49,620 DEBUG: Info: Time for Training: 0.0563011169434[s]
-2016-09-06 10:25:49,620 DEBUG: Done: Training
-2016-09-06 10:25:49,620 DEBUG: Start: Predicting
-2016-09-06 10:25:49,623 DEBUG: Done: Predicting
-2016-09-06 10:25:49,623 DEBUG: Start: Getting Results
-2016-09-06 10:25:49,624 DEBUG: Done: Getting Results
-2016-09-06 10:25:49,624 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,625 INFO: Done: Result Analysis
-2016-09-06 10:25:49,632 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:49,632 DEBUG: Start: Training
-2016-09-06 10:25:49,636 DEBUG: Info: Time for Training: 0.072704076767[s]
-2016-09-06 10:25:49,636 DEBUG: Done: Training
-2016-09-06 10:25:49,637 DEBUG: Start: Predicting
-2016-09-06 10:25:49,639 DEBUG: Done: Predicting
-2016-09-06 10:25:49,639 DEBUG: Start: Getting Results
-2016-09-06 10:25:49,641 DEBUG: Done: Getting Results
-2016-09-06 10:25:49,641 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,642 INFO: Done: Result Analysis
-2016-09-06 10:25:49,706 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,706 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:49,706 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:25:49,706 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:25:49,706 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:49,706 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:49,707 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,707 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:49,707 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,707 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:49,707 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:49,707 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:49,707 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,707 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:49,745 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:49,745 DEBUG: Start: Training
-2016-09-06 10:25:49,745 DEBUG: Info: Time for Training: 0.0398440361023[s]
-2016-09-06 10:25:49,745 DEBUG: Done: Training
-2016-09-06 10:25:49,746 DEBUG: Start: Predicting
-2016-09-06 10:25:49,755 DEBUG: Done: Predicting
-2016-09-06 10:25:49,755 DEBUG: Start: Getting Results
-2016-09-06 10:25:49,756 DEBUG: Done: Getting Results
-2016-09-06 10:25:49,756 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:25:49,756 INFO: Done: Result Analysis
-2016-09-06 10:25:50,033 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:50,033 DEBUG: Start: Training
-2016-09-06 10:25:50,081 DEBUG: Info: Time for Training: 0.37574505806[s]
-2016-09-06 10:25:50,081 DEBUG: Done: Training
-2016-09-06 10:25:50,082 DEBUG: Start: Predicting
-2016-09-06 10:25:50,087 DEBUG: Done: Predicting
-2016-09-06 10:25:50,088 DEBUG: Start: Getting Results
-2016-09-06 10:25:50,089 DEBUG: Done: Getting Results
-2016-09-06 10:25:50,089 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 18, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,089 INFO: Done: Result Analysis
-2016-09-06 10:25:50,155 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,156 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:25:50,156 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:50,156 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,156 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:25:50,156 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:50,156 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,157 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,157 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,157 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,157 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:50,157 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:50,157 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,157 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,204 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:50,204 DEBUG: Start: Training
-2016-09-06 10:25:50,205 DEBUG: Info: Time for Training: 0.0503079891205[s]
-2016-09-06 10:25:50,205 DEBUG: Done: Training
-2016-09-06 10:25:50,205 DEBUG: Start: Predicting
-2016-09-06 10:25:50,216 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:50,216 DEBUG: Start: Training
-2016-09-06 10:25:50,232 DEBUG: Done: Predicting
-2016-09-06 10:25:50,232 DEBUG: Start: Getting Results
-2016-09-06 10:25:50,236 DEBUG: Done: Getting Results
-2016-09-06 10:25:50,236 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,236 INFO: Done: Result Analysis
-2016-09-06 10:25:50,245 DEBUG: Info: Time for Training: 0.0895159244537[s]
-2016-09-06 10:25:50,245 DEBUG: Done: Training
-2016-09-06 10:25:50,245 DEBUG: Start: Predicting
-2016-09-06 10:25:50,249 DEBUG: Done: Predicting
-2016-09-06 10:25:50,249 DEBUG: Start: Getting Results
-2016-09-06 10:25:50,250 DEBUG: Done: Getting Results
-2016-09-06 10:25:50,251 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.480952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.480952380952
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,251 INFO: Done: Result Analysis
-2016-09-06 10:25:50,403 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,403 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,403 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:25:50,403 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:50,403 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:25:50,403 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:50,404 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,404 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,404 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:50,404 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,404 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,404 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,404 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:50,405 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,454 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:50,454 DEBUG: Start: Training
-2016-09-06 10:25:50,466 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:50,466 DEBUG: Start: Training
-2016-09-06 10:25:50,472 DEBUG: Info: Time for Training: 0.0699520111084[s]
-2016-09-06 10:25:50,472 DEBUG: Done: Training
-2016-09-06 10:25:50,472 DEBUG: Start: Predicting
-2016-09-06 10:25:50,479 DEBUG: Done: Predicting
-2016-09-06 10:25:50,479 DEBUG: Start: Getting Results
-2016-09-06 10:25:50,480 DEBUG: Done: Getting Results
-2016-09-06 10:25:50,480 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,480 INFO: Done: Result Analysis
-2016-09-06 10:25:50,487 DEBUG: Info: Time for Training: 0.084676027298[s]
-2016-09-06 10:25:50,487 DEBUG: Done: Training
-2016-09-06 10:25:50,487 DEBUG: Start: Predicting
-2016-09-06 10:25:50,493 DEBUG: Done: Predicting
-2016-09-06 10:25:50,493 DEBUG: Start: Getting Results
-2016-09-06 10:25:50,494 DEBUG: Done: Getting Results
-2016-09-06 10:25:50,495 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,495 INFO: Done: Result Analysis
-2016-09-06 10:25:50,549 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,549 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:25:50,549 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:50,549 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,549 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:25:50,549 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:50,550 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,550 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,550 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,550 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:50,550 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,550 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,550 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:50,550 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,594 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:50,594 DEBUG: Start: Training
-2016-09-06 10:25:50,598 DEBUG: Info: Time for Training: 0.0494570732117[s]
-2016-09-06 10:25:50,598 DEBUG: Done: Training
-2016-09-06 10:25:50,598 DEBUG: Start: Predicting
-2016-09-06 10:25:50,601 DEBUG: Done: Predicting
-2016-09-06 10:25:50,601 DEBUG: Start: Getting Results
-2016-09-06 10:25:50,602 DEBUG: Done: Getting Results
-2016-09-06 10:25:50,603 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,603 INFO: Done: Result Analysis
-2016-09-06 10:25:50,604 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:50,605 DEBUG: Start: Training
-2016-09-06 10:25:50,610 DEBUG: Info: Time for Training: 0.0612750053406[s]
-2016-09-06 10:25:50,610 DEBUG: Done: Training
-2016-09-06 10:25:50,610 DEBUG: Start: Predicting
-2016-09-06 10:25:50,613 DEBUG: Done: Predicting
-2016-09-06 10:25:50,613 DEBUG: Start: Getting Results
-2016-09-06 10:25:50,615 DEBUG: Done: Getting Results
-2016-09-06 10:25:50,615 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,615 INFO: Done: Result Analysis
-2016-09-06 10:25:50,695 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,695 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:50,695 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:25:50,695 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:25:50,696 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:50,696 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:50,696 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,696 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:50,696 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,696 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:50,696 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:50,697 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,697 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:50,697 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:50,731 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:50,732 DEBUG: Start: Training
-2016-09-06 10:25:50,732 DEBUG: Info: Time for Training: 0.0376601219177[s]
-2016-09-06 10:25:50,732 DEBUG: Done: Training
-2016-09-06 10:25:50,732 DEBUG: Start: Predicting
-2016-09-06 10:25:50,740 DEBUG: Done: Predicting
-2016-09-06 10:25:50,741 DEBUG: Start: Getting Results
-2016-09-06 10:25:50,742 DEBUG: Done: Getting Results
-2016-09-06 10:25:50,742 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:25:50,742 INFO: Done: Result Analysis
-2016-09-06 10:25:51,022 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:51,022 DEBUG: Start: Training
-2016-09-06 10:25:51,071 DEBUG: Info: Time for Training: 0.37623500824[s]
-2016-09-06 10:25:51,071 DEBUG: Done: Training
-2016-09-06 10:25:51,071 DEBUG: Start: Predicting
-2016-09-06 10:25:51,077 DEBUG: Done: Predicting
-2016-09-06 10:25:51,077 DEBUG: Start: Getting Results
-2016-09-06 10:25:51,079 DEBUG: Done: Getting Results
-2016-09-06 10:25:51,079 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 18, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,079 INFO: Done: Result Analysis
-2016-09-06 10:25:51,152 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,152 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,153 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:25:51,153 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:25:51,153 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:51,153 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:51,154 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:51,154 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:51,154 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:51,154 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:51,154 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:51,154 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:51,154 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,154 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,224 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:51,225 DEBUG: Start: Training
-2016-09-06 10:25:51,226 DEBUG: Info: Time for Training: 0.0744400024414[s]
-2016-09-06 10:25:51,226 DEBUG: Done: Training
-2016-09-06 10:25:51,226 DEBUG: Start: Predicting
-2016-09-06 10:25:51,232 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:51,232 DEBUG: Start: Training
-2016-09-06 10:25:51,243 DEBUG: Done: Predicting
-2016-09-06 10:25:51,244 DEBUG: Start: Getting Results
-2016-09-06 10:25:51,246 DEBUG: Done: Getting Results
-2016-09-06 10:25:51,247 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,247 INFO: Done: Result Analysis
-2016-09-06 10:25:51,262 DEBUG: Info: Time for Training: 0.110822916031[s]
-2016-09-06 10:25:51,263 DEBUG: Done: Training
-2016-09-06 10:25:51,263 DEBUG: Start: Predicting
-2016-09-06 10:25:51,266 DEBUG: Done: Predicting
-2016-09-06 10:25:51,267 DEBUG: Start: Getting Results
-2016-09-06 10:25:51,268 DEBUG: Done: Getting Results
-2016-09-06 10:25:51,268 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,268 INFO: Done: Result Analysis
-2016-09-06 10:25:51,400 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,400 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,401 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:25:51,401 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:25:51,401 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:51,401 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:51,402 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:51,402 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:25:51,402 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:51,403 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:25:51,403 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:51,403 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:51,403 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,403 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,474 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:51,474 DEBUG: Start: Training
-2016-09-06 10:25:51,487 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:51,487 DEBUG: Start: Training
-2016-09-06 10:25:51,500 DEBUG: Info: Time for Training: 0.101441144943[s]
-2016-09-06 10:25:51,501 DEBUG: Done: Training
-2016-09-06 10:25:51,501 DEBUG: Start: Predicting
-2016-09-06 10:25:51,509 DEBUG: Done: Predicting
-2016-09-06 10:25:51,510 DEBUG: Start: Getting Results
-2016-09-06 10:25:51,511 DEBUG: Done: Getting Results
-2016-09-06 10:25:51,512 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,512 INFO: Done: Result Analysis
-2016-09-06 10:25:51,513 DEBUG: Info: Time for Training: 0.114230155945[s]
-2016-09-06 10:25:51,513 DEBUG: Done: Training
-2016-09-06 10:25:51,513 DEBUG: Start: Predicting
-2016-09-06 10:25:51,518 DEBUG: Done: Predicting
-2016-09-06 10:25:51,518 DEBUG: Start: Getting Results
-2016-09-06 10:25:51,519 DEBUG: Done: Getting Results
-2016-09-06 10:25:51,519 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,519 INFO: Done: Result Analysis
-2016-09-06 10:25:51,649 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,649 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,649 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:25:51,649 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:25:51,650 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:51,650 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:51,650 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:51,650 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:51,650 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:51,650 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:51,650 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:51,651 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,651 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:51,651 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,688 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:51,688 DEBUG: Start: Training
-2016-09-06 10:25:51,690 DEBUG: Info: Time for Training: 0.0416340827942[s]
-2016-09-06 10:25:51,690 DEBUG: Done: Training
-2016-09-06 10:25:51,690 DEBUG: Start: Predicting
-2016-09-06 10:25:51,693 DEBUG: Done: Predicting
-2016-09-06 10:25:51,693 DEBUG: Start: Getting Results
-2016-09-06 10:25:51,694 DEBUG: Done: Getting Results
-2016-09-06 10:25:51,694 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.914285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.914285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,695 INFO: Done: Result Analysis
-2016-09-06 10:25:51,703 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:51,703 DEBUG: Start: Training
-2016-09-06 10:25:51,708 DEBUG: Info: Time for Training: 0.0598511695862[s]
-2016-09-06 10:25:51,708 DEBUG: Done: Training
-2016-09-06 10:25:51,709 DEBUG: Start: Predicting
-2016-09-06 10:25:51,712 DEBUG: Done: Predicting
-2016-09-06 10:25:51,712 DEBUG: Start: Getting Results
-2016-09-06 10:25:51,713 DEBUG: Done: Getting Results
-2016-09-06 10:25:51,714 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,714 INFO: Done: Result Analysis
-2016-09-06 10:25:51,800 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,800 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:51,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:25:51,801 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:25:51,801 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:51,801 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:51,802 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:51,802 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:51,802 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:51,802 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:51,802 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:51,802 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:51,802 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,802 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:51,853 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:51,853 DEBUG: Start: Training
-2016-09-06 10:25:51,854 DEBUG: Info: Time for Training: 0.0547788143158[s]
-2016-09-06 10:25:51,855 DEBUG: Done: Training
-2016-09-06 10:25:51,855 DEBUG: Start: Predicting
-2016-09-06 10:25:51,866 DEBUG: Done: Predicting
-2016-09-06 10:25:51,866 DEBUG: Start: Getting Results
-2016-09-06 10:25:51,868 DEBUG: Done: Getting Results
-2016-09-06 10:25:51,868 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:25:51,868 INFO: Done: Result Analysis
-2016-09-06 10:25:52,153 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:52,153 DEBUG: Start: Training
-2016-09-06 10:25:52,201 DEBUG: Info: Time for Training: 0.401795864105[s]
-2016-09-06 10:25:52,202 DEBUG: Done: Training
-2016-09-06 10:25:52,202 DEBUG: Start: Predicting
-2016-09-06 10:25:52,208 DEBUG: Done: Predicting
-2016-09-06 10:25:52,208 DEBUG: Start: Getting Results
-2016-09-06 10:25:52,209 DEBUG: Done: Getting Results
-2016-09-06 10:25:52,209 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 18, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:25:52,209 INFO: Done: Result Analysis
-2016-09-06 10:25:52,349 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:52,350 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:25:52,350 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:25:52,350 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:25:52,350 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:52,350 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:25:52,351 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:52,351 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:25:52,351 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:52,351 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:25:52,351 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:52,351 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:25:52,351 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:52,351 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:25:52,399 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:52,399 DEBUG: Start: Training
-2016-09-06 10:25:52,400 DEBUG: Info: Time for Training: 0.0507619380951[s]
-2016-09-06 10:25:52,400 DEBUG: Done: Training
-2016-09-06 10:25:52,400 DEBUG: Start: Predicting
-2016-09-06 10:25:52,407 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:25:52,407 DEBUG: Start: Training
-2016-09-06 10:25:52,425 DEBUG: Done: Predicting
-2016-09-06 10:25:52,426 DEBUG: Start: Getting Results
-2016-09-06 10:25:52,427 DEBUG: Done: Getting Results
-2016-09-06 10:25:52,427 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.644444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.644444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:25:52,428 INFO: Done: Result Analysis
-2016-09-06 10:25:52,431 DEBUG: Info: Time for Training: 0.0818819999695[s]
-2016-09-06 10:25:52,431 DEBUG: Done: Training
-2016-09-06 10:25:52,431 DEBUG: Start: Predicting
-2016-09-06 10:25:52,435 DEBUG: Done: Predicting
-2016-09-06 10:25:52,435 DEBUG: Start: Getting Results
-2016-09-06 10:25:52,436 DEBUG: Done: Getting Results
-2016-09-06 10:25:52,436 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.452380952381
-accuracy_score on test : 0.388888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.452380952381
- - Score on test : 0.388888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:25:52,436 INFO: Done: Result Analysis
-2016-09-06 10:25:52,647 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:25:52,647 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:25:52,648 INFO: Info: Shape of View0 :(300, 20)
-2016-09-06 10:25:52,649 INFO: Info: Shape of View1 :(300, 20)
-2016-09-06 10:25:52,649 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:25:52,649 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:25:52,649 INFO: Info: Shape of View2 :(300, 20)
-2016-09-06 10:25:52,650 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 10:25:52,650 INFO: Info: Shape of View0 :(300, 20)
-2016-09-06 10:25:52,650 INFO: Done: Read Database Files
-2016-09-06 10:25:52,651 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:25:52,652 INFO: Info: Shape of View1 :(300, 20)
-2016-09-06 10:25:52,653 INFO: Info: Shape of View2 :(300, 20)
-2016-09-06 10:25:52,654 INFO: Info: Shape of View3 :(300, 18)
-2016-09-06 10:25:52,654 INFO: Done: Read Database Files
-2016-09-06 10:25:52,654 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:25:52,656 INFO: Done: Determine validation split
-2016-09-06 10:25:52,656 INFO: Start: Determine 5 folds
-2016-09-06 10:25:52,658 INFO: Done: Determine validation split
-2016-09-06 10:25:52,658 INFO: Start: Determine 5 folds
-2016-09-06 10:25:52,665 INFO: Info: Length of Learning Sets: 168
-2016-09-06 10:25:52,665 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:25:52,666 INFO: Info: Length of Validation Set: 90
-2016-09-06 10:25:52,666 INFO: Done: Determine folds
-2016-09-06 10:25:52,666 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:25:52,666 INFO: Start: Classification
-2016-09-06 10:25:52,666 INFO: Start: Fold number 1
-2016-09-06 10:25:52,669 INFO: Info: Length of Learning Sets: 168
-2016-09-06 10:25:52,669 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:25:52,670 INFO: Info: Length of Validation Set: 90
-2016-09-06 10:25:52,670 INFO: Done: Determine folds
-2016-09-06 10:25:52,670 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:25:52,670 INFO: Start: Classification
-2016-09-06 10:25:52,670 INFO: Start: Fold number 1
-2016-09-06 10:25:52,699 DEBUG: Start: Iteration 1
-2016-09-06 10:25:52,708 DEBUG: View 0 : 0.434523809524
-2016-09-06 10:25:52,716 DEBUG: View 1 : 0.470238095238
-2016-09-06 10:25:52,724 DEBUG: View 2 : 0.505952380952
-2016-09-06 10:25:52,731 DEBUG: View 3 : 0.553571428571
-2016-09-06 10:25:52,763 DEBUG: Best view : View0
-2016-09-06 10:25:52,841 DEBUG: Start: Iteration 2
-2016-09-06 10:25:52,849 DEBUG: View 0 : 0.720238095238
-2016-09-06 10:25:52,857 DEBUG: View 1 : 0.666666666667
-2016-09-06 10:25:52,865 DEBUG: View 2 : 0.660714285714
-2016-09-06 10:25:52,872 DEBUG: View 3 : 0.744047619048
-2016-09-06 10:25:52,909 DEBUG: Best view : View3
-2016-09-06 10:25:53,053 DEBUG: Start: Iteration 3
-2016-09-06 10:25:53,071 DEBUG: View 0 : 0.720238095238
-2016-09-06 10:25:53,080 DEBUG: View 1 : 0.666666666667
-2016-09-06 10:25:53,088 DEBUG: View 2 : 0.660714285714
-2016-09-06 10:25:53,095 DEBUG: View 3 : 0.744047619048
-2016-09-06 10:25:53,134 DEBUG: Best view : View3
-2016-09-06 10:25:53,338 DEBUG: Start: Iteration 4
-2016-09-06 10:25:53,345 DEBUG: View 0 : 0.77380952381
-2016-09-06 10:25:53,353 DEBUG: View 1 : 0.732142857143
-2016-09-06 10:25:53,360 DEBUG: View 2 : 0.678571428571
-2016-09-06 10:25:53,369 DEBUG: View 3 : 0.690476190476
-2016-09-06 10:25:53,419 DEBUG: Best view : View0
-2016-09-06 10:25:53,697 INFO: Start: Classification
-2016-09-06 10:25:54,148 INFO: Done: Fold number 1
-2016-09-06 10:25:54,148 INFO: Start: Fold number 2
-2016-09-06 10:25:54,177 DEBUG: Start: Iteration 1
-2016-09-06 10:25:54,184 DEBUG: View 0 : 0.5
-2016-09-06 10:25:54,190 DEBUG: View 1 : 0.5
-2016-09-06 10:25:54,197 DEBUG: View 2 : 0.5
-2016-09-06 10:25:54,204 DEBUG: View 3 : 0.5
-2016-09-06 10:25:54,234 DEBUG: Best view : View0
-2016-09-06 10:25:54,309 DEBUG: Start: Iteration 2
-2016-09-06 10:25:54,317 DEBUG: View 0 : 0.72619047619
-2016-09-06 10:25:54,325 DEBUG: View 1 : 0.761904761905
-2016-09-06 10:25:54,333 DEBUG: View 2 : 0.666666666667
-2016-09-06 10:25:54,341 DEBUG: View 3 : 0.732142857143
-2016-09-06 10:25:54,377 DEBUG: Best view : View1
-2016-09-06 10:25:54,515 DEBUG: Start: Iteration 3
-2016-09-06 10:25:54,523 DEBUG: View 0 : 0.72619047619
-2016-09-06 10:25:54,531 DEBUG: View 1 : 0.761904761905
-2016-09-06 10:25:54,539 DEBUG: View 2 : 0.666666666667
-2016-09-06 10:25:54,546 DEBUG: View 3 : 0.732142857143
-2016-09-06 10:25:54,584 DEBUG: Best view : View1
-2016-09-06 10:25:54,786 DEBUG: Start: Iteration 4
-2016-09-06 10:25:54,794 DEBUG: View 0 : 0.678571428571
-2016-09-06 10:25:54,802 DEBUG: View 1 : 0.744047619048
-2016-09-06 10:25:54,809 DEBUG: View 2 : 0.738095238095
-2016-09-06 10:25:54,817 DEBUG: View 3 : 0.708333333333
-2016-09-06 10:25:54,857 DEBUG: Best view : View1
-2016-09-06 10:25:55,121 INFO: Start: Classification
-2016-09-06 10:25:55,575 INFO: Done: Fold number 2
-2016-09-06 10:25:55,575 INFO: Start: Fold number 3
-2016-09-06 10:25:55,604 DEBUG: Start: Iteration 1
-2016-09-06 10:25:55,611 DEBUG: View 0 : 0.577380952381
-2016-09-06 10:25:55,619 DEBUG: View 1 : 0.52380952381
-2016-09-06 10:25:55,626 DEBUG: View 2 : 0.47619047619
-2016-09-06 10:25:55,633 DEBUG: View 3 : 0.541666666667
-2016-09-06 10:25:55,664 DEBUG: Best view : View0
-2016-09-06 10:25:55,738 DEBUG: Start: Iteration 2
-2016-09-06 10:25:55,746 DEBUG: View 0 : 0.72619047619
-2016-09-06 10:25:55,754 DEBUG: View 1 : 0.77380952381
-2016-09-06 10:25:55,762 DEBUG: View 2 : 0.714285714286
-2016-09-06 10:25:55,769 DEBUG: View 3 : 0.767857142857
-2016-09-06 10:25:55,805 DEBUG: Best view : View1
-2016-09-06 10:25:55,944 DEBUG: Start: Iteration 3
-2016-09-06 10:25:55,952 DEBUG: View 0 : 0.72619047619
-2016-09-06 10:25:55,960 DEBUG: View 1 : 0.77380952381
-2016-09-06 10:25:55,967 DEBUG: View 2 : 0.714285714286
-2016-09-06 10:25:55,975 DEBUG: View 3 : 0.767857142857
-2016-09-06 10:25:56,013 DEBUG: Best view : View1
-2016-09-06 10:25:56,215 DEBUG: Start: Iteration 4
-2016-09-06 10:25:56,223 DEBUG: View 0 : 0.72619047619
-2016-09-06 10:25:56,231 DEBUG: View 1 : 0.672619047619
-2016-09-06 10:25:56,239 DEBUG: View 2 : 0.738095238095
-2016-09-06 10:25:56,246 DEBUG: View 3 : 0.702380952381
-2016-09-06 10:25:56,287 DEBUG: Best view : View2
-2016-09-06 10:25:56,554 INFO: Start: Classification
-2016-09-06 10:25:57,010 INFO: Done: Fold number 3
-2016-09-06 10:25:57,010 INFO: Start: Fold number 4
-2016-09-06 10:25:57,038 DEBUG: Start: Iteration 1
-2016-09-06 10:25:57,045 DEBUG: View 0 : 0.535714285714
-2016-09-06 10:25:57,053 DEBUG: View 1 : 0.5
-2016-09-06 10:25:57,060 DEBUG: View 2 : 0.440476190476
-2016-09-06 10:25:57,067 DEBUG: View 3 : 0.47619047619
-2016-09-06 10:25:57,097 DEBUG: Best view : View3
-2016-09-06 10:25:57,171 DEBUG: Start: Iteration 2
-2016-09-06 10:25:57,178 DEBUG: View 0 : 0.779761904762
-2016-09-06 10:25:57,186 DEBUG: View 1 : 0.744047619048
-2016-09-06 10:25:57,193 DEBUG: View 2 : 0.702380952381
-2016-09-06 10:25:57,201 DEBUG: View 3 : 0.714285714286
-2016-09-06 10:25:57,237 DEBUG: Best view : View0
-2016-09-06 10:25:57,376 DEBUG: Start: Iteration 3
-2016-09-06 10:25:57,383 DEBUG: View 0 : 0.779761904762
-2016-09-06 10:25:57,391 DEBUG: View 1 : 0.744047619048
-2016-09-06 10:25:57,399 DEBUG: View 2 : 0.702380952381
-2016-09-06 10:25:57,406 DEBUG: View 3 : 0.714285714286
-2016-09-06 10:25:57,445 DEBUG: Best view : View0
-2016-09-06 10:25:57,648 DEBUG: Start: Iteration 4
-2016-09-06 10:25:57,656 DEBUG: View 0 : 0.72619047619
-2016-09-06 10:25:57,664 DEBUG: View 1 : 0.672619047619
-2016-09-06 10:25:57,671 DEBUG: View 2 : 0.690476190476
-2016-09-06 10:25:57,678 DEBUG: View 3 : 0.732142857143
-2016-09-06 10:25:57,719 DEBUG: Best view : View3
-2016-09-06 10:25:57,985 INFO: Start: Classification
-2016-09-06 10:25:58,436 INFO: Done: Fold number 4
-2016-09-06 10:25:58,436 INFO: Start: Fold number 5
-2016-09-06 10:25:58,464 DEBUG: Start: Iteration 1
-2016-09-06 10:25:58,471 DEBUG: View 0 : 0.5
-2016-09-06 10:25:58,477 DEBUG: View 1 : 0.5
-2016-09-06 10:25:58,484 DEBUG: View 2 : 0.5
-2016-09-06 10:25:58,491 DEBUG: View 3 : 0.5
-2016-09-06 10:25:58,520 DEBUG: Best view : View0
-2016-09-06 10:25:58,595 DEBUG: Start: Iteration 2
-2016-09-06 10:25:58,602 DEBUG: View 0 : 0.75
-2016-09-06 10:25:58,610 DEBUG: View 1 : 0.75
-2016-09-06 10:25:58,618 DEBUG: View 2 : 0.714285714286
-2016-09-06 10:25:58,625 DEBUG: View 3 : 0.738095238095
-2016-09-06 10:25:58,661 DEBUG: Best view : View0
-2016-09-06 10:25:58,798 DEBUG: Start: Iteration 3
-2016-09-06 10:25:58,806 DEBUG: View 0 : 0.75
-2016-09-06 10:25:58,813 DEBUG: View 1 : 0.75
-2016-09-06 10:25:58,821 DEBUG: View 2 : 0.714285714286
-2016-09-06 10:25:58,829 DEBUG: View 3 : 0.738095238095
-2016-09-06 10:25:58,867 DEBUG: Best view : View0
-2016-09-06 10:25:59,068 DEBUG: Start: Iteration 4
-2016-09-06 10:25:59,075 DEBUG: View 0 : 0.720238095238
-2016-09-06 10:25:59,083 DEBUG: View 1 : 0.642857142857
-2016-09-06 10:25:59,091 DEBUG: View 2 : 0.660714285714
-2016-09-06 10:25:59,098 DEBUG: View 3 : 0.678571428571
-2016-09-06 10:25:59,138 DEBUG: Best view : View0
-2016-09-06 10:25:59,405 INFO: Start: Classification
-2016-09-06 10:25:59,857 INFO: Done: Fold number 5
-2016-09-06 10:25:59,857 INFO: Done: Classification
-2016-09-06 10:25:59,857 INFO: Info: Time for Classification: 7[s]
-2016-09-06 10:25:59,857 INFO: Start: Result Analysis for Mumbo
-2016-09-06 10:26:02,299 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 76.1904761905
- -On Test : 50.9523809524
- -On Validation : 51.3333333333Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 20), View1 of shape (300, 20), View2 of shape (300, 20), View3 of shape (300, 18)
- -5 folds
- - Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:06 0:00:00
- Total 0:00:19 0:00:02
- So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.264880952381
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.253571428571
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.250595238095
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.273214285714
- - Percentage of time chosen : 0.2
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.263095238095
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.276785714286
- - Percentage of time chosen : 0.3
- - On View2 :
- - Mean average Accuracy : 0.257142857143
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.267261904762
- - Percentage of time chosen : 0.0
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.275595238095
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.274404761905
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.264285714286
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.277976190476
- - Percentage of time chosen : 0.0
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.282142857143
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.266071428571
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.253571428571
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.26369047619
- - Percentage of time chosen : 0.2
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.272023809524
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.264285714286
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.258928571429
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.265476190476
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 3
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View3
- Fold 5
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 74.4047619048
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View3
- Fold 2
- Accuracy on train : 76.1904761905
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View1
- Fold 3
- Accuracy on train : 77.380952381
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View1
- Fold 4
- Accuracy on train : 77.9761904762
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View0
- Fold 5
- Accuracy on train : 75.0
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View0
- - Iteration 3
- Fold 1
- Accuracy on train : 74.4047619048
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View3
- Fold 2
- Accuracy on train : 76.1904761905
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View1
- Fold 3
- Accuracy on train : 77.380952381
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View1
- Fold 4
- Accuracy on train : 77.9761904762
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View0
- Fold 5
- Accuracy on train : 75.0
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View0
- - Iteration 4
- Fold 1
- Accuracy on train : 74.4047619048
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 76.1904761905
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View1
- Fold 3
- Accuracy on train : 77.380952381
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View2
- Fold 4
- Accuracy on train : 77.9761904762
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View3
- Fold 5
- Accuracy on train : 75.0
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View0
- - Iteration 5
- Fold 1
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 3
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 5
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
-2016-09-06 10:26:02,608 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d211664d715e653f105e4cb7348645cbee180914..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d51b4eaf83f6fcd9f130c294e0468833ed4a5a30..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e3ecbf951561881a8374881ac8bc0fe3a642d4ed..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102548Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4b027c7737631df018275a3fa863d4b0c5d4f4a1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a7e7788a97badf1e4004631a77488335330e38ad..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 30353e793e5d8fda38d37a59b6d47062fa33d2bd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b1fdee3f802ec1e9b294a3063836dcdded981bdc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 18, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d845e8ffe5475d6824fa5fb1eb0715b5419d135c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7979763ec2fe478fd1f9ac624b8b1e7c859e43fe..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0d73c12c86fbd5a461212442740b218cfa2865cc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e7530b2030eb1b89de274a1e738a79311c21a64b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102549Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 81b99c2e1a21bae2f05c93cfaaf197b41ed1f81a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e1cc8a5dea9e439bd0dd2d2126906ab6b3301da2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 28d885b8a2594e3239a77e5e2f9c6d69fb6ad797..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e4c62d656eaffd32dfaf75c88479c53853bf48ef..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 18, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 524dffb8908cbb276a7bf45ce4e62809630f93c9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d606bc6922cebc9421c17329044fdbc329fa896a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.480952380952
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.480952380952
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 453b1235bd05275bd81329f3b067ab1a19fbcbc1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7c633b5cdb2f55d96155f1c9644cf98c72fb197d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102550Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 672961f84dae6107ded9cb484468ad5e0b44d685..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0eb148d0f94635a4eb45860c0dc29b277b382a35..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.914285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.914285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bb5e97e21ed3fc4818f8d322ea1ddfc1c8c8fdd9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 41
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c393d7069fb9490036b11dac6e2a9a93c422a45d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 18, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 871e4290219af97a41fc070e7ea4627668709946..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a6d7a51981416562a40a5d96442d9dce564cf7c6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f7ae9cc62f78613dcc5d334c654ac33efab193e3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1a3bc86eb86d31f9a6159e631eae547a64a50248..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102551Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e56487859a03db2850790b6dc6fa129cf8c9b808..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 18, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a809878b1228936053994b3d07334593ea238e29..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.644444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.644444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 805ad6deb8240290a77fc5dbec24f4b5ff19d9d8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102552Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.452380952381
-accuracy_score on test : 0.388888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5608
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.452380952381
- - Score on test : 0.388888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index 26f1647524b9834de293998d66cd7531623da06f..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 0873f3f6aef8fd84300dab66628803e450ab4c47..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-102602Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,235 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 76.1904761905
- -On Test : 50.9523809524
- -On Validation : 51.3333333333Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 20), View1 of shape (300, 20), View2 of shape (300, 20), View3 of shape (300, 18)
- -5 folds
- - Validation set length : 90 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:06 0:00:00
- Total 0:00:19 0:00:02
- So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.264880952381
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.253571428571
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.250595238095
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.273214285714
- - Percentage of time chosen : 0.2
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.263095238095
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.276785714286
- - Percentage of time chosen : 0.3
- - On View2 :
- - Mean average Accuracy : 0.257142857143
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.267261904762
- - Percentage of time chosen : 0.0
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.275595238095
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.274404761905
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.264285714286
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.277976190476
- - Percentage of time chosen : 0.0
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.282142857143
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.266071428571
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.253571428571
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.26369047619
- - Percentage of time chosen : 0.2
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.272023809524
- - Percentage of time chosen : 1.0
- - On View1 :
- - Mean average Accuracy : 0.264285714286
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.258928571429
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.265476190476
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 3
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View3
- Fold 5
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 74.4047619048
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View3
- Fold 2
- Accuracy on train : 76.1904761905
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View1
- Fold 3
- Accuracy on train : 77.380952381
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View1
- Fold 4
- Accuracy on train : 77.9761904762
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View0
- Fold 5
- Accuracy on train : 75.0
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View0
- - Iteration 3
- Fold 1
- Accuracy on train : 74.4047619048
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View3
- Fold 2
- Accuracy on train : 76.1904761905
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View1
- Fold 3
- Accuracy on train : 77.380952381
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View1
- Fold 4
- Accuracy on train : 77.9761904762
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View0
- Fold 5
- Accuracy on train : 75.0
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View0
- - Iteration 4
- Fold 1
- Accuracy on train : 74.4047619048
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 76.1904761905
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View1
- Fold 3
- Accuracy on train : 77.380952381
- Accuracy on test : 0.0
- Accuracy on validation : 51.1111111111
- Selected View : View2
- Fold 4
- Accuracy on train : 77.9761904762
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View3
- Fold 5
- Accuracy on train : 75.0
- Accuracy on test : 0.0
- Accuracy on validation : 52.2222222222
- Selected View : View0
- - Iteration 5
- Fold 1
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 2
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 3
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 4
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
- Fold 5
- Accuracy on train : 50.0
- Accuracy on test : 0.0
- Accuracy on validation : 50.0
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index edd1146e89d61f806df070c36677845a7acca855..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1624 +0,0 @@
-2016-09-06 10:31:15,988 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:31:15,989 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00010290625 Gbytes /!\
-2016-09-06 10:31:20,994 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:31:20,996 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:31:21,043 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,043 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,044 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:31:21,044 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:31:21,044 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:21,044 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:21,045 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,045 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,045 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,045 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,045 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:21,045 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:21,045 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,045 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,080 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:21,080 DEBUG: Start: Training
-2016-09-06 10:31:21,082 DEBUG: Info: Time for Training: 0.0389211177826[s]
-2016-09-06 10:31:21,082 DEBUG: Done: Training
-2016-09-06 10:31:21,082 DEBUG: Start: Predicting
-2016-09-06 10:31:21,085 DEBUG: Done: Predicting
-2016-09-06 10:31:21,085 DEBUG: Start: Getting Results
-2016-09-06 10:31:21,086 DEBUG: Done: Getting Results
-2016-09-06 10:31:21,086 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.938095238095
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.938095238095
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,087 INFO: Done: Result Analysis
-2016-09-06 10:31:21,095 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:21,095 DEBUG: Start: Training
-2016-09-06 10:31:21,098 DEBUG: Info: Time for Training: 0.0554401874542[s]
-2016-09-06 10:31:21,098 DEBUG: Done: Training
-2016-09-06 10:31:21,099 DEBUG: Start: Predicting
-2016-09-06 10:31:21,101 DEBUG: Done: Predicting
-2016-09-06 10:31:21,101 DEBUG: Start: Getting Results
-2016-09-06 10:31:21,103 DEBUG: Done: Getting Results
-2016-09-06 10:31:21,103 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,104 INFO: Done: Result Analysis
-2016-09-06 10:31:21,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:31:21,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:31:21,197 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:21,197 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:21,197 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,197 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,198 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,198 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,198 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:21,198 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:21,198 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,198 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,230 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:21,230 DEBUG: Start: Training
-2016-09-06 10:31:21,231 DEBUG: Info: Time for Training: 0.0347671508789[s]
-2016-09-06 10:31:21,231 DEBUG: Done: Training
-2016-09-06 10:31:21,231 DEBUG: Start: Predicting
-2016-09-06 10:31:21,238 DEBUG: Done: Predicting
-2016-09-06 10:31:21,238 DEBUG: Start: Getting Results
-2016-09-06 10:31:21,239 DEBUG: Done: Getting Results
-2016-09-06 10:31:21,239 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,240 INFO: Done: Result Analysis
-2016-09-06 10:31:21,497 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:21,497 DEBUG: Start: Training
-2016-09-06 10:31:21,540 DEBUG: Info: Time for Training: 0.343922138214[s]
-2016-09-06 10:31:21,540 DEBUG: Done: Training
-2016-09-06 10:31:21,540 DEBUG: Start: Predicting
-2016-09-06 10:31:21,546 DEBUG: Done: Predicting
-2016-09-06 10:31:21,546 DEBUG: Start: Getting Results
-2016-09-06 10:31:21,548 DEBUG: Done: Getting Results
-2016-09-06 10:31:21,548 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 17, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,548 INFO: Done: Result Analysis
-2016-09-06 10:31:21,646 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,646 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,646 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:31:21,646 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:31:21,646 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:21,647 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:21,647 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,647 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,647 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,647 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,647 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:21,647 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:21,647 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,647 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,692 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:21,692 DEBUG: Start: Training
-2016-09-06 10:31:21,693 DEBUG: Info: Time for Training: 0.0476639270782[s]
-2016-09-06 10:31:21,693 DEBUG: Done: Training
-2016-09-06 10:31:21,693 DEBUG: Start: Predicting
-2016-09-06 10:31:21,698 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:21,698 DEBUG: Start: Training
-2016-09-06 10:31:21,710 DEBUG: Done: Predicting
-2016-09-06 10:31:21,710 DEBUG: Start: Getting Results
-2016-09-06 10:31:21,713 DEBUG: Done: Getting Results
-2016-09-06 10:31:21,713 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,713 INFO: Done: Result Analysis
-2016-09-06 10:31:21,721 DEBUG: Info: Time for Training: 0.0758979320526[s]
-2016-09-06 10:31:21,722 DEBUG: Done: Training
-2016-09-06 10:31:21,722 DEBUG: Start: Predicting
-2016-09-06 10:31:21,725 DEBUG: Done: Predicting
-2016-09-06 10:31:21,725 DEBUG: Start: Getting Results
-2016-09-06 10:31:21,726 DEBUG: Done: Getting Results
-2016-09-06 10:31:21,726 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,727 INFO: Done: Result Analysis
-2016-09-06 10:31:21,797 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,797 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:21,798 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:31:21,798 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:31:21,798 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:21,798 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:21,799 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,799 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:31:21,799 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,799 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:31:21,799 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:21,799 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:21,799 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,799 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:21,869 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:21,869 DEBUG: Start: Training
-2016-09-06 10:31:21,877 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:21,877 DEBUG: Start: Training
-2016-09-06 10:31:21,892 DEBUG: Info: Time for Training: 0.0960838794708[s]
-2016-09-06 10:31:21,893 DEBUG: Done: Training
-2016-09-06 10:31:21,893 DEBUG: Start: Predicting
-2016-09-06 10:31:21,899 DEBUG: Done: Predicting
-2016-09-06 10:31:21,900 DEBUG: Start: Getting Results
-2016-09-06 10:31:21,900 DEBUG: Info: Time for Training: 0.103469848633[s]
-2016-09-06 10:31:21,900 DEBUG: Done: Training
-2016-09-06 10:31:21,900 DEBUG: Start: Predicting
-2016-09-06 10:31:21,901 DEBUG: Done: Getting Results
-2016-09-06 10:31:21,901 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,901 INFO: Done: Result Analysis
-2016-09-06 10:31:21,903 DEBUG: Done: Predicting
-2016-09-06 10:31:21,903 DEBUG: Start: Getting Results
-2016-09-06 10:31:21,905 DEBUG: Done: Getting Results
-2016-09-06 10:31:21,905 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:31:21,905 INFO: Done: Result Analysis
-2016-09-06 10:31:22,048 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,048 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,049 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:31:22,049 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:31:22,049 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:22,049 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:22,051 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,051 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,051 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,051 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,051 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:22,051 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:22,051 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,051 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,102 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:22,102 DEBUG: Start: Training
-2016-09-06 10:31:22,104 DEBUG: Info: Time for Training: 0.0569620132446[s]
-2016-09-06 10:31:22,105 DEBUG: Done: Training
-2016-09-06 10:31:22,105 DEBUG: Start: Predicting
-2016-09-06 10:31:22,108 DEBUG: Done: Predicting
-2016-09-06 10:31:22,109 DEBUG: Start: Getting Results
-2016-09-06 10:31:22,111 DEBUG: Done: Getting Results
-2016-09-06 10:31:22,111 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.838095238095
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.838095238095
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,111 INFO: Done: Result Analysis
-2016-09-06 10:31:22,121 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:22,121 DEBUG: Start: Training
-2016-09-06 10:31:22,125 DEBUG: Info: Time for Training: 0.0774049758911[s]
-2016-09-06 10:31:22,125 DEBUG: Done: Training
-2016-09-06 10:31:22,125 DEBUG: Start: Predicting
-2016-09-06 10:31:22,128 DEBUG: Done: Predicting
-2016-09-06 10:31:22,128 DEBUG: Start: Getting Results
-2016-09-06 10:31:22,130 DEBUG: Done: Getting Results
-2016-09-06 10:31:22,130 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,130 INFO: Done: Result Analysis
-2016-09-06 10:31:22,194 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,194 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,194 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:31:22,194 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:31:22,194 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:22,194 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:22,195 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,195 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,195 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,195 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,196 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:22,196 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:22,196 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,196 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,248 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:22,248 DEBUG: Start: Training
-2016-09-06 10:31:22,248 DEBUG: Info: Time for Training: 0.0551888942719[s]
-2016-09-06 10:31:22,248 DEBUG: Done: Training
-2016-09-06 10:31:22,249 DEBUG: Start: Predicting
-2016-09-06 10:31:22,255 DEBUG: Done: Predicting
-2016-09-06 10:31:22,256 DEBUG: Start: Getting Results
-2016-09-06 10:31:22,257 DEBUG: Done: Getting Results
-2016-09-06 10:31:22,257 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,257 INFO: Done: Result Analysis
-2016-09-06 10:31:22,555 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:22,555 DEBUG: Start: Training
-2016-09-06 10:31:22,602 DEBUG: Info: Time for Training: 0.409327983856[s]
-2016-09-06 10:31:22,603 DEBUG: Done: Training
-2016-09-06 10:31:22,603 DEBUG: Start: Predicting
-2016-09-06 10:31:22,609 DEBUG: Done: Predicting
-2016-09-06 10:31:22,609 DEBUG: Start: Getting Results
-2016-09-06 10:31:22,610 DEBUG: Done: Getting Results
-2016-09-06 10:31:22,610 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 17, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,611 INFO: Done: Result Analysis
-2016-09-06 10:31:22,751 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,751 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,752 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:31:22,752 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:31:22,752 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:22,752 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:22,753 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,753 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,753 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,753 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,753 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:22,753 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:22,753 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,753 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,823 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:22,823 DEBUG: Start: Training
-2016-09-06 10:31:22,824 DEBUG: Info: Time for Training: 0.0737769603729[s]
-2016-09-06 10:31:22,824 DEBUG: Done: Training
-2016-09-06 10:31:22,825 DEBUG: Start: Predicting
-2016-09-06 10:31:22,828 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:22,828 DEBUG: Start: Training
-2016-09-06 10:31:22,836 DEBUG: Done: Predicting
-2016-09-06 10:31:22,837 DEBUG: Start: Getting Results
-2016-09-06 10:31:22,839 DEBUG: Done: Getting Results
-2016-09-06 10:31:22,839 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,839 INFO: Done: Result Analysis
-2016-09-06 10:31:22,852 DEBUG: Info: Time for Training: 0.10190820694[s]
-2016-09-06 10:31:22,853 DEBUG: Done: Training
-2016-09-06 10:31:22,853 DEBUG: Start: Predicting
-2016-09-06 10:31:22,856 DEBUG: Done: Predicting
-2016-09-06 10:31:22,856 DEBUG: Start: Getting Results
-2016-09-06 10:31:22,857 DEBUG: Done: Getting Results
-2016-09-06 10:31:22,857 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:22,858 INFO: Done: Result Analysis
-2016-09-06 10:31:22,994 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,994 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:22,994 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:31:22,994 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:31:22,994 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:22,994 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:22,996 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,996 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:31:22,996 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,996 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:31:22,996 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:22,996 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:22,996 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:22,996 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,070 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:23,070 DEBUG: Start: Training
-2016-09-06 10:31:23,077 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:23,077 DEBUG: Start: Training
-2016-09-06 10:31:23,093 DEBUG: Info: Time for Training: 0.10053896904[s]
-2016-09-06 10:31:23,094 DEBUG: Done: Training
-2016-09-06 10:31:23,094 DEBUG: Start: Predicting
-2016-09-06 10:31:23,101 DEBUG: Done: Predicting
-2016-09-06 10:31:23,101 DEBUG: Start: Getting Results
-2016-09-06 10:31:23,102 DEBUG: Info: Time for Training: 0.108783006668[s]
-2016-09-06 10:31:23,102 DEBUG: Done: Training
-2016-09-06 10:31:23,102 DEBUG: Start: Predicting
-2016-09-06 10:31:23,103 DEBUG: Done: Getting Results
-2016-09-06 10:31:23,104 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,104 INFO: Done: Result Analysis
-2016-09-06 10:31:23,107 DEBUG: Done: Predicting
-2016-09-06 10:31:23,107 DEBUG: Start: Getting Results
-2016-09-06 10:31:23,108 DEBUG: Done: Getting Results
-2016-09-06 10:31:23,108 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,108 INFO: Done: Result Analysis
-2016-09-06 10:31:23,242 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,243 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:31:23,243 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:23,243 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,243 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:31:23,244 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:23,244 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,244 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,244 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:23,244 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,245 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,245 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,245 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:23,245 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,282 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:23,282 DEBUG: Start: Training
-2016-09-06 10:31:23,283 DEBUG: Info: Time for Training: 0.0416178703308[s]
-2016-09-06 10:31:23,283 DEBUG: Done: Training
-2016-09-06 10:31:23,284 DEBUG: Start: Predicting
-2016-09-06 10:31:23,286 DEBUG: Done: Predicting
-2016-09-06 10:31:23,286 DEBUG: Start: Getting Results
-2016-09-06 10:31:23,288 DEBUG: Done: Getting Results
-2016-09-06 10:31:23,288 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.785714285714
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.785714285714
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,288 INFO: Done: Result Analysis
-2016-09-06 10:31:23,292 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:23,292 DEBUG: Start: Training
-2016-09-06 10:31:23,296 DEBUG: Info: Time for Training: 0.0540850162506[s]
-2016-09-06 10:31:23,296 DEBUG: Done: Training
-2016-09-06 10:31:23,296 DEBUG: Start: Predicting
-2016-09-06 10:31:23,299 DEBUG: Done: Predicting
-2016-09-06 10:31:23,299 DEBUG: Start: Getting Results
-2016-09-06 10:31:23,301 DEBUG: Done: Getting Results
-2016-09-06 10:31:23,301 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,301 INFO: Done: Result Analysis
-2016-09-06 10:31:23,391 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,391 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,391 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:31:23,391 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:31:23,391 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:23,391 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:23,392 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,392 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,392 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,392 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,393 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:23,393 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:23,393 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,393 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,430 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:23,430 DEBUG: Start: Training
-2016-09-06 10:31:23,431 DEBUG: Info: Time for Training: 0.0408799648285[s]
-2016-09-06 10:31:23,431 DEBUG: Done: Training
-2016-09-06 10:31:23,431 DEBUG: Start: Predicting
-2016-09-06 10:31:23,439 DEBUG: Done: Predicting
-2016-09-06 10:31:23,439 DEBUG: Start: Getting Results
-2016-09-06 10:31:23,440 DEBUG: Done: Getting Results
-2016-09-06 10:31:23,441 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,441 INFO: Done: Result Analysis
-2016-09-06 10:31:23,693 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:23,693 DEBUG: Start: Training
-2016-09-06 10:31:23,736 DEBUG: Info: Time for Training: 0.345639944077[s]
-2016-09-06 10:31:23,736 DEBUG: Done: Training
-2016-09-06 10:31:23,736 DEBUG: Start: Predicting
-2016-09-06 10:31:23,742 DEBUG: Done: Predicting
-2016-09-06 10:31:23,742 DEBUG: Start: Getting Results
-2016-09-06 10:31:23,743 DEBUG: Done: Getting Results
-2016-09-06 10:31:23,743 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.961904761905
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 17, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.961904761905
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,743 INFO: Done: Result Analysis
-2016-09-06 10:31:23,843 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,843 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:23,844 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:31:23,844 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:31:23,844 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:23,844 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:23,845 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,845 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:23,845 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,845 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:23,845 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:23,845 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:23,845 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,845 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:23,914 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:23,914 DEBUG: Start: Training
-2016-09-06 10:31:23,915 DEBUG: Info: Time for Training: 0.0723230838776[s]
-2016-09-06 10:31:23,915 DEBUG: Done: Training
-2016-09-06 10:31:23,915 DEBUG: Start: Predicting
-2016-09-06 10:31:23,917 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:23,917 DEBUG: Start: Training
-2016-09-06 10:31:23,927 DEBUG: Done: Predicting
-2016-09-06 10:31:23,927 DEBUG: Start: Getting Results
-2016-09-06 10:31:23,929 DEBUG: Done: Getting Results
-2016-09-06 10:31:23,929 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,930 INFO: Done: Result Analysis
-2016-09-06 10:31:23,939 DEBUG: Info: Time for Training: 0.0961401462555[s]
-2016-09-06 10:31:23,939 DEBUG: Done: Training
-2016-09-06 10:31:23,939 DEBUG: Start: Predicting
-2016-09-06 10:31:23,942 DEBUG: Done: Predicting
-2016-09-06 10:31:23,942 DEBUG: Start: Getting Results
-2016-09-06 10:31:23,943 DEBUG: Done: Getting Results
-2016-09-06 10:31:23,943 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:31:23,943 INFO: Done: Result Analysis
-2016-09-06 10:31:24,085 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,085 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,085 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:31:24,085 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:31:24,085 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:24,085 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:24,086 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:24,086 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:31:24,086 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:24,086 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:31:24,086 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:24,086 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:24,086 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,086 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,144 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:24,144 DEBUG: Start: Training
-2016-09-06 10:31:24,149 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:24,149 DEBUG: Start: Training
-2016-09-06 10:31:24,161 DEBUG: Info: Time for Training: 0.0771129131317[s]
-2016-09-06 10:31:24,161 DEBUG: Done: Training
-2016-09-06 10:31:24,161 DEBUG: Start: Predicting
-2016-09-06 10:31:24,164 DEBUG: Done: Predicting
-2016-09-06 10:31:24,165 DEBUG: Start: Getting Results
-2016-09-06 10:31:24,166 DEBUG: Done: Getting Results
-2016-09-06 10:31:24,166 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:31:24,166 INFO: Done: Result Analysis
-2016-09-06 10:31:24,172 DEBUG: Info: Time for Training: 0.0878200531006[s]
-2016-09-06 10:31:24,172 DEBUG: Done: Training
-2016-09-06 10:31:24,172 DEBUG: Start: Predicting
-2016-09-06 10:31:24,178 DEBUG: Done: Predicting
-2016-09-06 10:31:24,178 DEBUG: Start: Getting Results
-2016-09-06 10:31:24,179 DEBUG: Done: Getting Results
-2016-09-06 10:31:24,179 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:31:24,180 INFO: Done: Result Analysis
-2016-09-06 10:31:24,332 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,332 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:31:24,332 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:24,333 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,333 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:24,333 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:31:24,333 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:24,333 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:24,333 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:24,333 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,334 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:24,334 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:24,334 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:24,334 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,384 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:24,384 DEBUG: Start: Training
-2016-09-06 10:31:24,386 DEBUG: Info: Time for Training: 0.0551080703735[s]
-2016-09-06 10:31:24,386 DEBUG: Done: Training
-2016-09-06 10:31:24,387 DEBUG: Start: Predicting
-2016-09-06 10:31:24,389 DEBUG: Done: Predicting
-2016-09-06 10:31:24,389 DEBUG: Start: Getting Results
-2016-09-06 10:31:24,391 DEBUG: Done: Getting Results
-2016-09-06 10:31:24,391 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.919047619048
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:31:24,392 INFO: Done: Result Analysis
-2016-09-06 10:31:24,418 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:24,418 DEBUG: Start: Training
-2016-09-06 10:31:24,423 DEBUG: Info: Time for Training: 0.0905458927155[s]
-2016-09-06 10:31:24,423 DEBUG: Done: Training
-2016-09-06 10:31:24,423 DEBUG: Start: Predicting
-2016-09-06 10:31:24,427 DEBUG: Done: Predicting
-2016-09-06 10:31:24,428 DEBUG: Start: Getting Results
-2016-09-06 10:31:24,430 DEBUG: Done: Getting Results
-2016-09-06 10:31:24,430 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:31:24,431 INFO: Done: Result Analysis
-2016-09-06 10:31:24,595 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,595 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:31:24,596 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:24,596 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:24,597 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:24,597 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:24,597 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,600 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:24,601 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:31:24,601 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:24,602 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:24,602 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:24,602 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:24,602 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:24,652 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:24,652 DEBUG: Start: Training
-2016-09-06 10:31:24,653 DEBUG: Info: Time for Training: 0.0590059757233[s]
-2016-09-06 10:31:24,653 DEBUG: Done: Training
-2016-09-06 10:31:24,653 DEBUG: Start: Predicting
-2016-09-06 10:31:24,664 DEBUG: Done: Predicting
-2016-09-06 10:31:24,665 DEBUG: Start: Getting Results
-2016-09-06 10:31:24,667 DEBUG: Done: Getting Results
-2016-09-06 10:31:24,667 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:31:24,667 INFO: Done: Result Analysis
-2016-09-06 10:31:24,960 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:24,960 DEBUG: Start: Training
-2016-09-06 10:31:25,007 DEBUG: Info: Time for Training: 0.407965898514[s]
-2016-09-06 10:31:25,008 DEBUG: Done: Training
-2016-09-06 10:31:25,008 DEBUG: Start: Predicting
-2016-09-06 10:31:25,014 DEBUG: Done: Predicting
-2016-09-06 10:31:25,014 DEBUG: Start: Getting Results
-2016-09-06 10:31:25,015 DEBUG: Done: Getting Results
-2016-09-06 10:31:25,016 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 17, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.4
-
-
- Classification took 0:00:00
-2016-09-06 10:31:25,016 INFO: Done: Result Analysis
-2016-09-06 10:31:25,149 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:25,149 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:31:25,149 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:31:25,149 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:31:25,149 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:25,149 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:31:25,150 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:25,150 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:31:25,150 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:25,150 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:31:25,151 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:25,151 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:31:25,151 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:25,151 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:31:25,220 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:25,220 DEBUG: Start: Training
-2016-09-06 10:31:25,221 DEBUG: Info: Time for Training: 0.0730149745941[s]
-2016-09-06 10:31:25,222 DEBUG: Done: Training
-2016-09-06 10:31:25,222 DEBUG: Start: Predicting
-2016-09-06 10:31:25,226 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:31:25,226 DEBUG: Start: Training
-2016-09-06 10:31:25,234 DEBUG: Done: Predicting
-2016-09-06 10:31:25,235 DEBUG: Start: Getting Results
-2016-09-06 10:31:25,236 DEBUG: Done: Getting Results
-2016-09-06 10:31:25,236 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:31:25,237 INFO: Done: Result Analysis
-2016-09-06 10:31:25,251 DEBUG: Info: Time for Training: 0.103055000305[s]
-2016-09-06 10:31:25,252 DEBUG: Done: Training
-2016-09-06 10:31:25,253 DEBUG: Start: Predicting
-2016-09-06 10:31:25,259 DEBUG: Done: Predicting
-2016-09-06 10:31:25,259 DEBUG: Start: Getting Results
-2016-09-06 10:31:25,261 DEBUG: Done: Getting Results
-2016-09-06 10:31:25,261 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:31:25,262 INFO: Done: Result Analysis
-2016-09-06 10:31:25,546 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:31:25,546 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:31:25,547 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 10:31:25,547 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:31:25,547 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:31:25,547 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 10:31:25,548 INFO: Info: Shape of View0 :(300, 8)
-2016-09-06 10:31:25,548 INFO: Info: Shape of View2 :(300, 5)
-2016-09-06 10:31:25,548 INFO: Info: Shape of View1 :(300, 10)
-2016-09-06 10:31:25,548 INFO: Info: Shape of View3 :(300, 13)
-2016-09-06 10:31:25,548 INFO: Done: Read Database Files
-2016-09-06 10:31:25,549 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:31:25,549 INFO: Info: Shape of View2 :(300, 5)
-2016-09-06 10:31:25,549 INFO: Info: Shape of View3 :(300, 13)
-2016-09-06 10:31:25,549 INFO: Done: Read Database Files
-2016-09-06 10:31:25,550 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:31:25,553 INFO: Done: Determine validation split
-2016-09-06 10:31:25,553 INFO: Start: Determine 5 folds
-2016-09-06 10:31:25,554 INFO: Done: Determine validation split
-2016-09-06 10:31:25,554 INFO: Start: Determine 5 folds
-2016-09-06 10:31:25,561 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:31:25,561 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:31:25,561 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:31:25,561 INFO: Done: Determine folds
-2016-09-06 10:31:25,561 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:31:25,561 INFO: Start: Classification
-2016-09-06 10:31:25,561 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:31:25,561 INFO: Start: Fold number 1
-2016-09-06 10:31:25,561 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:31:25,561 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:31:25,562 INFO: Done: Determine folds
-2016-09-06 10:31:25,562 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:31:25,562 INFO: Start: Classification
-2016-09-06 10:31:25,562 INFO: Start: Fold number 1
-2016-09-06 10:31:25,604 DEBUG: Start: Iteration 1
-2016-09-06 10:31:25,618 DEBUG: View 0 : 0.538461538462
-2016-09-06 10:31:25,626 DEBUG: View 1 : 0.550295857988
-2016-09-06 10:31:25,634 DEBUG: View 2 : 0.520710059172
-2016-09-06 10:31:25,642 DEBUG: View 3 : 0.508875739645
-2016-09-06 10:31:25,674 DEBUG: Best view : View3
-2016-09-06 10:31:25,752 DEBUG: Start: Iteration 2
-2016-09-06 10:31:25,760 DEBUG: View 0 : 0.715976331361
-2016-09-06 10:31:25,767 DEBUG: View 1 : 0.715976331361
-2016-09-06 10:31:25,774 DEBUG: View 2 : 0.704142011834
-2016-09-06 10:31:25,782 DEBUG: View 3 : 0.751479289941
-2016-09-06 10:31:25,819 DEBUG: Best view : View3
-2016-09-06 10:31:25,964 DEBUG: Start: Iteration 3
-2016-09-06 10:31:25,980 DEBUG: View 0 : 0.715976331361
-2016-09-06 10:31:25,987 DEBUG: View 1 : 0.715976331361
-2016-09-06 10:31:25,994 DEBUG: View 2 : 0.704142011834
-2016-09-06 10:31:26,001 DEBUG: View 3 : 0.751479289941
-2016-09-06 10:31:26,040 DEBUG: Best view : View3
-2016-09-06 10:31:26,244 DEBUG: Start: Iteration 4
-2016-09-06 10:31:26,251 DEBUG: View 0 : 0.692307692308
-2016-09-06 10:31:26,258 DEBUG: View 1 : 0.573964497041
-2016-09-06 10:31:26,265 DEBUG: View 2 : 0.686390532544
-2016-09-06 10:31:26,272 DEBUG: View 3 : 0.633136094675
-2016-09-06 10:31:26,313 DEBUG: Best view : View2
-2016-09-06 10:31:26,583 INFO: Start: Classification
-2016-09-06 10:31:27,038 INFO: Done: Fold number 1
-2016-09-06 10:31:27,038 INFO: Start: Fold number 2
-2016-09-06 10:31:27,068 DEBUG: Start: Iteration 1
-2016-09-06 10:31:27,075 DEBUG: View 0 : 0.514792899408
-2016-09-06 10:31:27,082 DEBUG: View 1 : 0.538461538462
-2016-09-06 10:31:27,089 DEBUG: View 2 : 0.473372781065
-2016-09-06 10:31:27,096 DEBUG: View 3 : 0.497041420118
-2016-09-06 10:31:27,127 DEBUG: Best view : View1
-2016-09-06 10:31:27,203 DEBUG: Start: Iteration 2
-2016-09-06 10:31:27,210 DEBUG: View 0 : 0.674556213018
-2016-09-06 10:31:27,217 DEBUG: View 1 : 0.727810650888
-2016-09-06 10:31:27,224 DEBUG: View 2 : 0.644970414201
-2016-09-06 10:31:27,232 DEBUG: View 3 : 0.686390532544
-2016-09-06 10:31:27,268 DEBUG: Best view : View1
-2016-09-06 10:31:27,409 DEBUG: Start: Iteration 3
-2016-09-06 10:31:27,416 DEBUG: View 0 : 0.674556213018
-2016-09-06 10:31:27,423 DEBUG: View 1 : 0.727810650888
-2016-09-06 10:31:27,430 DEBUG: View 2 : 0.644970414201
-2016-09-06 10:31:27,437 DEBUG: View 3 : 0.686390532544
-2016-09-06 10:31:27,478 DEBUG: Best view : View1
-2016-09-06 10:31:27,682 DEBUG: Start: Iteration 4
-2016-09-06 10:31:27,689 DEBUG: View 0 : 0.644970414201
-2016-09-06 10:31:27,696 DEBUG: View 1 : 0.692307692308
-2016-09-06 10:31:27,703 DEBUG: View 2 : 0.650887573964
-2016-09-06 10:31:27,711 DEBUG: View 3 : 0.698224852071
-2016-09-06 10:31:27,752 DEBUG: Best view : View1
-2016-09-06 10:31:28,021 INFO: Start: Classification
-2016-09-06 10:31:28,472 INFO: Done: Fold number 2
-2016-09-06 10:31:28,472 INFO: Start: Fold number 3
-2016-09-06 10:31:28,501 DEBUG: Start: Iteration 1
-2016-09-06 10:31:28,508 DEBUG: View 0 : 0.497041420118
-2016-09-06 10:31:28,514 DEBUG: View 1 : 0.485207100592
-2016-09-06 10:31:28,521 DEBUG: View 2 : 0.491124260355
-2016-09-06 10:31:28,528 DEBUG: View 3 : 0.568047337278
-2016-09-06 10:31:28,558 DEBUG: Best view : View0
-2016-09-06 10:31:28,633 DEBUG: Start: Iteration 2
-2016-09-06 10:31:28,640 DEBUG: View 0 : 0.674556213018
-2016-09-06 10:31:28,647 DEBUG: View 1 : 0.597633136095
-2016-09-06 10:31:28,654 DEBUG: View 2 : 0.692307692308
-2016-09-06 10:31:28,661 DEBUG: View 3 : 0.751479289941
-2016-09-06 10:31:28,697 DEBUG: Best view : View3
-2016-09-06 10:31:28,836 DEBUG: Start: Iteration 3
-2016-09-06 10:31:28,844 DEBUG: View 0 : 0.674556213018
-2016-09-06 10:31:28,851 DEBUG: View 1 : 0.597633136095
-2016-09-06 10:31:28,857 DEBUG: View 2 : 0.692307692308
-2016-09-06 10:31:28,865 DEBUG: View 3 : 0.751479289941
-2016-09-06 10:31:28,903 DEBUG: Best view : View3
-2016-09-06 10:31:29,107 DEBUG: Start: Iteration 4
-2016-09-06 10:31:29,114 DEBUG: View 0 : 0.656804733728
-2016-09-06 10:31:29,121 DEBUG: View 1 : 0.639053254438
-2016-09-06 10:31:29,128 DEBUG: View 2 : 0.674556213018
-2016-09-06 10:31:29,136 DEBUG: View 3 : 0.792899408284
-2016-09-06 10:31:29,177 DEBUG: Best view : View3
-2016-09-06 10:31:29,444 INFO: Start: Classification
-2016-09-06 10:31:29,896 INFO: Done: Fold number 3
-2016-09-06 10:31:29,896 INFO: Start: Fold number 4
-2016-09-06 10:31:29,924 DEBUG: Start: Iteration 1
-2016-09-06 10:31:29,931 DEBUG: View 0 : 0.479289940828
-2016-09-06 10:31:29,938 DEBUG: View 1 : 0.479289940828
-2016-09-06 10:31:29,945 DEBUG: View 2 : 0.479289940828
-2016-09-06 10:31:29,951 DEBUG: View 3 : 0.479289940828
-2016-09-06 10:31:29,951 WARNING: WARNING: All bad for iteration 0
-2016-09-06 10:31:29,982 DEBUG: Best view : View0
-2016-09-06 10:31:30,058 DEBUG: Start: Iteration 2
-2016-09-06 10:31:30,065 DEBUG: View 0 : 0.627218934911
-2016-09-06 10:31:30,072 DEBUG: View 1 : 0.686390532544
-2016-09-06 10:31:30,080 DEBUG: View 2 : 0.686390532544
-2016-09-06 10:31:30,087 DEBUG: View 3 : 0.751479289941
-2016-09-06 10:31:30,123 DEBUG: Best view : View3
-2016-09-06 10:31:30,265 DEBUG: Start: Iteration 3
-2016-09-06 10:31:30,272 DEBUG: View 0 : 0.627218934911
-2016-09-06 10:31:30,280 DEBUG: View 1 : 0.686390532544
-2016-09-06 10:31:30,287 DEBUG: View 2 : 0.686390532544
-2016-09-06 10:31:30,294 DEBUG: View 3 : 0.751479289941
-2016-09-06 10:31:30,334 DEBUG: Best view : View3
-2016-09-06 10:31:30,537 DEBUG: Start: Iteration 4
-2016-09-06 10:31:30,544 DEBUG: View 0 : 0.615384615385
-2016-09-06 10:31:30,551 DEBUG: View 1 : 0.603550295858
-2016-09-06 10:31:30,559 DEBUG: View 2 : 0.644970414201
-2016-09-06 10:31:30,566 DEBUG: View 3 : 0.662721893491
-2016-09-06 10:31:30,607 DEBUG: Best view : View3
-2016-09-06 10:31:30,876 INFO: Start: Classification
-2016-09-06 10:31:31,332 INFO: Done: Fold number 4
-2016-09-06 10:31:31,332 INFO: Start: Fold number 5
-2016-09-06 10:31:31,361 DEBUG: Start: Iteration 1
-2016-09-06 10:31:31,368 DEBUG: View 0 : 0.491124260355
-2016-09-06 10:31:31,375 DEBUG: View 1 : 0.479289940828
-2016-09-06 10:31:31,382 DEBUG: View 2 : 0.479289940828
-2016-09-06 10:31:31,388 DEBUG: View 3 : 0.526627218935
-2016-09-06 10:31:31,418 DEBUG: Best view : View3
-2016-09-06 10:31:31,493 DEBUG: Start: Iteration 2
-2016-09-06 10:31:31,501 DEBUG: View 0 : 0.668639053254
-2016-09-06 10:31:31,508 DEBUG: View 1 : 0.656804733728
-2016-09-06 10:31:31,515 DEBUG: View 2 : 0.656804733728
-2016-09-06 10:31:31,523 DEBUG: View 3 : 0.715976331361
-2016-09-06 10:31:31,559 DEBUG: Best view : View3
-2016-09-06 10:31:31,698 DEBUG: Start: Iteration 3
-2016-09-06 10:31:31,705 DEBUG: View 0 : 0.668639053254
-2016-09-06 10:31:31,712 DEBUG: View 1 : 0.656804733728
-2016-09-06 10:31:31,719 DEBUG: View 2 : 0.656804733728
-2016-09-06 10:31:31,727 DEBUG: View 3 : 0.715976331361
-2016-09-06 10:31:31,765 DEBUG: Best view : View3
-2016-09-06 10:31:31,969 DEBUG: Start: Iteration 4
-2016-09-06 10:31:31,977 DEBUG: View 0 : 0.633136094675
-2016-09-06 10:31:31,984 DEBUG: View 1 : 0.556213017751
-2016-09-06 10:31:31,991 DEBUG: View 2 : 0.568047337278
-2016-09-06 10:31:31,998 DEBUG: View 3 : 0.639053254438
-2016-09-06 10:31:32,039 DEBUG: Best view : View3
-2016-09-06 10:31:32,311 INFO: Start: Classification
-2016-09-06 10:31:32,766 INFO: Done: Fold number 5
-2016-09-06 10:31:32,767 INFO: Done: Classification
-2016-09-06 10:31:32,767 INFO: Info: Time for Classification: 7[s]
-2016-09-06 10:31:32,767 INFO: Start: Result Analysis for Mumbo
-2016-09-06 10:31:35,218 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 73.9644970414
- -On Test : 48.5714285714
- -On Validation : 51.6853932584Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 8), View1 of shape (300, 10), View2 of shape (300, 5), View3 of shape (300, 13)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:06 0:00:00
- Total 0:00:19 0:00:02
- So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.266272189349
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.255621301775
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.261538461538
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.26449704142
- - Percentage of time chosen : 0.3
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.250887573964
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.268639053254
- - Percentage of time chosen : 0.4
- - On View2 :
- - Mean average Accuracy : 0.241420118343
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.256804733728
- - Percentage of time chosen : 0.0
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.250295857988
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.231952662722
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.255029585799
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.286390532544
- - Percentage of time chosen : 0.3
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.234911242604
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.245562130178
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.249704142012
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.26449704142
- - Percentage of time chosen : 0.3
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.246153846154
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.234911242604
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.236094674556
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.259763313609
- - Percentage of time chosen : 0.4
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View3
- Fold 2
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View1
- Fold 3
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 4
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 5
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View3
- - Iteration 2
- Fold 1
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- Fold 2
- Accuracy on train : 72.7810650888
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View1
- Fold 3
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 62.9213483146
- Selected View : View3
- Fold 4
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- Fold 5
- Accuracy on train : 71.5976331361
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- - Iteration 3
- Fold 1
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- Fold 2
- Accuracy on train : 72.7810650888
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View1
- Fold 3
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 62.9213483146
- Selected View : View3
- Fold 4
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- Fold 5
- Accuracy on train : 71.5976331361
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- - Iteration 4
- Fold 1
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View2
- Fold 2
- Accuracy on train : 72.7810650888
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View1
- Fold 3
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 62.9213483146
- Selected View : View3
- Fold 4
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- Fold 5
- Accuracy on train : 71.5976331361
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- - Iteration 5
- Fold 1
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 2
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 3
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 4
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 5
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
-2016-09-06 10:31:35,405 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 47001d62273a4a5ea015e3796b2c3ec2e25a4196..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 03adc80f728caac1b9b830f0b577977feba3c44a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.938095238095
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.938095238095
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c7e9d43274727a3c4aec18d8b743fc62f0a91b01..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 461f44c9e4e58ede514323459fe9974d3f848019..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 17, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 207b9a7e43cdd6abc7fd3387bf0a390ef0bc7ef5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ba87e538664d0cfbffd36505ed1c464111c02f6a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3c15e79999a413450bdc3caab769a6f33b2bb73a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8f4eb2ffd27c3c75a6e3266a9d377a79093e1a4a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103121Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a51fc411d8e5e72d67393891f9a87fb2e742de3f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a89da4e85160e762e0120082c1416988040498a5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.838095238095
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.838095238095
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f62702a63465eba6fa423787af629e2e0346e47b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2ca481244c162bee62e23f73e2def3b3ad8e18cb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 17, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5dfa38e3609dec5f1f4936c3ebaef76e51a2d938..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7a807ce4fcf2c721abea540f95dca1d1385ff64e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103122Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c696ff7a1b0aadcedba2b7ef82d442e9441b88e3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 12a61d1ed8e3c7b34937737b04ed946a4741e12e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.785714285714
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.785714285714
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7afc9baf908d951b570ebd741436d025caacda42..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8b74e15997b5669225e79e81368184888dd75321..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.961904761905
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 17, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.961904761905
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5368fdbf81e67273da1a517f7f7cec0b61c464ae..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 66e58177f2e55924821ce8a85708d80d55c123d4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4959e4f4d4bdb768828241fc18a03bb20bc721a9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e1f9847a2a81f2ff1d479e62dddbe37a473d987a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103123Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 474fe79a70c8360c5d1a26e84e43be1b5027fe37..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e82c8d88143a257c1a0520149eaaf739e0190808..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.919047619048
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bb1b14bb1cbdf6dfbd7d9205a1f4990b63a998b9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b9e72ceaf74f5230c64760fa06d8eb9b4836e9c9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c25b7951cdc7de7d0e1ac57b7707f5147f5078d5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103124Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 06e42109d453523c1aaf677df928e42e4e5078cf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 17, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ec5c49784c9deeab11d9cb6b398de439e22b6e81..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 39374ee4729da9905a32b9f5346d79ebe57027f3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103125Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2481
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index 129780daaf421ae71fa74885fd4baaf13677ad38..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 1d66943e2c8b83707705f0a7c047f3475dec17d1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103135Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,235 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 73.9644970414
- -On Test : 48.5714285714
- -On Validation : 51.6853932584Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 8), View1 of shape (300, 10), View2 of shape (300, 5), View3 of shape (300, 13)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:05 0:00:00
- Fold 5 0:00:06 0:00:00
- Total 0:00:19 0:00:02
- So a total classification time of 0:00:07.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.266272189349
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.255621301775
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.261538461538
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.26449704142
- - Percentage of time chosen : 0.3
- - Fold 1, used 5
- - On View0 :
- - Mean average Accuracy : 0.250887573964
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.268639053254
- - Percentage of time chosen : 0.4
- - On View2 :
- - Mean average Accuracy : 0.241420118343
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.256804733728
- - Percentage of time chosen : 0.0
- - Fold 2, used 5
- - On View0 :
- - Mean average Accuracy : 0.250295857988
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.231952662722
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.255029585799
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.286390532544
- - Percentage of time chosen : 0.3
- - Fold 3, used 5
- - On View0 :
- - Mean average Accuracy : 0.234911242604
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.245562130178
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.249704142012
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.26449704142
- - Percentage of time chosen : 0.3
- - Fold 4, used 5
- - On View0 :
- - Mean average Accuracy : 0.246153846154
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.234911242604
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.236094674556
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.259763313609
- - Percentage of time chosen : 0.4
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View3
- Fold 2
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View1
- Fold 3
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 4
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 5
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View3
- - Iteration 2
- Fold 1
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- Fold 2
- Accuracy on train : 72.7810650888
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View1
- Fold 3
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 62.9213483146
- Selected View : View3
- Fold 4
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- Fold 5
- Accuracy on train : 71.5976331361
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- - Iteration 3
- Fold 1
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- Fold 2
- Accuracy on train : 72.7810650888
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View1
- Fold 3
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 62.9213483146
- Selected View : View3
- Fold 4
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- Fold 5
- Accuracy on train : 71.5976331361
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- - Iteration 4
- Fold 1
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View2
- Fold 2
- Accuracy on train : 72.7810650888
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View1
- Fold 3
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 62.9213483146
- Selected View : View3
- Fold 4
- Accuracy on train : 75.1479289941
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- Fold 5
- Accuracy on train : 71.5976331361
- Accuracy on test : 0.0
- Accuracy on validation : 49.4382022472
- Selected View : View3
- - Iteration 5
- Fold 1
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 2
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 3
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 4
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
- Fold 5
- Accuracy on train : 47.9289940828
- Accuracy on test : 0.0
- Accuracy on validation : 47.191011236
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index df4e947675f7e8d232b7dd710800e7689ec2c36d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103409-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1580 +0,0 @@
-2016-09-06 10:34:09,127 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:34:09,127 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.0001380625 Gbytes /!\
-2016-09-06 10:34:14,141 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:34:14,144 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:34:14,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,196 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:34:14,197 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:34:14,197 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:14,197 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:14,197 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,197 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,197 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,197 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,198 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:14,198 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:14,198 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,198 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,227 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:14,227 DEBUG: Start: Training
-2016-09-06 10:34:14,228 DEBUG: Info: Time for Training: 0.0323169231415[s]
-2016-09-06 10:34:14,228 DEBUG: Done: Training
-2016-09-06 10:34:14,228 DEBUG: Start: Predicting
-2016-09-06 10:34:14,231 DEBUG: Done: Predicting
-2016-09-06 10:34:14,231 DEBUG: Start: Getting Results
-2016-09-06 10:34:14,232 DEBUG: Done: Getting Results
-2016-09-06 10:34:14,233 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.652380952381
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,233 INFO: Done: Result Analysis
-2016-09-06 10:34:14,245 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:14,245 DEBUG: Start: Training
-2016-09-06 10:34:14,249 DEBUG: Info: Time for Training: 0.0530989170074[s]
-2016-09-06 10:34:14,249 DEBUG: Done: Training
-2016-09-06 10:34:14,249 DEBUG: Start: Predicting
-2016-09-06 10:34:14,252 DEBUG: Done: Predicting
-2016-09-06 10:34:14,252 DEBUG: Start: Getting Results
-2016-09-06 10:34:14,254 DEBUG: Done: Getting Results
-2016-09-06 10:34:14,254 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,254 INFO: Done: Result Analysis
-2016-09-06 10:34:14,344 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,344 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,344 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:34:14,344 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:34:14,345 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:14,345 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:14,345 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,345 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,345 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,345 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,345 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:14,345 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:14,345 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,345 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,378 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:14,379 DEBUG: Start: Training
-2016-09-06 10:34:14,379 DEBUG: Info: Time for Training: 0.0353238582611[s]
-2016-09-06 10:34:14,379 DEBUG: Done: Training
-2016-09-06 10:34:14,379 DEBUG: Start: Predicting
-2016-09-06 10:34:14,386 DEBUG: Done: Predicting
-2016-09-06 10:34:14,386 DEBUG: Start: Getting Results
-2016-09-06 10:34:14,387 DEBUG: Done: Getting Results
-2016-09-06 10:34:14,387 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 34
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,388 INFO: Done: Result Analysis
-2016-09-06 10:34:14,487 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:14,487 DEBUG: Start: Training
-2016-09-06 10:34:14,505 DEBUG: Info: Time for Training: 0.160764932632[s]
-2016-09-06 10:34:14,505 DEBUG: Done: Training
-2016-09-06 10:34:14,505 DEBUG: Start: Predicting
-2016-09-06 10:34:14,509 DEBUG: Done: Predicting
-2016-09-06 10:34:14,509 DEBUG: Start: Getting Results
-2016-09-06 10:34:14,510 DEBUG: Done: Getting Results
-2016-09-06 10:34:14,510 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,510 INFO: Done: Result Analysis
-2016-09-06 10:34:14,599 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,599 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,599 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:34:14,599 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:34:14,599 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:14,599 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:14,600 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,600 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,600 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,600 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,601 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:14,601 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:14,601 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,601 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,648 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:14,648 DEBUG: Start: Training
-2016-09-06 10:34:14,649 DEBUG: Info: Time for Training: 0.0514440536499[s]
-2016-09-06 10:34:14,649 DEBUG: Done: Training
-2016-09-06 10:34:14,649 DEBUG: Start: Predicting
-2016-09-06 10:34:14,651 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:14,651 DEBUG: Start: Training
-2016-09-06 10:34:14,653 DEBUG: Done: Predicting
-2016-09-06 10:34:14,654 DEBUG: Start: Getting Results
-2016-09-06 10:34:14,655 DEBUG: Done: Getting Results
-2016-09-06 10:34:14,655 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,656 INFO: Done: Result Analysis
-2016-09-06 10:34:14,670 DEBUG: Info: Time for Training: 0.0719361305237[s]
-2016-09-06 10:34:14,670 DEBUG: Done: Training
-2016-09-06 10:34:14,670 DEBUG: Start: Predicting
-2016-09-06 10:34:14,673 DEBUG: Done: Predicting
-2016-09-06 10:34:14,673 DEBUG: Start: Getting Results
-2016-09-06 10:34:14,675 DEBUG: Done: Getting Results
-2016-09-06 10:34:14,675 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.471428571429
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,675 INFO: Done: Result Analysis
-2016-09-06 10:34:14,747 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,748 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:34:14,748 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:14,748 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,748 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,749 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,749 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:34:14,749 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:14,749 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:14,749 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,750 DEBUG: Info: Shape X_train:(210, 9), Length of y_train:210
-2016-09-06 10:34:14,750 DEBUG: Info: Shape X_test:(90, 9), Length of y_test:90
-2016-09-06 10:34:14,750 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:14,750 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,803 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:14,803 DEBUG: Start: Training
-2016-09-06 10:34:14,803 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:14,804 DEBUG: Start: Training
-2016-09-06 10:34:14,821 DEBUG: Info: Time for Training: 0.0726821422577[s]
-2016-09-06 10:34:14,821 DEBUG: Done: Training
-2016-09-06 10:34:14,821 DEBUG: Start: Predicting
-2016-09-06 10:34:14,821 DEBUG: Info: Time for Training: 0.0746259689331[s]
-2016-09-06 10:34:14,822 DEBUG: Done: Training
-2016-09-06 10:34:14,822 DEBUG: Start: Predicting
-2016-09-06 10:34:14,825 DEBUG: Done: Predicting
-2016-09-06 10:34:14,825 DEBUG: Start: Getting Results
-2016-09-06 10:34:14,826 DEBUG: Done: Predicting
-2016-09-06 10:34:14,826 DEBUG: Start: Getting Results
-2016-09-06 10:34:14,826 DEBUG: Done: Getting Results
-2016-09-06 10:34:14,827 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,827 INFO: Done: Result Analysis
-2016-09-06 10:34:14,827 DEBUG: Done: Getting Results
-2016-09-06 10:34:14,827 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,828 INFO: Done: Result Analysis
-2016-09-06 10:34:14,892 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,893 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:34:14,893 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:14,893 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:14,893 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:34:14,893 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:14,894 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:14,894 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:14,894 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:14,894 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:14,894 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:14,894 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,894 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:14,895 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:14,927 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:14,927 DEBUG: Start: Training
-2016-09-06 10:34:14,928 DEBUG: Info: Time for Training: 0.036386013031[s]
-2016-09-06 10:34:14,928 DEBUG: Done: Training
-2016-09-06 10:34:14,928 DEBUG: Start: Predicting
-2016-09-06 10:34:14,931 DEBUG: Done: Predicting
-2016-09-06 10:34:14,932 DEBUG: Start: Getting Results
-2016-09-06 10:34:14,933 DEBUG: Done: Getting Results
-2016-09-06 10:34:14,933 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,933 INFO: Done: Result Analysis
-2016-09-06 10:34:14,948 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:14,948 DEBUG: Start: Training
-2016-09-06 10:34:14,952 DEBUG: Info: Time for Training: 0.0607821941376[s]
-2016-09-06 10:34:14,953 DEBUG: Done: Training
-2016-09-06 10:34:14,953 DEBUG: Start: Predicting
-2016-09-06 10:34:14,955 DEBUG: Done: Predicting
-2016-09-06 10:34:14,956 DEBUG: Start: Getting Results
-2016-09-06 10:34:14,957 DEBUG: Done: Getting Results
-2016-09-06 10:34:14,957 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:34:14,958 INFO: Done: Result Analysis
-2016-09-06 10:34:15,039 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,039 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,039 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:34:15,039 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:34:15,040 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:15,040 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:15,040 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,040 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,040 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,040 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,040 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:15,040 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:15,040 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,040 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,073 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:15,073 DEBUG: Start: Training
-2016-09-06 10:34:15,074 DEBUG: Info: Time for Training: 0.0351400375366[s]
-2016-09-06 10:34:15,074 DEBUG: Done: Training
-2016-09-06 10:34:15,074 DEBUG: Start: Predicting
-2016-09-06 10:34:15,081 DEBUG: Done: Predicting
-2016-09-06 10:34:15,081 DEBUG: Start: Getting Results
-2016-09-06 10:34:15,083 DEBUG: Done: Getting Results
-2016-09-06 10:34:15,083 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 34
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,083 INFO: Done: Result Analysis
-2016-09-06 10:34:15,182 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:15,182 DEBUG: Start: Training
-2016-09-06 10:34:15,200 DEBUG: Info: Time for Training: 0.161078929901[s]
-2016-09-06 10:34:15,200 DEBUG: Done: Training
-2016-09-06 10:34:15,200 DEBUG: Start: Predicting
-2016-09-06 10:34:15,204 DEBUG: Done: Predicting
-2016-09-06 10:34:15,204 DEBUG: Start: Getting Results
-2016-09-06 10:34:15,205 DEBUG: Done: Getting Results
-2016-09-06 10:34:15,205 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.685714285714
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.685714285714
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,205 INFO: Done: Result Analysis
-2016-09-06 10:34:15,291 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,291 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:34:15,292 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:34:15,292 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:15,292 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:15,293 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,293 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,294 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,294 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,294 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:15,294 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:15,294 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,294 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,362 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:15,362 DEBUG: Start: Training
-2016-09-06 10:34:15,363 DEBUG: Info: Time for Training: 0.07275390625[s]
-2016-09-06 10:34:15,363 DEBUG: Done: Training
-2016-09-06 10:34:15,363 DEBUG: Start: Predicting
-2016-09-06 10:34:15,369 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:15,369 DEBUG: Start: Training
-2016-09-06 10:34:15,378 DEBUG: Done: Predicting
-2016-09-06 10:34:15,378 DEBUG: Start: Getting Results
-2016-09-06 10:34:15,380 DEBUG: Done: Getting Results
-2016-09-06 10:34:15,380 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.633333333333
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.633333333333
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,381 INFO: Done: Result Analysis
-2016-09-06 10:34:15,396 DEBUG: Info: Time for Training: 0.105423927307[s]
-2016-09-06 10:34:15,396 DEBUG: Done: Training
-2016-09-06 10:34:15,396 DEBUG: Start: Predicting
-2016-09-06 10:34:15,399 DEBUG: Done: Predicting
-2016-09-06 10:34:15,399 DEBUG: Start: Getting Results
-2016-09-06 10:34:15,401 DEBUG: Done: Getting Results
-2016-09-06 10:34:15,401 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.388888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.388888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,401 INFO: Done: Result Analysis
-2016-09-06 10:34:15,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,536 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:34:15,536 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:34:15,536 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:15,536 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:15,537 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,537 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 10:34:15,537 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,537 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 10:34:15,537 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:15,537 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:15,537 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,537 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,607 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:15,607 DEBUG: Start: Training
-2016-09-06 10:34:15,616 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:15,616 DEBUG: Start: Training
-2016-09-06 10:34:15,632 DEBUG: Info: Time for Training: 0.0972080230713[s]
-2016-09-06 10:34:15,632 DEBUG: Done: Training
-2016-09-06 10:34:15,632 DEBUG: Start: Predicting
-2016-09-06 10:34:15,641 DEBUG: Done: Predicting
-2016-09-06 10:34:15,641 DEBUG: Start: Getting Results
-2016-09-06 10:34:15,643 DEBUG: Done: Getting Results
-2016-09-06 10:34:15,643 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,643 DEBUG: Info: Time for Training: 0.10800909996[s]
-2016-09-06 10:34:15,643 DEBUG: Done: Training
-2016-09-06 10:34:15,643 INFO: Done: Result Analysis
-2016-09-06 10:34:15,643 DEBUG: Start: Predicting
-2016-09-06 10:34:15,647 DEBUG: Done: Predicting
-2016-09-06 10:34:15,648 DEBUG: Start: Getting Results
-2016-09-06 10:34:15,649 DEBUG: Done: Getting Results
-2016-09-06 10:34:15,649 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,649 INFO: Done: Result Analysis
-2016-09-06 10:34:15,791 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,791 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,792 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:34:15,792 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:34:15,792 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:15,792 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:15,793 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:15,793 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:15,793 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:15,793 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:15,793 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:15,793 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:15,793 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,793 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,840 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:15,840 DEBUG: Start: Training
-2016-09-06 10:34:15,841 DEBUG: Info: Time for Training: 0.0506250858307[s]
-2016-09-06 10:34:15,842 DEBUG: Done: Training
-2016-09-06 10:34:15,842 DEBUG: Start: Predicting
-2016-09-06 10:34:15,845 DEBUG: Done: Predicting
-2016-09-06 10:34:15,846 DEBUG: Start: Getting Results
-2016-09-06 10:34:15,848 DEBUG: Done: Getting Results
-2016-09-06 10:34:15,848 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,848 INFO: Done: Result Analysis
-2016-09-06 10:34:15,867 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:15,867 DEBUG: Start: Training
-2016-09-06 10:34:15,873 DEBUG: Info: Time for Training: 0.081845998764[s]
-2016-09-06 10:34:15,873 DEBUG: Done: Training
-2016-09-06 10:34:15,873 DEBUG: Start: Predicting
-2016-09-06 10:34:15,876 DEBUG: Done: Predicting
-2016-09-06 10:34:15,876 DEBUG: Start: Getting Results
-2016-09-06 10:34:15,877 DEBUG: Done: Getting Results
-2016-09-06 10:34:15,878 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:34:15,878 INFO: Done: Result Analysis
-2016-09-06 10:34:15,935 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,935 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:15,936 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:34:15,936 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:34:15,936 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:15,936 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:15,937 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:15,937 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:15,937 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:15,937 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:15,937 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:15,937 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:15,937 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,937 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:15,986 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:15,986 DEBUG: Start: Training
-2016-09-06 10:34:15,987 DEBUG: Info: Time for Training: 0.0520849227905[s]
-2016-09-06 10:34:15,987 DEBUG: Done: Training
-2016-09-06 10:34:15,987 DEBUG: Start: Predicting
-2016-09-06 10:34:15,997 DEBUG: Done: Predicting
-2016-09-06 10:34:15,997 DEBUG: Start: Getting Results
-2016-09-06 10:34:16,000 DEBUG: Done: Getting Results
-2016-09-06 10:34:16,000 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 34
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,000 INFO: Done: Result Analysis
-2016-09-06 10:34:16,100 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:16,100 DEBUG: Start: Training
-2016-09-06 10:34:16,117 DEBUG: Info: Time for Training: 0.182558059692[s]
-2016-09-06 10:34:16,117 DEBUG: Done: Training
-2016-09-06 10:34:16,117 DEBUG: Start: Predicting
-2016-09-06 10:34:16,121 DEBUG: Done: Predicting
-2016-09-06 10:34:16,121 DEBUG: Start: Getting Results
-2016-09-06 10:34:16,123 DEBUG: Done: Getting Results
-2016-09-06 10:34:16,123 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.680952380952
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.680952380952
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,123 INFO: Done: Result Analysis
-2016-09-06 10:34:16,184 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,184 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,185 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:34:16,185 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:34:16,185 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:16,185 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:16,185 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:16,185 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:16,185 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:16,186 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:16,186 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:16,186 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:16,186 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,186 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,231 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:16,232 DEBUG: Start: Training
-2016-09-06 10:34:16,232 DEBUG: Info: Time for Training: 0.0485727787018[s]
-2016-09-06 10:34:16,232 DEBUG: Done: Training
-2016-09-06 10:34:16,233 DEBUG: Start: Predicting
-2016-09-06 10:34:16,239 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:16,239 DEBUG: Start: Training
-2016-09-06 10:34:16,248 DEBUG: Done: Predicting
-2016-09-06 10:34:16,248 DEBUG: Start: Getting Results
-2016-09-06 10:34:16,250 DEBUG: Done: Getting Results
-2016-09-06 10:34:16,250 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,250 INFO: Done: Result Analysis
-2016-09-06 10:34:16,262 DEBUG: Info: Time for Training: 0.0779819488525[s]
-2016-09-06 10:34:16,262 DEBUG: Done: Training
-2016-09-06 10:34:16,262 DEBUG: Start: Predicting
-2016-09-06 10:34:16,265 DEBUG: Done: Predicting
-2016-09-06 10:34:16,266 DEBUG: Start: Getting Results
-2016-09-06 10:34:16,267 DEBUG: Done: Getting Results
-2016-09-06 10:34:16,267 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,267 INFO: Done: Result Analysis
-2016-09-06 10:34:16,341 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,341 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,342 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:34:16,342 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:34:16,342 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:16,342 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:16,343 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:16,343 DEBUG: Info: Shape X_train:(210, 17), Length of y_train:210
-2016-09-06 10:34:16,343 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:16,343 DEBUG: Info: Shape X_test:(90, 17), Length of y_test:90
-2016-09-06 10:34:16,343 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:16,343 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:16,344 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,344 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,409 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:16,410 DEBUG: Start: Training
-2016-09-06 10:34:16,413 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:16,413 DEBUG: Start: Training
-2016-09-06 10:34:16,431 DEBUG: Info: Time for Training: 0.0899150371552[s]
-2016-09-06 10:34:16,431 DEBUG: Done: Training
-2016-09-06 10:34:16,431 DEBUG: Start: Predicting
-2016-09-06 10:34:16,431 DEBUG: Info: Time for Training: 0.0902900695801[s]
-2016-09-06 10:34:16,431 DEBUG: Done: Training
-2016-09-06 10:34:16,431 DEBUG: Start: Predicting
-2016-09-06 10:34:16,435 DEBUG: Done: Predicting
-2016-09-06 10:34:16,436 DEBUG: Start: Getting Results
-2016-09-06 10:34:16,437 DEBUG: Done: Getting Results
-2016-09-06 10:34:16,437 DEBUG: Done: Predicting
-2016-09-06 10:34:16,437 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,437 DEBUG: Start: Getting Results
-2016-09-06 10:34:16,437 INFO: Done: Result Analysis
-2016-09-06 10:34:16,439 DEBUG: Done: Getting Results
-2016-09-06 10:34:16,439 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,439 INFO: Done: Result Analysis
-2016-09-06 10:34:16,585 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,585 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:34:16,586 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:34:16,586 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:16,586 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:16,586 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,586 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,586 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,586 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,586 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:16,586 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:16,587 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,587 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,617 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:16,617 DEBUG: Start: Training
-2016-09-06 10:34:16,618 DEBUG: Info: Time for Training: 0.0332248210907[s]
-2016-09-06 10:34:16,618 DEBUG: Done: Training
-2016-09-06 10:34:16,618 DEBUG: Start: Predicting
-2016-09-06 10:34:16,621 DEBUG: Done: Predicting
-2016-09-06 10:34:16,621 DEBUG: Start: Getting Results
-2016-09-06 10:34:16,622 DEBUG: Done: Getting Results
-2016-09-06 10:34:16,622 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,623 INFO: Done: Result Analysis
-2016-09-06 10:34:16,636 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:16,636 DEBUG: Start: Training
-2016-09-06 10:34:16,640 DEBUG: Info: Time for Training: 0.054888010025[s]
-2016-09-06 10:34:16,640 DEBUG: Done: Training
-2016-09-06 10:34:16,640 DEBUG: Start: Predicting
-2016-09-06 10:34:16,643 DEBUG: Done: Predicting
-2016-09-06 10:34:16,643 DEBUG: Start: Getting Results
-2016-09-06 10:34:16,645 DEBUG: Done: Getting Results
-2016-09-06 10:34:16,645 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,645 INFO: Done: Result Analysis
-2016-09-06 10:34:16,736 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,736 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,736 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:34:16,736 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:34:16,737 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:16,737 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:16,737 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,737 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,737 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,737 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,737 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:16,737 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:16,738 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,738 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,771 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:16,771 DEBUG: Start: Training
-2016-09-06 10:34:16,772 DEBUG: Info: Time for Training: 0.0359029769897[s]
-2016-09-06 10:34:16,772 DEBUG: Done: Training
-2016-09-06 10:34:16,772 DEBUG: Start: Predicting
-2016-09-06 10:34:16,779 DEBUG: Done: Predicting
-2016-09-06 10:34:16,779 DEBUG: Start: Getting Results
-2016-09-06 10:34:16,780 DEBUG: Done: Getting Results
-2016-09-06 10:34:16,780 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 34
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,780 INFO: Done: Result Analysis
-2016-09-06 10:34:16,879 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:16,879 DEBUG: Start: Training
-2016-09-06 10:34:16,896 DEBUG: Info: Time for Training: 0.160787820816[s]
-2016-09-06 10:34:16,897 DEBUG: Done: Training
-2016-09-06 10:34:16,897 DEBUG: Start: Predicting
-2016-09-06 10:34:16,900 DEBUG: Done: Predicting
-2016-09-06 10:34:16,900 DEBUG: Start: Getting Results
-2016-09-06 10:34:16,902 DEBUG: Done: Getting Results
-2016-09-06 10:34:16,902 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.709523809524
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.709523809524
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:34:16,902 INFO: Done: Result Analysis
-2016-09-06 10:34:16,988 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,988 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:34:16,989 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:34:16,989 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:34:16,989 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:16,989 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:34:16,990 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,990 DEBUG: Info: Shape X_train:(210, 10), Length of y_train:210
-2016-09-06 10:34:16,990 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,990 DEBUG: Info: Shape X_test:(90, 10), Length of y_test:90
-2016-09-06 10:34:16,990 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:16,990 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:34:16,990 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:16,990 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:34:17,060 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:17,061 DEBUG: Start: Training
-2016-09-06 10:34:17,062 DEBUG: Info: Time for Training: 0.0743260383606[s]
-2016-09-06 10:34:17,062 DEBUG: Done: Training
-2016-09-06 10:34:17,062 DEBUG: Start: Predicting
-2016-09-06 10:34:17,066 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:34:17,066 DEBUG: Start: Training
-2016-09-06 10:34:17,076 DEBUG: Done: Predicting
-2016-09-06 10:34:17,077 DEBUG: Start: Getting Results
-2016-09-06 10:34:17,080 DEBUG: Done: Getting Results
-2016-09-06 10:34:17,080 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:34:17,081 INFO: Done: Result Analysis
-2016-09-06 10:34:17,094 DEBUG: Info: Time for Training: 0.106731176376[s]
-2016-09-06 10:34:17,094 DEBUG: Done: Training
-2016-09-06 10:34:17,094 DEBUG: Start: Predicting
-2016-09-06 10:34:17,097 DEBUG: Done: Predicting
-2016-09-06 10:34:17,097 DEBUG: Start: Getting Results
-2016-09-06 10:34:17,099 DEBUG: Done: Getting Results
-2016-09-06 10:34:17,099 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:34:17,099 INFO: Done: Result Analysis
-2016-09-06 10:34:17,389 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:34:17,389 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:34:17,390 INFO: Info: Shape of View0 :(300, 9)
-2016-09-06 10:34:17,390 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:34:17,390 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:34:17,390 INFO: Info: Shape of View1 :(300, 15)
-2016-09-06 10:34:17,391 INFO: Info: Shape of View0 :(300, 9)
-2016-09-06 10:34:17,391 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 10:34:17,392 INFO: Info: Shape of View1 :(300, 15)
-2016-09-06 10:34:17,392 INFO: Info: Shape of View3 :(300, 10)
-2016-09-06 10:34:17,392 INFO: Done: Read Database Files
-2016-09-06 10:34:17,392 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:34:17,392 INFO: Info: Shape of View2 :(300, 17)
-2016-09-06 10:34:17,393 INFO: Info: Shape of View3 :(300, 10)
-2016-09-06 10:34:17,393 INFO: Done: Read Database Files
-2016-09-06 10:34:17,393 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:34:17,397 INFO: Done: Determine validation split
-2016-09-06 10:34:17,398 INFO: Start: Determine 5 folds
-2016-09-06 10:34:17,398 INFO: Done: Determine validation split
-2016-09-06 10:34:17,398 INFO: Start: Determine 5 folds
-2016-09-06 10:34:17,407 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:34:17,408 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:34:17,408 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:34:17,408 INFO: Done: Determine folds
-2016-09-06 10:34:17,408 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:34:17,408 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:34:17,408 INFO: Start: Classification
-2016-09-06 10:34:17,408 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:34:17,408 INFO: Start: Fold number 1
-2016-09-06 10:34:17,408 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:34:17,408 INFO: Done: Determine folds
-2016-09-06 10:34:17,408 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:34:17,408 INFO: Start: Classification
-2016-09-06 10:34:17,408 INFO: Start: Fold number 1
-2016-09-06 10:34:17,439 DEBUG: Start: Iteration 1
-2016-09-06 10:34:17,447 DEBUG: View 0 : 0.526627218935
-2016-09-06 10:34:17,455 DEBUG: View 1 : 0.455621301775
-2016-09-06 10:34:17,463 DEBUG: View 2 : 0.473372781065
-2016-09-06 10:34:17,471 DEBUG: View 3 : 0.485207100592
-2016-09-06 10:34:17,504 DEBUG: Best view : View2
-2016-09-06 10:34:17,582 DEBUG: Start: Iteration 2
-2016-09-06 10:34:17,590 DEBUG: View 0 : 0.710059171598
-2016-09-06 10:34:17,598 DEBUG: View 1 : 0.763313609467
-2016-09-06 10:34:17,605 DEBUG: View 2 : 0.775147928994
-2016-09-06 10:34:17,613 DEBUG: View 3 : 0.715976331361
-2016-09-06 10:34:17,650 DEBUG: Best view : View2
-2016-09-06 10:34:17,793 DEBUG: Start: Iteration 3
-2016-09-06 10:34:17,807 DEBUG: View 0 : 0.710059171598
-2016-09-06 10:34:17,815 DEBUG: View 1 : 0.763313609467
-2016-09-06 10:34:17,824 DEBUG: View 2 : 0.775147928994
-2016-09-06 10:34:17,831 DEBUG: View 3 : 0.715976331361
-2016-09-06 10:34:17,875 DEBUG: Best view : View2
-2016-09-06 10:34:18,092 DEBUG: Start: Iteration 4
-2016-09-06 10:34:18,099 DEBUG: View 0 : 0.662721893491
-2016-09-06 10:34:18,107 DEBUG: View 1 : 0.662721893491
-2016-09-06 10:34:18,114 DEBUG: View 2 : 0.633136094675
-2016-09-06 10:34:18,121 DEBUG: View 3 : 0.680473372781
-2016-09-06 10:34:18,162 DEBUG: Best view : View3
-2016-09-06 10:34:18,430 INFO: Start: Classification
-2016-09-06 10:34:18,888 INFO: Done: Fold number 1
-2016-09-06 10:34:18,888 INFO: Start: Fold number 2
-2016-09-06 10:34:18,917 DEBUG: Start: Iteration 1
-2016-09-06 10:34:18,924 DEBUG: View 0 : 0.556213017751
-2016-09-06 10:34:18,931 DEBUG: View 1 : 0.603550295858
-2016-09-06 10:34:18,939 DEBUG: View 2 : 0.497041420118
-2016-09-06 10:34:18,946 DEBUG: View 3 : 0.491124260355
-2016-09-06 10:34:18,976 DEBUG: Best view : View1
-2016-09-06 10:34:19,051 DEBUG: Start: Iteration 2
-2016-09-06 10:34:19,059 DEBUG: View 0 : 0.715976331361
-2016-09-06 10:34:19,066 DEBUG: View 1 : 0.680473372781
-2016-09-06 10:34:19,073 DEBUG: View 2 : 0.710059171598
-2016-09-06 10:34:19,080 DEBUG: View 3 : 0.704142011834
-2016-09-06 10:34:19,116 DEBUG: Best view : View0
-2016-09-06 10:34:19,257 DEBUG: Start: Iteration 3
-2016-09-06 10:34:19,264 DEBUG: View 0 : 0.715976331361
-2016-09-06 10:34:19,272 DEBUG: View 1 : 0.680473372781
-2016-09-06 10:34:19,279 DEBUG: View 2 : 0.710059171598
-2016-09-06 10:34:19,287 DEBUG: View 3 : 0.704142011834
-2016-09-06 10:34:19,325 DEBUG: Best view : View0
-2016-09-06 10:34:19,529 INFO: Start: Classification
-2016-09-06 10:34:19,873 INFO: Done: Fold number 2
-2016-09-06 10:34:19,874 INFO: Start: Fold number 3
-2016-09-06 10:34:19,903 DEBUG: Start: Iteration 1
-2016-09-06 10:34:19,909 DEBUG: View 0 : 0.544378698225
-2016-09-06 10:34:19,916 DEBUG: View 1 : 0.544378698225
-2016-09-06 10:34:19,923 DEBUG: View 2 : 0.544378698225
-2016-09-06 10:34:19,930 DEBUG: View 3 : 0.544378698225
-2016-09-06 10:34:19,961 DEBUG: Best view : View0
-2016-09-06 10:34:20,036 DEBUG: Start: Iteration 2
-2016-09-06 10:34:20,043 DEBUG: View 0 : 0.692307692308
-2016-09-06 10:34:20,051 DEBUG: View 1 : 0.692307692308
-2016-09-06 10:34:20,058 DEBUG: View 2 : 0.769230769231
-2016-09-06 10:34:20,065 DEBUG: View 3 : 0.733727810651
-2016-09-06 10:34:20,101 DEBUG: Best view : View2
-2016-09-06 10:34:20,241 DEBUG: Start: Iteration 3
-2016-09-06 10:34:20,249 DEBUG: View 0 : 0.692307692308
-2016-09-06 10:34:20,256 DEBUG: View 1 : 0.692307692308
-2016-09-06 10:34:20,264 DEBUG: View 2 : 0.769230769231
-2016-09-06 10:34:20,271 DEBUG: View 3 : 0.733727810651
-2016-09-06 10:34:20,310 DEBUG: Best view : View2
-2016-09-06 10:34:20,513 INFO: Start: Classification
-2016-09-06 10:34:20,853 INFO: Done: Fold number 3
-2016-09-06 10:34:20,853 INFO: Start: Fold number 4
-2016-09-06 10:34:20,882 DEBUG: Start: Iteration 1
-2016-09-06 10:34:20,889 DEBUG: View 0 : 0.585798816568
-2016-09-06 10:34:20,896 DEBUG: View 1 : 0.502958579882
-2016-09-06 10:34:20,904 DEBUG: View 2 : 0.538461538462
-2016-09-06 10:34:20,911 DEBUG: View 3 : 0.544378698225
-2016-09-06 10:34:20,940 DEBUG: Best view : View3
-2016-09-06 10:34:21,016 DEBUG: Start: Iteration 2
-2016-09-06 10:34:21,023 DEBUG: View 0 : 0.692307692308
-2016-09-06 10:34:21,031 DEBUG: View 1 : 0.704142011834
-2016-09-06 10:34:21,038 DEBUG: View 2 : 0.650887573964
-2016-09-06 10:34:21,045 DEBUG: View 3 : 0.698224852071
-2016-09-06 10:34:21,081 DEBUG: Best view : View1
-2016-09-06 10:34:21,221 DEBUG: Start: Iteration 3
-2016-09-06 10:34:21,228 DEBUG: View 0 : 0.692307692308
-2016-09-06 10:34:21,235 DEBUG: View 1 : 0.704142011834
-2016-09-06 10:34:21,243 DEBUG: View 2 : 0.650887573964
-2016-09-06 10:34:21,250 DEBUG: View 3 : 0.698224852071
-2016-09-06 10:34:21,289 DEBUG: Best view : View1
-2016-09-06 10:34:21,493 INFO: Start: Classification
-2016-09-06 10:34:21,834 INFO: Done: Fold number 4
-2016-09-06 10:34:21,834 INFO: Start: Fold number 5
-2016-09-06 10:34:21,864 DEBUG: Start: Iteration 1
-2016-09-06 10:34:21,871 DEBUG: View 0 : 0.455621301775
-2016-09-06 10:34:21,877 DEBUG: View 1 : 0.455621301775
-2016-09-06 10:34:21,884 DEBUG: View 2 : 0.455621301775
-2016-09-06 10:34:21,891 DEBUG: View 3 : 0.455621301775
-2016-09-06 10:34:21,891 WARNING: WARNING: All bad for iteration 0
-2016-09-06 10:34:21,920 DEBUG: Best view : View0
-2016-09-06 10:34:21,995 DEBUG: Start: Iteration 2
-2016-09-06 10:34:22,002 DEBUG: View 0 : 0.680473372781
-2016-09-06 10:34:22,010 DEBUG: View 1 : 0.733727810651
-2016-09-06 10:34:22,017 DEBUG: View 2 : 0.745562130178
-2016-09-06 10:34:22,024 DEBUG: View 3 : 0.710059171598
-2016-09-06 10:34:22,060 DEBUG: Best view : View2
-2016-09-06 10:34:22,200 DEBUG: Start: Iteration 3
-2016-09-06 10:34:22,206 DEBUG: View 0 : 0.680473372781
-2016-09-06 10:34:22,214 DEBUG: View 1 : 0.733727810651
-2016-09-06 10:34:22,221 DEBUG: View 2 : 0.745562130178
-2016-09-06 10:34:22,228 DEBUG: View 3 : 0.710059171598
-2016-09-06 10:34:22,267 DEBUG: Best view : View2
-2016-09-06 10:34:22,470 INFO: Start: Classification
-2016-09-06 10:34:22,812 INFO: Done: Fold number 5
-2016-09-06 10:34:22,812 INFO: Done: Classification
-2016-09-06 10:34:22,812 INFO: Info: Time for Classification: 5[s]
-2016-09-06 10:34:22,812 INFO: Start: Result Analysis for Mumbo
-2016-09-06 10:34:24,767 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 74.201183432
- -On Test : 49.5238095238
- -On Validation : 52.808988764Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 9), View1 of shape (300, 15), View2 of shape (300, 17), View3 of shape (300, 10)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:04 0:00:00
- Fold 5 0:00:05 0:00:00
- Total 0:00:15 0:00:01
- So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.260946745562
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.26449704142
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.265680473373
- - Percentage of time chosen : 0.3
- - On View3 :
- - Mean average Accuracy : 0.259763313609
- - Percentage of time chosen : 0.1
- - Fold 1, used 4
- - On View0 :
- - Mean average Accuracy : 0.198816568047
- - Percentage of time chosen : 0.9
- - On View1 :
- - Mean average Accuracy : 0.196449704142
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.191715976331
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.189940828402
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.192899408284
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.192899408284
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.208284023669
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.201183431953
- - Percentage of time chosen : 0.0
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.197041420118
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.191124260355
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.184023668639
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.194082840237
- - Percentage of time chosen : 0.1
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.181656804734
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.192307692308
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.194674556213
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.187573964497
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View2
- Fold 2
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View1
- Fold 3
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 4
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View3
- Fold 5
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 77.5147928994
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View2
- Fold 2
- Accuracy on train : 71.5976331361
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 3
- Accuracy on train : 76.9230769231
- Accuracy on test : 0.0
- Accuracy on validation : 52.808988764
- Selected View : View2
- Fold 4
- Accuracy on train : 70.4142011834
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View1
- Fold 5
- Accuracy on train : 74.5562130178
- Accuracy on test : 0.0
- Accuracy on validation : 46.0674157303
- Selected View : View2
- - Iteration 3
- Fold 1
- Accuracy on train : 77.5147928994
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View2
- Fold 2
- Accuracy on train : 71.5976331361
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 3
- Accuracy on train : 76.9230769231
- Accuracy on test : 0.0
- Accuracy on validation : 52.808988764
- Selected View : View2
- Fold 4
- Accuracy on train : 70.4142011834
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View1
- Fold 5
- Accuracy on train : 74.5562130178
- Accuracy on test : 0.0
- Accuracy on validation : 46.0674157303
- Selected View : View2
- - Iteration 4
- Fold 1
- Accuracy on train : 77.5147928994
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View3
- Fold 2
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 3
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 4
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 5
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- - Iteration 5
- Fold 1
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
-2016-09-06 10:34:24,951 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b21ed4f89b12e4e6cc230bb6c2c11cce8c59bafb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 77474e0f2cfa4d49e3cc6bdcb34c985e935a8331..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 09eb9bd997ef68d2394c6169df6157316cf189c6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 34
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75aeeab8d5f66df7d6c23af6c3a6e17eb066910e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index de64a87eba45859e1bba6de874ddcd46cef0b48a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8accfa21b0b077c4478095c473f44ae09a2de3e3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.471428571429
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8d98bcf93125e7cc7c715dd8f8a84c79024e2387..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ebca06a942aecfa1f3d5ffb4934bf508a10271f0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103414Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 9)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ca874bc8bb3d2e4a6c3aa970a37b2e5bfdeab058..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.611111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.611111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 33f3d2e5301edd079b28096300c0f3454e5a744f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ef3258f892730096ac37a2e27b2b2ae30580dbb6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 34
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 42abfcbf91a82ab1deec96781e4c053620e3f3bb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.685714285714
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.685714285714
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 098bc8e60e42866c1bd874028d7e84f3fbc51829..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.633333333333
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.633333333333
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f8a69440f753bd0b9135db3c7090bee2fec84e90..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.5
-accuracy_score on test : 0.388888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.388888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 85ebfdd4167460ea1e22921a464245bba62cbf57..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d4cd5d9a640932eb06164cd69e789bba45a1c367..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103415Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e41e726a1e154dfcce7ac5a2ca77060ce501d3c1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3c3a4f96943d487d993e2ec45692351be9998f68..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9bf78e66e84b4bfff3fc3f5edf7fe5be6f0510d1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 34
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0b0dc718c52da05c632439a0aa6ecccd1923b079..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.709523809524
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.709523809524
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fe4a10e8c09a7d242953e9b36f98a6fcb347fc04..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index eaf4be419957663e1b48d32421a7020c8e185219..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7a8b79bfd3a2c1f4a7531f302cacb9a873abffc8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b1b7d9bc14b194df6ffcfcd0d29cd627320eadbe..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103416Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 17)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index dc3c519a66862291779f5923512c88856c638c5b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6bbe21818a569ca1bebb20ba2433686351c54053..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103417Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 10)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2978
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index 6055a9f380f8d3c63daeba5147253811e3b5075a..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index f514bd751d264316a0deeeba3c4b770eef1bbee8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103424Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,215 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 74.201183432
- -On Test : 49.5238095238
- -On Validation : 52.808988764Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 9), View1 of shape (300, 15), View2 of shape (300, 17), View3 of shape (300, 10)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:01 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:04 0:00:00
- Fold 5 0:00:05 0:00:00
- Total 0:00:15 0:00:01
- So a total classification time of 0:00:05.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 5
- - On View0 :
- - Mean average Accuracy : 0.260946745562
- - Percentage of time chosen : 0.6
- - On View1 :
- - Mean average Accuracy : 0.26449704142
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.265680473373
- - Percentage of time chosen : 0.3
- - On View3 :
- - Mean average Accuracy : 0.259763313609
- - Percentage of time chosen : 0.1
- - Fold 1, used 4
- - On View0 :
- - Mean average Accuracy : 0.198816568047
- - Percentage of time chosen : 0.9
- - On View1 :
- - Mean average Accuracy : 0.196449704142
- - Percentage of time chosen : 0.1
- - On View2 :
- - Mean average Accuracy : 0.191715976331
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.189940828402
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.192899408284
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.192899408284
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.208284023669
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.201183431953
- - Percentage of time chosen : 0.0
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.197041420118
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.191124260355
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.184023668639
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.194082840237
- - Percentage of time chosen : 0.1
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.181656804734
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.192307692308
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.194674556213
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.187573964497
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View2
- Fold 2
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View1
- Fold 3
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 4
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View3
- Fold 5
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 77.5147928994
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View2
- Fold 2
- Accuracy on train : 71.5976331361
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 3
- Accuracy on train : 76.9230769231
- Accuracy on test : 0.0
- Accuracy on validation : 52.808988764
- Selected View : View2
- Fold 4
- Accuracy on train : 70.4142011834
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View1
- Fold 5
- Accuracy on train : 74.5562130178
- Accuracy on test : 0.0
- Accuracy on validation : 46.0674157303
- Selected View : View2
- - Iteration 3
- Fold 1
- Accuracy on train : 77.5147928994
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View2
- Fold 2
- Accuracy on train : 71.5976331361
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 3
- Accuracy on train : 76.9230769231
- Accuracy on test : 0.0
- Accuracy on validation : 52.808988764
- Selected View : View2
- Fold 4
- Accuracy on train : 70.4142011834
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View1
- Fold 5
- Accuracy on train : 74.5562130178
- Accuracy on test : 0.0
- Accuracy on validation : 46.0674157303
- Selected View : View2
- - Iteration 4
- Fold 1
- Accuracy on train : 77.5147928994
- Accuracy on test : 0.0
- Accuracy on validation : 50.5617977528
- Selected View : View3
- Fold 2
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 3
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 4
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- Fold 5
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
- - Iteration 5
- Fold 1
- Accuracy on train : 54.4378698225
- Accuracy on test : 0.0
- Accuracy on validation : 53.9325842697
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index e52630384505f65735f8d8a6002f04345a5e7525..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103742-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1570 +0,0 @@
-2016-09-06 10:37:42,137 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:37:42,137 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00010290625 Gbytes /!\
-2016-09-06 10:37:47,151 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:37:47,154 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:37:47,209 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,210 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:37:47,210 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:47,211 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,211 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,211 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,211 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:47,211 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:37:47,211 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,211 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:47,212 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,213 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,213 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:47,213 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,247 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:47,247 DEBUG: Start: Training
-2016-09-06 10:37:47,248 DEBUG: Info: Time for Training: 0.0386531352997[s]
-2016-09-06 10:37:47,248 DEBUG: Done: Training
-2016-09-06 10:37:47,248 DEBUG: Start: Predicting
-2016-09-06 10:37:47,251 DEBUG: Done: Predicting
-2016-09-06 10:37:47,251 DEBUG: Start: Getting Results
-2016-09-06 10:37:47,252 DEBUG: Done: Getting Results
-2016-09-06 10:37:47,252 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.633333333333
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.633333333333
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,253 INFO: Done: Result Analysis
-2016-09-06 10:37:47,259 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:47,259 DEBUG: Start: Training
-2016-09-06 10:37:47,262 DEBUG: Info: Time for Training: 0.0534279346466[s]
-2016-09-06 10:37:47,262 DEBUG: Done: Training
-2016-09-06 10:37:47,262 DEBUG: Start: Predicting
-2016-09-06 10:37:47,265 DEBUG: Done: Predicting
-2016-09-06 10:37:47,265 DEBUG: Start: Getting Results
-2016-09-06 10:37:47,267 DEBUG: Done: Getting Results
-2016-09-06 10:37:47,267 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,267 INFO: Done: Result Analysis
-2016-09-06 10:37:47,354 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,354 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,354 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:37:47,354 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:37:47,354 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:47,354 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:47,355 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,355 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,355 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,355 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,355 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:47,355 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:47,355 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,355 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,386 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:47,386 DEBUG: Start: Training
-2016-09-06 10:37:47,387 DEBUG: Info: Time for Training: 0.0330820083618[s]
-2016-09-06 10:37:47,387 DEBUG: Done: Training
-2016-09-06 10:37:47,387 DEBUG: Start: Predicting
-2016-09-06 10:37:47,391 DEBUG: Done: Predicting
-2016-09-06 10:37:47,391 DEBUG: Start: Getting Results
-2016-09-06 10:37:47,392 DEBUG: Done: Getting Results
-2016-09-06 10:37:47,392 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.67619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,392 INFO: Done: Result Analysis
-2016-09-06 10:37:47,513 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:47,513 DEBUG: Start: Training
-2016-09-06 10:37:47,534 DEBUG: Info: Time for Training: 0.180033922195[s]
-2016-09-06 10:37:47,534 DEBUG: Done: Training
-2016-09-06 10:37:47,534 DEBUG: Start: Predicting
-2016-09-06 10:37:47,538 DEBUG: Done: Predicting
-2016-09-06 10:37:47,538 DEBUG: Start: Getting Results
-2016-09-06 10:37:47,539 DEBUG: Done: Getting Results
-2016-09-06 10:37:47,539 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.766666666667
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.766666666667
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,540 INFO: Done: Result Analysis
-2016-09-06 10:37:47,604 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,604 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,604 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:37:47,604 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:37:47,604 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:47,604 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:47,605 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,605 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,605 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,605 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,605 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:47,605 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:47,606 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,606 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,651 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:47,651 DEBUG: Start: Training
-2016-09-06 10:37:47,652 DEBUG: Info: Time for Training: 0.0492498874664[s]
-2016-09-06 10:37:47,652 DEBUG: Done: Training
-2016-09-06 10:37:47,652 DEBUG: Start: Predicting
-2016-09-06 10:37:47,662 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:47,662 DEBUG: Start: Training
-2016-09-06 10:37:47,663 DEBUG: Done: Predicting
-2016-09-06 10:37:47,664 DEBUG: Start: Getting Results
-2016-09-06 10:37:47,665 DEBUG: Done: Getting Results
-2016-09-06 10:37:47,666 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,666 INFO: Done: Result Analysis
-2016-09-06 10:37:47,682 DEBUG: Info: Time for Training: 0.0788521766663[s]
-2016-09-06 10:37:47,682 DEBUG: Done: Training
-2016-09-06 10:37:47,682 DEBUG: Start: Predicting
-2016-09-06 10:37:47,685 DEBUG: Done: Predicting
-2016-09-06 10:37:47,685 DEBUG: Start: Getting Results
-2016-09-06 10:37:47,686 DEBUG: Done: Getting Results
-2016-09-06 10:37:47,686 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,687 INFO: Done: Result Analysis
-2016-09-06 10:37:47,750 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,750 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:37:47,750 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:47,751 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,751 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,751 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,751 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:37:47,751 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:47,751 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:47,751 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,752 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:47,752 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:47,752 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:47,752 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,803 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:47,803 DEBUG: Start: Training
-2016-09-06 10:37:47,807 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:47,807 DEBUG: Start: Training
-2016-09-06 10:37:47,822 DEBUG: Info: Time for Training: 0.0717940330505[s]
-2016-09-06 10:37:47,823 DEBUG: Done: Training
-2016-09-06 10:37:47,823 DEBUG: Start: Predicting
-2016-09-06 10:37:47,827 DEBUG: Info: Time for Training: 0.07728099823[s]
-2016-09-06 10:37:47,827 DEBUG: Done: Training
-2016-09-06 10:37:47,827 DEBUG: Start: Predicting
-2016-09-06 10:37:47,828 DEBUG: Done: Predicting
-2016-09-06 10:37:47,828 DEBUG: Start: Getting Results
-2016-09-06 10:37:47,829 DEBUG: Done: Getting Results
-2016-09-06 10:37:47,829 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,830 INFO: Done: Result Analysis
-2016-09-06 10:37:47,831 DEBUG: Done: Predicting
-2016-09-06 10:37:47,831 DEBUG: Start: Getting Results
-2016-09-06 10:37:47,833 DEBUG: Done: Getting Results
-2016-09-06 10:37:47,833 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.366666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.366666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,833 INFO: Done: Result Analysis
-2016-09-06 10:37:47,901 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,901 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:47,902 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:37:47,902 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:37:47,902 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:47,902 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:47,903 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:47,903 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:47,903 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:47,903 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:47,903 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:47,903 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:47,903 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,903 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:47,948 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:47,948 DEBUG: Start: Training
-2016-09-06 10:37:47,949 DEBUG: Info: Time for Training: 0.0490880012512[s]
-2016-09-06 10:37:47,950 DEBUG: Done: Training
-2016-09-06 10:37:47,950 DEBUG: Start: Predicting
-2016-09-06 10:37:47,953 DEBUG: Done: Predicting
-2016-09-06 10:37:47,953 DEBUG: Start: Getting Results
-2016-09-06 10:37:47,955 DEBUG: Done: Getting Results
-2016-09-06 10:37:47,955 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.738095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,956 INFO: Done: Result Analysis
-2016-09-06 10:37:47,964 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:47,964 DEBUG: Start: Training
-2016-09-06 10:37:47,968 DEBUG: Info: Time for Training: 0.067459821701[s]
-2016-09-06 10:37:47,968 DEBUG: Done: Training
-2016-09-06 10:37:47,968 DEBUG: Start: Predicting
-2016-09-06 10:37:47,971 DEBUG: Done: Predicting
-2016-09-06 10:37:47,971 DEBUG: Start: Getting Results
-2016-09-06 10:37:47,973 DEBUG: Done: Getting Results
-2016-09-06 10:37:47,973 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:37:47,973 INFO: Done: Result Analysis
-2016-09-06 10:37:48,050 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,050 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,050 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:37:48,050 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:37:48,050 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:48,050 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:48,051 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,051 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,051 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,051 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,051 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:48,051 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:48,051 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,051 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,081 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:48,081 DEBUG: Start: Training
-2016-09-06 10:37:48,082 DEBUG: Info: Time for Training: 0.0320069789886[s]
-2016-09-06 10:37:48,082 DEBUG: Done: Training
-2016-09-06 10:37:48,082 DEBUG: Start: Predicting
-2016-09-06 10:37:48,087 DEBUG: Done: Predicting
-2016-09-06 10:37:48,087 DEBUG: Start: Getting Results
-2016-09-06 10:37:48,088 DEBUG: Done: Getting Results
-2016-09-06 10:37:48,088 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.657142857143
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.657142857143
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,088 INFO: Done: Result Analysis
-2016-09-06 10:37:48,209 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:48,209 DEBUG: Start: Training
-2016-09-06 10:37:48,230 DEBUG: Info: Time for Training: 0.180283069611[s]
-2016-09-06 10:37:48,230 DEBUG: Done: Training
-2016-09-06 10:37:48,230 DEBUG: Start: Predicting
-2016-09-06 10:37:48,234 DEBUG: Done: Predicting
-2016-09-06 10:37:48,234 DEBUG: Start: Getting Results
-2016-09-06 10:37:48,236 DEBUG: Done: Getting Results
-2016-09-06 10:37:48,236 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.833333333333
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.833333333333
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,236 INFO: Done: Result Analysis
-2016-09-06 10:37:48,304 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,304 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,305 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:37:48,305 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:37:48,305 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:48,305 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:48,306 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,306 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,307 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,307 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,307 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:48,307 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:48,307 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,307 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,363 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:48,363 DEBUG: Start: Training
-2016-09-06 10:37:48,364 DEBUG: Info: Time for Training: 0.0610771179199[s]
-2016-09-06 10:37:48,364 DEBUG: Done: Training
-2016-09-06 10:37:48,364 DEBUG: Start: Predicting
-2016-09-06 10:37:48,371 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:48,371 DEBUG: Start: Training
-2016-09-06 10:37:48,389 DEBUG: Done: Predicting
-2016-09-06 10:37:48,390 DEBUG: Start: Getting Results
-2016-09-06 10:37:48,391 DEBUG: Done: Getting Results
-2016-09-06 10:37:48,391 DEBUG: Info: Time for Training: 0.088534116745[s]
-2016-09-06 10:37:48,391 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,391 DEBUG: Done: Training
-2016-09-06 10:37:48,392 DEBUG: Start: Predicting
-2016-09-06 10:37:48,392 INFO: Done: Result Analysis
-2016-09-06 10:37:48,395 DEBUG: Done: Predicting
-2016-09-06 10:37:48,395 DEBUG: Start: Getting Results
-2016-09-06 10:37:48,397 DEBUG: Done: Getting Results
-2016-09-06 10:37:48,397 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,397 INFO: Done: Result Analysis
-2016-09-06 10:37:48,548 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,548 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:37:48,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:37:48,548 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:48,548 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:48,549 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,549 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-06 10:37:48,549 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,549 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-06 10:37:48,549 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:48,549 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:48,550 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,550 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,600 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:48,600 DEBUG: Start: Training
-2016-09-06 10:37:48,603 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:48,603 DEBUG: Start: Training
-2016-09-06 10:37:48,612 DEBUG: Info: Time for Training: 0.0646460056305[s]
-2016-09-06 10:37:48,612 DEBUG: Done: Training
-2016-09-06 10:37:48,612 DEBUG: Start: Predicting
-2016-09-06 10:37:48,616 DEBUG: Done: Predicting
-2016-09-06 10:37:48,616 DEBUG: Start: Getting Results
-2016-09-06 10:37:48,617 DEBUG: Done: Getting Results
-2016-09-06 10:37:48,617 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,618 INFO: Done: Result Analysis
-2016-09-06 10:37:48,622 DEBUG: Info: Time for Training: 0.0746550559998[s]
-2016-09-06 10:37:48,622 DEBUG: Done: Training
-2016-09-06 10:37:48,622 DEBUG: Start: Predicting
-2016-09-06 10:37:48,628 DEBUG: Done: Predicting
-2016-09-06 10:37:48,628 DEBUG: Start: Getting Results
-2016-09-06 10:37:48,629 DEBUG: Done: Getting Results
-2016-09-06 10:37:48,629 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,629 INFO: Done: Result Analysis
-2016-09-06 10:37:48,696 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,696 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:37:48,696 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:48,696 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,696 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:37:48,697 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:48,697 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:48,697 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:48,697 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:48,697 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:48,697 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,698 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:48,698 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:48,698 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,734 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:48,735 DEBUG: Start: Training
-2016-09-06 10:37:48,736 DEBUG: Info: Time for Training: 0.0404579639435[s]
-2016-09-06 10:37:48,736 DEBUG: Done: Training
-2016-09-06 10:37:48,736 DEBUG: Start: Predicting
-2016-09-06 10:37:48,739 DEBUG: Done: Predicting
-2016-09-06 10:37:48,739 DEBUG: Start: Getting Results
-2016-09-06 10:37:48,740 DEBUG: Done: Getting Results
-2016-09-06 10:37:48,741 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.690476190476
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.690476190476
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,741 INFO: Done: Result Analysis
-2016-09-06 10:37:48,750 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:48,750 DEBUG: Start: Training
-2016-09-06 10:37:48,754 DEBUG: Info: Time for Training: 0.059161901474[s]
-2016-09-06 10:37:48,754 DEBUG: Done: Training
-2016-09-06 10:37:48,754 DEBUG: Start: Predicting
-2016-09-06 10:37:48,757 DEBUG: Done: Predicting
-2016-09-06 10:37:48,757 DEBUG: Start: Getting Results
-2016-09-06 10:37:48,759 DEBUG: Done: Getting Results
-2016-09-06 10:37:48,759 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,760 INFO: Done: Result Analysis
-2016-09-06 10:37:48,847 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,847 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:37:48,847 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:48,848 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:48,848 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:48,848 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:48,848 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,848 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:48,848 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:37:48,849 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:48,849 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:48,849 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:48,849 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:48,850 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:48,879 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:48,879 DEBUG: Start: Training
-2016-09-06 10:37:48,880 DEBUG: Info: Time for Training: 0.0334219932556[s]
-2016-09-06 10:37:48,880 DEBUG: Done: Training
-2016-09-06 10:37:48,880 DEBUG: Start: Predicting
-2016-09-06 10:37:48,885 DEBUG: Done: Predicting
-2016-09-06 10:37:48,885 DEBUG: Start: Getting Results
-2016-09-06 10:37:48,887 DEBUG: Done: Getting Results
-2016-09-06 10:37:48,887 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.690476190476
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.690476190476
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 10:37:48,887 INFO: Done: Result Analysis
-2016-09-06 10:37:49,024 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:49,024 DEBUG: Start: Training
-2016-09-06 10:37:49,046 DEBUG: Info: Time for Training: 0.198237895966[s]
-2016-09-06 10:37:49,046 DEBUG: Done: Training
-2016-09-06 10:37:49,046 DEBUG: Start: Predicting
-2016-09-06 10:37:49,050 DEBUG: Done: Predicting
-2016-09-06 10:37:49,051 DEBUG: Start: Getting Results
-2016-09-06 10:37:49,053 DEBUG: Done: Getting Results
-2016-09-06 10:37:49,053 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.809523809524
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.809523809524
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,053 INFO: Done: Result Analysis
-2016-09-06 10:37:49,200 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,200 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,200 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:37:49,200 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:37:49,200 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:49,200 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:49,201 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:49,201 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:49,201 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:49,201 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:49,202 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:49,202 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:49,202 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,202 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,272 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:49,273 DEBUG: Start: Training
-2016-09-06 10:37:49,274 DEBUG: Info: Time for Training: 0.0749552249908[s]
-2016-09-06 10:37:49,274 DEBUG: Done: Training
-2016-09-06 10:37:49,274 DEBUG: Start: Predicting
-2016-09-06 10:37:49,279 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:49,279 DEBUG: Start: Training
-2016-09-06 10:37:49,293 DEBUG: Done: Predicting
-2016-09-06 10:37:49,293 DEBUG: Start: Getting Results
-2016-09-06 10:37:49,295 DEBUG: Done: Getting Results
-2016-09-06 10:37:49,295 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,295 INFO: Done: Result Analysis
-2016-09-06 10:37:49,299 DEBUG: Info: Time for Training: 0.0997180938721[s]
-2016-09-06 10:37:49,299 DEBUG: Done: Training
-2016-09-06 10:37:49,299 DEBUG: Start: Predicting
-2016-09-06 10:37:49,302 DEBUG: Done: Predicting
-2016-09-06 10:37:49,302 DEBUG: Start: Getting Results
-2016-09-06 10:37:49,304 DEBUG: Done: Getting Results
-2016-09-06 10:37:49,304 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,304 INFO: Done: Result Analysis
-2016-09-06 10:37:49,450 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,450 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,450 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:37:49,450 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:37:49,450 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:49,450 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:49,451 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:49,451 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:37:49,451 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:49,451 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:37:49,452 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:49,452 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:49,452 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,452 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,501 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:49,501 DEBUG: Start: Training
-2016-09-06 10:37:49,501 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:49,502 DEBUG: Start: Training
-2016-09-06 10:37:49,515 DEBUG: Info: Time for Training: 0.0661950111389[s]
-2016-09-06 10:37:49,516 DEBUG: Done: Training
-2016-09-06 10:37:49,516 DEBUG: Start: Predicting
-2016-09-06 10:37:49,518 DEBUG: Info: Time for Training: 0.0691938400269[s]
-2016-09-06 10:37:49,519 DEBUG: Done: Training
-2016-09-06 10:37:49,519 DEBUG: Start: Predicting
-2016-09-06 10:37:49,520 DEBUG: Done: Predicting
-2016-09-06 10:37:49,520 DEBUG: Start: Getting Results
-2016-09-06 10:37:49,522 DEBUG: Done: Getting Results
-2016-09-06 10:37:49,522 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,522 INFO: Done: Result Analysis
-2016-09-06 10:37:49,525 DEBUG: Done: Predicting
-2016-09-06 10:37:49,525 DEBUG: Start: Getting Results
-2016-09-06 10:37:49,526 DEBUG: Done: Getting Results
-2016-09-06 10:37:49,526 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,526 INFO: Done: Result Analysis
-2016-09-06 10:37:49,597 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,598 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:37:49,598 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:49,598 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,598 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,598 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:37:49,598 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:49,598 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,599 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:49,599 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,599 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,599 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,599 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:49,599 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,629 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:49,629 DEBUG: Start: Training
-2016-09-06 10:37:49,629 DEBUG: Info: Time for Training: 0.0316710472107[s]
-2016-09-06 10:37:49,629 DEBUG: Done: Training
-2016-09-06 10:37:49,630 DEBUG: Start: Predicting
-2016-09-06 10:37:49,632 DEBUG: Done: Predicting
-2016-09-06 10:37:49,632 DEBUG: Start: Getting Results
-2016-09-06 10:37:49,634 DEBUG: Done: Getting Results
-2016-09-06 10:37:49,634 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.742857142857
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.742857142857
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,634 INFO: Done: Result Analysis
-2016-09-06 10:37:49,645 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:49,645 DEBUG: Start: Training
-2016-09-06 10:37:49,648 DEBUG: Info: Time for Training: 0.0510141849518[s]
-2016-09-06 10:37:49,648 DEBUG: Done: Training
-2016-09-06 10:37:49,648 DEBUG: Start: Predicting
-2016-09-06 10:37:49,651 DEBUG: Done: Predicting
-2016-09-06 10:37:49,651 DEBUG: Start: Getting Results
-2016-09-06 10:37:49,653 DEBUG: Done: Getting Results
-2016-09-06 10:37:49,653 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,653 INFO: Done: Result Analysis
-2016-09-06 10:37:49,750 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,750 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,751 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:37:49,751 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:37:49,751 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:49,751 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:49,751 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,751 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,752 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,752 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,752 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:49,752 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:49,752 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,752 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,784 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:49,784 DEBUG: Start: Training
-2016-09-06 10:37:49,785 DEBUG: Info: Time for Training: 0.0350430011749[s]
-2016-09-06 10:37:49,785 DEBUG: Done: Training
-2016-09-06 10:37:49,785 DEBUG: Start: Predicting
-2016-09-06 10:37:49,789 DEBUG: Done: Predicting
-2016-09-06 10:37:49,789 DEBUG: Start: Getting Results
-2016-09-06 10:37:49,790 DEBUG: Done: Getting Results
-2016-09-06 10:37:49,791 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.709523809524
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.709523809524
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,791 INFO: Done: Result Analysis
-2016-09-06 10:37:49,912 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:49,912 DEBUG: Start: Training
-2016-09-06 10:37:49,932 DEBUG: Info: Time for Training: 0.182338953018[s]
-2016-09-06 10:37:49,932 DEBUG: Done: Training
-2016-09-06 10:37:49,932 DEBUG: Start: Predicting
-2016-09-06 10:37:49,936 DEBUG: Done: Predicting
-2016-09-06 10:37:49,936 DEBUG: Start: Getting Results
-2016-09-06 10:37:49,938 DEBUG: Done: Getting Results
-2016-09-06 10:37:49,938 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.8
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.8
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:37:49,938 INFO: Done: Result Analysis
-2016-09-06 10:37:49,997 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,997 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:37:49,998 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:37:49,998 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:37:49,998 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:49,998 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:37:49,999 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,999 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 10:37:49,999 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,999 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 10:37:49,999 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:49,999 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:37:49,999 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:49,999 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:37:50,048 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:50,048 DEBUG: Start: Training
-2016-09-06 10:37:50,053 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:37:50,053 DEBUG: Start: Training
-2016-09-06 10:37:50,053 DEBUG: Info: Time for Training: 0.056587934494[s]
-2016-09-06 10:37:50,054 DEBUG: Done: Training
-2016-09-06 10:37:50,054 DEBUG: Start: Predicting
-2016-09-06 10:37:50,064 DEBUG: Info: Time for Training: 0.0674622058868[s]
-2016-09-06 10:37:50,064 DEBUG: Done: Training
-2016-09-06 10:37:50,065 DEBUG: Start: Predicting
-2016-09-06 10:37:50,068 DEBUG: Done: Predicting
-2016-09-06 10:37:50,068 DEBUG: Start: Getting Results
-2016-09-06 10:37:50,069 DEBUG: Done: Getting Results
-2016-09-06 10:37:50,069 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:37:50,069 INFO: Done: Result Analysis
-2016-09-06 10:37:50,075 DEBUG: Done: Predicting
-2016-09-06 10:37:50,076 DEBUG: Start: Getting Results
-2016-09-06 10:37:50,079 DEBUG: Done: Getting Results
-2016-09-06 10:37:50,079 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:37:50,079 INFO: Done: Result Analysis
-2016-09-06 10:37:50,290 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:37:50,290 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:37:50,291 INFO: Info: Shape of View0 :(300, 5)
-2016-09-06 10:37:50,292 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 10:37:50,292 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:37:50,292 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 10:37:50,292 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:37:50,293 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 10:37:50,293 INFO: Done: Read Database Files
-2016-09-06 10:37:50,293 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:37:50,294 INFO: Info: Shape of View0 :(300, 5)
-2016-09-06 10:37:50,295 INFO: Info: Shape of View1 :(300, 12)
-2016-09-06 10:37:50,295 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 10:37:50,296 INFO: Info: Shape of View3 :(300, 5)
-2016-09-06 10:37:50,296 INFO: Done: Read Database Files
-2016-09-06 10:37:50,296 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:37:50,299 INFO: Done: Determine validation split
-2016-09-06 10:37:50,299 INFO: Start: Determine 5 folds
-2016-09-06 10:37:50,301 INFO: Done: Determine validation split
-2016-09-06 10:37:50,301 INFO: Start: Determine 5 folds
-2016-09-06 10:37:50,308 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:37:50,308 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:37:50,308 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:37:50,308 INFO: Done: Determine folds
-2016-09-06 10:37:50,308 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:37:50,308 INFO: Start: Classification
-2016-09-06 10:37:50,309 INFO: Start: Fold number 1
-2016-09-06 10:37:50,310 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:37:50,310 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:37:50,310 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:37:50,310 INFO: Done: Determine folds
-2016-09-06 10:37:50,310 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:37:50,310 INFO: Start: Classification
-2016-09-06 10:37:50,310 INFO: Start: Fold number 1
-2016-09-06 10:37:50,340 DEBUG: Start: Iteration 1
-2016-09-06 10:37:50,348 DEBUG: View 0 : 0.431952662722
-2016-09-06 10:37:50,355 DEBUG: View 1 : 0.431952662722
-2016-09-06 10:37:50,363 DEBUG: View 2 : 0.431952662722
-2016-09-06 10:37:50,370 DEBUG: View 3 : 0.431952662722
-2016-09-06 10:37:50,371 WARNING: WARNING: All bad for iteration 0
-2016-09-06 10:37:50,403 DEBUG: Best view : View0
-2016-09-06 10:37:50,482 DEBUG: Start: Iteration 2
-2016-09-06 10:37:50,489 DEBUG: View 0 : 0.721893491124
-2016-09-06 10:37:50,497 DEBUG: View 1 : 0.710059171598
-2016-09-06 10:37:50,505 DEBUG: View 2 : 0.680473372781
-2016-09-06 10:37:50,513 DEBUG: View 3 : 0.739644970414
-2016-09-06 10:37:50,551 DEBUG: Best view : View3
-2016-09-06 10:37:50,697 DEBUG: Start: Iteration 3
-2016-09-06 10:37:50,712 DEBUG: View 0 : 0.721893491124
-2016-09-06 10:37:50,722 DEBUG: View 1 : 0.710059171598
-2016-09-06 10:37:50,730 DEBUG: View 2 : 0.680473372781
-2016-09-06 10:37:50,737 DEBUG: View 3 : 0.739644970414
-2016-09-06 10:37:50,776 DEBUG: Best view : View3
-2016-09-06 10:37:50,980 INFO: Start: Classification
-2016-09-06 10:37:51,321 INFO: Done: Fold number 1
-2016-09-06 10:37:51,321 INFO: Start: Fold number 2
-2016-09-06 10:37:51,351 DEBUG: Start: Iteration 1
-2016-09-06 10:37:51,358 DEBUG: View 0 : 0.431952662722
-2016-09-06 10:37:51,365 DEBUG: View 1 : 0.431952662722
-2016-09-06 10:37:51,372 DEBUG: View 2 : 0.431952662722
-2016-09-06 10:37:51,380 DEBUG: View 3 : 0.431952662722
-2016-09-06 10:37:51,380 WARNING: WARNING: All bad for iteration 0
-2016-09-06 10:37:51,410 DEBUG: Best view : View0
-2016-09-06 10:37:51,489 DEBUG: Start: Iteration 2
-2016-09-06 10:37:51,496 DEBUG: View 0 : 0.710059171598
-2016-09-06 10:37:51,504 DEBUG: View 1 : 0.644970414201
-2016-09-06 10:37:51,511 DEBUG: View 2 : 0.721893491124
-2016-09-06 10:37:51,518 DEBUG: View 3 : 0.674556213018
-2016-09-06 10:37:51,555 DEBUG: Best view : View2
-2016-09-06 10:37:51,694 DEBUG: Start: Iteration 3
-2016-09-06 10:37:51,701 DEBUG: View 0 : 0.710059171598
-2016-09-06 10:37:51,709 DEBUG: View 1 : 0.644970414201
-2016-09-06 10:37:51,716 DEBUG: View 2 : 0.721893491124
-2016-09-06 10:37:51,723 DEBUG: View 3 : 0.674556213018
-2016-09-06 10:37:51,762 DEBUG: Best view : View2
-2016-09-06 10:37:51,964 INFO: Start: Classification
-2016-09-06 10:37:52,305 INFO: Done: Fold number 2
-2016-09-06 10:37:52,305 INFO: Start: Fold number 3
-2016-09-06 10:37:52,333 DEBUG: Start: Iteration 1
-2016-09-06 10:37:52,340 DEBUG: View 0 : 0.414201183432
-2016-09-06 10:37:52,347 DEBUG: View 1 : 0.467455621302
-2016-09-06 10:37:52,355 DEBUG: View 2 : 0.461538461538
-2016-09-06 10:37:52,361 DEBUG: View 3 : 0.514792899408
-2016-09-06 10:37:52,391 DEBUG: Best view : View2
-2016-09-06 10:37:52,466 DEBUG: Start: Iteration 2
-2016-09-06 10:37:52,473 DEBUG: View 0 : 0.710059171598
-2016-09-06 10:37:52,481 DEBUG: View 1 : 0.721893491124
-2016-09-06 10:37:52,489 DEBUG: View 2 : 0.704142011834
-2016-09-06 10:37:52,495 DEBUG: View 3 : 0.733727810651
-2016-09-06 10:37:52,532 DEBUG: Best view : View3
-2016-09-06 10:37:52,672 DEBUG: Start: Iteration 3
-2016-09-06 10:37:52,679 DEBUG: View 0 : 0.710059171598
-2016-09-06 10:37:52,686 DEBUG: View 1 : 0.721893491124
-2016-09-06 10:37:52,694 DEBUG: View 2 : 0.704142011834
-2016-09-06 10:37:52,701 DEBUG: View 3 : 0.733727810651
-2016-09-06 10:37:52,739 DEBUG: Best view : View3
-2016-09-06 10:37:52,941 INFO: Start: Classification
-2016-09-06 10:37:53,279 INFO: Done: Fold number 3
-2016-09-06 10:37:53,279 INFO: Start: Fold number 4
-2016-09-06 10:37:53,308 DEBUG: Start: Iteration 1
-2016-09-06 10:37:53,315 DEBUG: View 0 : 0.568047337278
-2016-09-06 10:37:53,322 DEBUG: View 1 : 0.568047337278
-2016-09-06 10:37:53,328 DEBUG: View 2 : 0.568047337278
-2016-09-06 10:37:53,335 DEBUG: View 3 : 0.568047337278
-2016-09-06 10:37:53,365 DEBUG: Best view : View0
-2016-09-06 10:37:53,439 DEBUG: Start: Iteration 2
-2016-09-06 10:37:53,446 DEBUG: View 0 : 0.662721893491
-2016-09-06 10:37:53,454 DEBUG: View 1 : 0.715976331361
-2016-09-06 10:37:53,461 DEBUG: View 2 : 0.739644970414
-2016-09-06 10:37:53,468 DEBUG: View 3 : 0.692307692308
-2016-09-06 10:37:53,504 DEBUG: Best view : View2
-2016-09-06 10:37:53,643 DEBUG: Start: Iteration 3
-2016-09-06 10:37:53,650 DEBUG: View 0 : 0.662721893491
-2016-09-06 10:37:53,658 DEBUG: View 1 : 0.715976331361
-2016-09-06 10:37:53,665 DEBUG: View 2 : 0.739644970414
-2016-09-06 10:37:53,673 DEBUG: View 3 : 0.692307692308
-2016-09-06 10:37:53,711 DEBUG: Best view : View2
-2016-09-06 10:37:53,914 INFO: Start: Classification
-2016-09-06 10:37:54,254 INFO: Done: Fold number 4
-2016-09-06 10:37:54,254 INFO: Start: Fold number 5
-2016-09-06 10:37:54,282 DEBUG: Start: Iteration 1
-2016-09-06 10:37:54,289 DEBUG: View 0 : 0.431952662722
-2016-09-06 10:37:54,296 DEBUG: View 1 : 0.431952662722
-2016-09-06 10:37:54,303 DEBUG: View 2 : 0.431952662722
-2016-09-06 10:37:54,309 DEBUG: View 3 : 0.431952662722
-2016-09-06 10:37:54,310 WARNING: WARNING: All bad for iteration 0
-2016-09-06 10:37:54,340 DEBUG: Best view : View0
-2016-09-06 10:37:54,415 DEBUG: Start: Iteration 2
-2016-09-06 10:37:54,423 DEBUG: View 0 : 0.656804733728
-2016-09-06 10:37:54,430 DEBUG: View 1 : 0.733727810651
-2016-09-06 10:37:54,437 DEBUG: View 2 : 0.692307692308
-2016-09-06 10:37:54,444 DEBUG: View 3 : 0.710059171598
-2016-09-06 10:37:54,480 DEBUG: Best view : View1
-2016-09-06 10:37:54,620 DEBUG: Start: Iteration 3
-2016-09-06 10:37:54,627 DEBUG: View 0 : 0.656804733728
-2016-09-06 10:37:54,634 DEBUG: View 1 : 0.733727810651
-2016-09-06 10:37:54,642 DEBUG: View 2 : 0.692307692308
-2016-09-06 10:37:54,649 DEBUG: View 3 : 0.710059171598
-2016-09-06 10:37:54,688 DEBUG: Best view : View1
-2016-09-06 10:37:54,891 INFO: Start: Classification
-2016-09-06 10:37:55,231 INFO: Done: Fold number 5
-2016-09-06 10:37:55,231 INFO: Done: Classification
-2016-09-06 10:37:55,231 INFO: Info: Time for Classification: 4[s]
-2016-09-06 10:37:55,231 INFO: Start: Result Analysis for Mumbo
-2016-09-06 10:37:57,067 INFO: Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 73.2544378698
- -On Test : 46.1904761905
- -On Validation : 57.0786516854Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 5), View1 of shape (300, 12), View2 of shape (300, 14), View3 of shape (300, 5)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:01 0:00:00
- Fold 3 0:00:02 0:00:00
- Fold 4 0:00:03 0:00:00
- Fold 5 0:00:04 0:00:00
- Total 0:00:13 0:00:01
- So a total classification time of 0:00:04.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 4
- - On View0 :
- - Mean average Accuracy : 0.187573964497
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.185207100592
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.179289940828
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.191124260355
- - Percentage of time chosen : 0.2
- - Fold 1, used 4
- - On View0 :
- - Mean average Accuracy : 0.185207100592
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.172189349112
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.187573964497
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.178106508876
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.183431952663
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.191124260355
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.186982248521
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.198224852071
- - Percentage of time chosen : 0.2
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.189349112426
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.2
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.204733727811
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.195266272189
- - Percentage of time chosen : 0.0
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.174556213018
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.189940828402
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.181656804734
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.185207100592
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 2
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 3
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View2
- Fold 4
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 5
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 73.9644970414
- Accuracy on test : 0.0
- Accuracy on validation : 66.2921348315
- Selected View : View3
- Fold 2
- Accuracy on train : 72.1893491124
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View2
- Fold 3
- Accuracy on train : 73.3727810651
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View3
- Fold 4
- Accuracy on train : 73.9644970414
- Accuracy on test : 0.0
- Accuracy on validation : 64.0449438202
- Selected View : View2
- Fold 5
- Accuracy on train : 73.3727810651
- Accuracy on test : 0.0
- Accuracy on validation : 51.6853932584
- Selected View : View1
- - Iteration 3
- Fold 1
- Accuracy on train : 73.9644970414
- Accuracy on test : 0.0
- Accuracy on validation : 66.2921348315
- Selected View : View3
- Fold 2
- Accuracy on train : 72.1893491124
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View2
- Fold 3
- Accuracy on train : 73.3727810651
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View3
- Fold 4
- Accuracy on train : 73.3727810651
- Accuracy on test : 0.0
- Accuracy on validation : 60.6741573034
- Selected View : View2
- Fold 5
- Accuracy on train : 73.3727810651
- Accuracy on test : 0.0
- Accuracy on validation : 51.6853932584
- Selected View : View1
- - Iteration 4
- Fold 1
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 2
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 3
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 4
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 5
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
-2016-09-06 10:37:57,248 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e15b39c3e5f5cea3fdd23289b5949f993a7578d8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d6b6f1ee26e3a862dded10980056d8ca2df2a65e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.738095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.738095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8033aa1cce1ff5e96eb58b835345d60f300cf98b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.67619047619
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 594945a82902d634a05b6837a381fe4c197cb3e7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.766666666667
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.766666666667
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 63c6ae28890236e93e07342595d2185183d08db2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8b456925285c8b84a6ae39dcb7d56c87c5972ceb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 00b45eb3b41d45af390a9e2988b9060090462823..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.366666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.366666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c2d3d627eb0ff0c9f95578b2f1e2f7f3fecfda2b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103747Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d49ccc5793b3cd8fba676af3d5aa10f92ca133b8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 96bde0ecd79984fedd5c0621e5bb9c1a1477800f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.690476190476
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.690476190476
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 40e8b33c4b9931566d7f7b265aac1a52fe779064..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.690476190476
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.690476190476
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 61507bca2f7b4fe9c922d85d643d4cd04e66421b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.833333333333
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.833333333333
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f8a94e87a80edb940442bcb95de89bbd46affe90..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8ebd07f45ad022d999bcaab360e9c042b906914d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cb5ad814a02065b63528f5bf97a304d7c969f422..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e113ccefa2a0093fce2acd39d7a50b70b7c50866..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103748Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d2a1e68014aae7bbae32ed22b874c1ebd6efadae..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d8ec68e9648744b82100ecf7fa6b227e72e3e4cf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.742857142857
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.742857142857
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a85fe8bb54b716f7c83e44ceec69d452d98206b1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.709523809524
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.709523809524
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bd9b6ea7d01b48136ba43d2beb4aaa3686aa4f3b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.8
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.8
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0baeab0532f326a580ca28ca5aa8eff0d09cd94c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5d435b5632b7c130f5124f2a40617a4872cf4654..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 78a5329c012d50a70ad3fb0e480a3d816fab08bf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8f3e16aec05d7d33b3e4961f9de8fb400216927b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103749Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c6c250a5484a573060809e38fb1ce64d09cca425..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 367caf755d6753a27251dc8eba5d858bdff74b60..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103750Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 4648
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png
deleted file mode 100644
index f5bbc8bd0c286eee40e266058650c7eda94a4b4b..0000000000000000000000000000000000000000
Binary files a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake-accuracyByIteration.png and /dev/null differ
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 65472fd456b000232b9ed53401823cf19a997e61..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103757Results-Mumbo-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,209 +0,0 @@
- Result for Multiview classification with Mumbo
-
-Average accuracy :
- -On Train : 73.2544378698
- -On Test : 46.1904761905
- -On Validation : 57.0786516854Dataset info :
- -Dataset name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : View0 of shape (300, 5), View1 of shape (300, 12), View2 of shape (300, 14), View3 of shape (300, 5)
- -5 folds
- - Validation set length : 89 for learning rate : 0.7 on a total number of examples of 300
-
-
-
-Mumbo configuration :
- -Used 1 core(s)
- -Iterations : min 1, max 10, threshold 0.01
- -Weak Classifiers : DecisionTree with depth 3, sub-sampled at 1.0 on View0
- -DecisionTree with depth 3, sub-sampled at 1.0 on View1
- -DecisionTree with depth 3, sub-sampled at 1.0 on View2
- -DecisionTree with depth 3, sub-sampled at 1.0 on View3
-
-
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:01 0:00:00
- Fold 3 0:00:02 0:00:00
- Fold 4 0:00:03 0:00:00
- Fold 5 0:00:04 0:00:00
- Total 0:00:13 0:00:01
- So a total classification time of 0:00:04.
-
-
-
-Mean average accuracies and stats for each fold :
- - Fold 0, used 4
- - On View0 :
- - Mean average Accuracy : 0.187573964497
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.185207100592
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.179289940828
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.191124260355
- - Percentage of time chosen : 0.2
- - Fold 1, used 4
- - On View0 :
- - Mean average Accuracy : 0.185207100592
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.172189349112
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.187573964497
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.178106508876
- - Percentage of time chosen : 0.0
- - Fold 2, used 4
- - On View0 :
- - Mean average Accuracy : 0.183431952663
- - Percentage of time chosen : 0.7
- - On View1 :
- - Mean average Accuracy : 0.191124260355
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.186982248521
- - Percentage of time chosen : 0.1
- - On View3 :
- - Mean average Accuracy : 0.198224852071
- - Percentage of time chosen : 0.2
- - Fold 3, used 4
- - On View0 :
- - Mean average Accuracy : 0.189349112426
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.2
- - Percentage of time chosen : 0.0
- - On View2 :
- - Mean average Accuracy : 0.204733727811
- - Percentage of time chosen : 0.2
- - On View3 :
- - Mean average Accuracy : 0.195266272189
- - Percentage of time chosen : 0.0
- - Fold 4, used 4
- - On View0 :
- - Mean average Accuracy : 0.174556213018
- - Percentage of time chosen : 0.8
- - On View1 :
- - Mean average Accuracy : 0.189940828402
- - Percentage of time chosen : 0.2
- - On View2 :
- - Mean average Accuracy : 0.181656804734
- - Percentage of time chosen : 0.0
- - On View3 :
- - Mean average Accuracy : 0.185207100592
- - Percentage of time chosen : 0.0
-
- For each iteration :
- - Iteration 1
- Fold 1
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 2
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 3
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View2
- Fold 4
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 5
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- - Iteration 2
- Fold 1
- Accuracy on train : 73.9644970414
- Accuracy on test : 0.0
- Accuracy on validation : 66.2921348315
- Selected View : View3
- Fold 2
- Accuracy on train : 72.1893491124
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View2
- Fold 3
- Accuracy on train : 73.3727810651
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View3
- Fold 4
- Accuracy on train : 73.9644970414
- Accuracy on test : 0.0
- Accuracy on validation : 64.0449438202
- Selected View : View2
- Fold 5
- Accuracy on train : 73.3727810651
- Accuracy on test : 0.0
- Accuracy on validation : 51.6853932584
- Selected View : View1
- - Iteration 3
- Fold 1
- Accuracy on train : 73.9644970414
- Accuracy on test : 0.0
- Accuracy on validation : 66.2921348315
- Selected View : View3
- Fold 2
- Accuracy on train : 72.1893491124
- Accuracy on test : 0.0
- Accuracy on validation : 48.3146067416
- Selected View : View2
- Fold 3
- Accuracy on train : 73.3727810651
- Accuracy on test : 0.0
- Accuracy on validation : 58.4269662921
- Selected View : View3
- Fold 4
- Accuracy on train : 73.3727810651
- Accuracy on test : 0.0
- Accuracy on validation : 60.6741573034
- Selected View : View2
- Fold 5
- Accuracy on train : 73.3727810651
- Accuracy on test : 0.0
- Accuracy on validation : 51.6853932584
- Selected View : View1
- - Iteration 4
- Fold 1
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 2
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 3
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 4
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
- Fold 5
- Accuracy on train : 56.8047337278
- Accuracy on test : 0.0
- Accuracy on validation : 57.3033707865
- Selected View : View0
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index bc6f192d94981023cc7955562900b39081a68607..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103929-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1250 +0,0 @@
-2016-09-06 10:39:29,831 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:39:29,831 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000170875 Gbytes /!\
-2016-09-06 10:39:34,843 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:39:34,844 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:39:34,896 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:34,896 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:39:34,896 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:34,897 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:34,897 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:39:34,897 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:34,897 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:34,898 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:34,898 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:34,898 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:34,898 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:34,898 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:34,899 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:34,899 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:34,942 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:34,943 DEBUG: Start: Training
-2016-09-06 10:39:34,946 DEBUG: Info: Time for Training: 0.0496671199799[s]
-2016-09-06 10:39:34,946 DEBUG: Done: Training
-2016-09-06 10:39:34,946 DEBUG: Start: Predicting
-2016-09-06 10:39:34,948 DEBUG: Done: Predicting
-2016-09-06 10:39:34,949 DEBUG: Start: Getting Results
-2016-09-06 10:39:34,950 DEBUG: Done: Getting Results
-2016-09-06 10:39:34,950 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:39:34,950 INFO: Done: Result Analysis
-2016-09-06 10:39:34,952 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:34,952 DEBUG: Start: Training
-2016-09-06 10:39:34,957 DEBUG: Info: Time for Training: 0.0618000030518[s]
-2016-09-06 10:39:34,957 DEBUG: Done: Training
-2016-09-06 10:39:34,957 DEBUG: Start: Predicting
-2016-09-06 10:39:34,960 DEBUG: Done: Predicting
-2016-09-06 10:39:34,961 DEBUG: Start: Getting Results
-2016-09-06 10:39:34,962 DEBUG: Done: Getting Results
-2016-09-06 10:39:34,963 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:34,963 INFO: Done: Result Analysis
-2016-09-06 10:39:35,046 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,046 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,046 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:39:35,046 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:39:35,046 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:35,046 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:35,047 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,047 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,047 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,047 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,047 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:35,047 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:35,047 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,047 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,081 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:35,081 DEBUG: Start: Training
-2016-09-06 10:39:35,081 DEBUG: Info: Time for Training: 0.0358710289001[s]
-2016-09-06 10:39:35,081 DEBUG: Done: Training
-2016-09-06 10:39:35,081 DEBUG: Start: Predicting
-2016-09-06 10:39:35,089 DEBUG: Done: Predicting
-2016-09-06 10:39:35,089 DEBUG: Start: Getting Results
-2016-09-06 10:39:35,091 DEBUG: Done: Getting Results
-2016-09-06 10:39:35,091 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,091 INFO: Done: Result Analysis
-2016-09-06 10:39:35,328 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:35,328 DEBUG: Start: Training
-2016-09-06 10:39:35,368 DEBUG: Info: Time for Training: 0.322957992554[s]
-2016-09-06 10:39:35,369 DEBUG: Done: Training
-2016-09-06 10:39:35,369 DEBUG: Start: Predicting
-2016-09-06 10:39:35,374 DEBUG: Done: Predicting
-2016-09-06 10:39:35,374 DEBUG: Start: Getting Results
-2016-09-06 10:39:35,376 DEBUG: Done: Getting Results
-2016-09-06 10:39:35,376 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 15, max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,376 INFO: Done: Result Analysis
-2016-09-06 10:39:35,496 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,496 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,496 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:39:35,496 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:39:35,497 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:35,497 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:35,498 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,498 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,498 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,498 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,498 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:35,498 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:35,498 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,498 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,546 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:35,546 DEBUG: Start: Training
-2016-09-06 10:39:35,547 DEBUG: Info: Time for Training: 0.0519301891327[s]
-2016-09-06 10:39:35,547 DEBUG: Done: Training
-2016-09-06 10:39:35,547 DEBUG: Start: Predicting
-2016-09-06 10:39:35,553 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:35,554 DEBUG: Start: Training
-2016-09-06 10:39:35,565 DEBUG: Done: Predicting
-2016-09-06 10:39:35,565 DEBUG: Start: Getting Results
-2016-09-06 10:39:35,567 DEBUG: Done: Getting Results
-2016-09-06 10:39:35,567 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,567 INFO: Done: Result Analysis
-2016-09-06 10:39:35,575 DEBUG: Info: Time for Training: 0.0798330307007[s]
-2016-09-06 10:39:35,575 DEBUG: Done: Training
-2016-09-06 10:39:35,575 DEBUG: Start: Predicting
-2016-09-06 10:39:35,579 DEBUG: Done: Predicting
-2016-09-06 10:39:35,579 DEBUG: Start: Getting Results
-2016-09-06 10:39:35,581 DEBUG: Done: Getting Results
-2016-09-06 10:39:35,581 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,581 INFO: Done: Result Analysis
-2016-09-06 10:39:35,643 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,643 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,644 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:39:35,644 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:39:35,644 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:35,644 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:35,645 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,645 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 10:39:35,645 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,646 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:35,646 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 10:39:35,646 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,646 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:35,646 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,698 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:35,699 DEBUG: Start: Training
-2016-09-06 10:39:35,701 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:35,701 DEBUG: Start: Training
-2016-09-06 10:39:35,716 DEBUG: Info: Time for Training: 0.0735998153687[s]
-2016-09-06 10:39:35,717 DEBUG: Done: Training
-2016-09-06 10:39:35,717 DEBUG: Start: Predicting
-2016-09-06 10:39:35,720 DEBUG: Info: Time for Training: 0.0770709514618[s]
-2016-09-06 10:39:35,720 DEBUG: Done: Training
-2016-09-06 10:39:35,720 DEBUG: Start: Predicting
-2016-09-06 10:39:35,723 DEBUG: Done: Predicting
-2016-09-06 10:39:35,723 DEBUG: Start: Getting Results
-2016-09-06 10:39:35,724 DEBUG: Done: Predicting
-2016-09-06 10:39:35,724 DEBUG: Done: Getting Results
-2016-09-06 10:39:35,724 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,724 DEBUG: Start: Getting Results
-2016-09-06 10:39:35,724 INFO: Done: Result Analysis
-2016-09-06 10:39:35,726 DEBUG: Done: Getting Results
-2016-09-06 10:39:35,726 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,726 INFO: Done: Result Analysis
-2016-09-06 10:39:35,791 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,791 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,791 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:39:35,791 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:39:35,791 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:35,791 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:35,792 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:35,792 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:35,792 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:35,792 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:35,792 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:35,792 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:35,792 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,792 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,826 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:35,827 DEBUG: Start: Training
-2016-09-06 10:39:35,828 DEBUG: Info: Time for Training: 0.0375211238861[s]
-2016-09-06 10:39:35,828 DEBUG: Done: Training
-2016-09-06 10:39:35,828 DEBUG: Start: Predicting
-2016-09-06 10:39:35,831 DEBUG: Done: Predicting
-2016-09-06 10:39:35,831 DEBUG: Start: Getting Results
-2016-09-06 10:39:35,832 DEBUG: Done: Getting Results
-2016-09-06 10:39:35,832 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,833 INFO: Done: Result Analysis
-2016-09-06 10:39:35,840 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:35,840 DEBUG: Start: Training
-2016-09-06 10:39:35,843 DEBUG: Info: Time for Training: 0.0527670383453[s]
-2016-09-06 10:39:35,843 DEBUG: Done: Training
-2016-09-06 10:39:35,843 DEBUG: Start: Predicting
-2016-09-06 10:39:35,846 DEBUG: Done: Predicting
-2016-09-06 10:39:35,846 DEBUG: Start: Getting Results
-2016-09-06 10:39:35,848 DEBUG: Done: Getting Results
-2016-09-06 10:39:35,848 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:39:35,848 INFO: Done: Result Analysis
-2016-09-06 10:39:35,945 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,945 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:35,945 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:39:35,945 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:39:35,945 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:35,945 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:35,946 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:35,946 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:35,947 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:35,947 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:35,947 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:35,947 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:35,947 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,947 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:35,998 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:35,998 DEBUG: Start: Training
-2016-09-06 10:39:35,999 DEBUG: Info: Time for Training: 0.0549540519714[s]
-2016-09-06 10:39:35,999 DEBUG: Done: Training
-2016-09-06 10:39:35,999 DEBUG: Start: Predicting
-2016-09-06 10:39:36,008 DEBUG: Done: Predicting
-2016-09-06 10:39:36,008 DEBUG: Start: Getting Results
-2016-09-06 10:39:36,011 DEBUG: Done: Getting Results
-2016-09-06 10:39:36,011 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,011 INFO: Done: Result Analysis
-2016-09-06 10:39:36,240 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:36,240 DEBUG: Start: Training
-2016-09-06 10:39:36,279 DEBUG: Info: Time for Training: 0.334888935089[s]
-2016-09-06 10:39:36,279 DEBUG: Done: Training
-2016-09-06 10:39:36,279 DEBUG: Start: Predicting
-2016-09-06 10:39:36,284 DEBUG: Done: Predicting
-2016-09-06 10:39:36,285 DEBUG: Start: Getting Results
-2016-09-06 10:39:36,286 DEBUG: Done: Getting Results
-2016-09-06 10:39:36,286 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 15, max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,286 INFO: Done: Result Analysis
-2016-09-06 10:39:36,394 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,394 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,394 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:39:36,394 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:39:36,394 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:36,394 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:36,395 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:36,395 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:36,395 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:36,395 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:36,395 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:36,395 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,395 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:36,396 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,441 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:36,441 DEBUG: Start: Training
-2016-09-06 10:39:36,442 DEBUG: Info: Time for Training: 0.0489950180054[s]
-2016-09-06 10:39:36,442 DEBUG: Done: Training
-2016-09-06 10:39:36,442 DEBUG: Start: Predicting
-2016-09-06 10:39:36,445 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:36,445 DEBUG: Start: Training
-2016-09-06 10:39:36,463 DEBUG: Info: Time for Training: 0.0693230628967[s]
-2016-09-06 10:39:36,463 DEBUG: Done: Training
-2016-09-06 10:39:36,463 DEBUG: Start: Predicting
-2016-09-06 10:39:36,466 DEBUG: Done: Predicting
-2016-09-06 10:39:36,466 DEBUG: Start: Getting Results
-2016-09-06 10:39:36,467 DEBUG: Done: Predicting
-2016-09-06 10:39:36,467 DEBUG: Start: Getting Results
-2016-09-06 10:39:36,468 DEBUG: Done: Getting Results
-2016-09-06 10:39:36,469 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,469 INFO: Done: Result Analysis
-2016-09-06 10:39:36,469 DEBUG: Done: Getting Results
-2016-09-06 10:39:36,469 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.377777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,470 INFO: Done: Result Analysis
-2016-09-06 10:39:36,547 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,547 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:39:36,548 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:39:36,548 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:36,548 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:36,550 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:36,550 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 10:39:36,550 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:36,550 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 10:39:36,550 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:36,550 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:36,550 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,550 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,603 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:36,603 DEBUG: Start: Training
-2016-09-06 10:39:36,613 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:36,614 DEBUG: Start: Training
-2016-09-06 10:39:36,619 DEBUG: Info: Time for Training: 0.0724880695343[s]
-2016-09-06 10:39:36,619 DEBUG: Done: Training
-2016-09-06 10:39:36,619 DEBUG: Start: Predicting
-2016-09-06 10:39:36,624 DEBUG: Done: Predicting
-2016-09-06 10:39:36,624 DEBUG: Start: Getting Results
-2016-09-06 10:39:36,626 DEBUG: Done: Getting Results
-2016-09-06 10:39:36,626 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,626 INFO: Done: Result Analysis
-2016-09-06 10:39:36,631 DEBUG: Info: Time for Training: 0.0851111412048[s]
-2016-09-06 10:39:36,631 DEBUG: Done: Training
-2016-09-06 10:39:36,632 DEBUG: Start: Predicting
-2016-09-06 10:39:36,635 DEBUG: Done: Predicting
-2016-09-06 10:39:36,635 DEBUG: Start: Getting Results
-2016-09-06 10:39:36,637 DEBUG: Done: Getting Results
-2016-09-06 10:39:36,637 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,638 INFO: Done: Result Analysis
-2016-09-06 10:39:36,795 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,795 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:39:36,795 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:36,796 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,796 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:36,797 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:39:36,797 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:36,797 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:36,797 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:36,797 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,798 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:36,798 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:36,798 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:36,798 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,840 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:36,840 DEBUG: Start: Training
-2016-09-06 10:39:36,844 DEBUG: Info: Time for Training: 0.0481259822845[s]
-2016-09-06 10:39:36,844 DEBUG: Done: Training
-2016-09-06 10:39:36,844 DEBUG: Start: Predicting
-2016-09-06 10:39:36,846 DEBUG: Done: Predicting
-2016-09-06 10:39:36,847 DEBUG: Start: Getting Results
-2016-09-06 10:39:36,848 DEBUG: Done: Getting Results
-2016-09-06 10:39:36,848 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,848 INFO: Done: Result Analysis
-2016-09-06 10:39:36,851 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:36,851 DEBUG: Start: Training
-2016-09-06 10:39:36,856 DEBUG: Info: Time for Training: 0.0621771812439[s]
-2016-09-06 10:39:36,856 DEBUG: Done: Training
-2016-09-06 10:39:36,856 DEBUG: Start: Predicting
-2016-09-06 10:39:36,859 DEBUG: Done: Predicting
-2016-09-06 10:39:36,860 DEBUG: Start: Getting Results
-2016-09-06 10:39:36,862 DEBUG: Done: Getting Results
-2016-09-06 10:39:36,862 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,862 INFO: Done: Result Analysis
-2016-09-06 10:39:36,939 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,939 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:36,940 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:39:36,940 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:39:36,940 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:36,940 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:36,940 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:36,940 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:36,940 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:36,940 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:36,940 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:36,940 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:36,941 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,941 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:36,974 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:36,974 DEBUG: Start: Training
-2016-09-06 10:39:36,974 DEBUG: Info: Time for Training: 0.0355970859528[s]
-2016-09-06 10:39:36,975 DEBUG: Done: Training
-2016-09-06 10:39:36,975 DEBUG: Start: Predicting
-2016-09-06 10:39:36,983 DEBUG: Done: Predicting
-2016-09-06 10:39:36,983 DEBUG: Start: Getting Results
-2016-09-06 10:39:36,984 DEBUG: Done: Getting Results
-2016-09-06 10:39:36,984 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:39:36,984 INFO: Done: Result Analysis
-2016-09-06 10:39:37,218 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:37,219 DEBUG: Start: Training
-2016-09-06 10:39:37,260 DEBUG: Info: Time for Training: 0.320749998093[s]
-2016-09-06 10:39:37,260 DEBUG: Done: Training
-2016-09-06 10:39:37,260 DEBUG: Start: Predicting
-2016-09-06 10:39:37,265 DEBUG: Done: Predicting
-2016-09-06 10:39:37,265 DEBUG: Start: Getting Results
-2016-09-06 10:39:37,267 DEBUG: Done: Getting Results
-2016-09-06 10:39:37,267 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 15, max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,267 INFO: Done: Result Analysis
-2016-09-06 10:39:37,387 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,387 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,387 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:39:37,387 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:39:37,387 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:37,387 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:37,388 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:37,388 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:37,388 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:37,388 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:37,388 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:37,388 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:37,388 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,388 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,439 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:37,439 DEBUG: Start: Training
-2016-09-06 10:39:37,440 DEBUG: Info: Time for Training: 0.0537350177765[s]
-2016-09-06 10:39:37,440 DEBUG: Done: Training
-2016-09-06 10:39:37,440 DEBUG: Start: Predicting
-2016-09-06 10:39:37,440 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:37,440 DEBUG: Start: Training
-2016-09-06 10:39:37,460 DEBUG: Done: Predicting
-2016-09-06 10:39:37,461 DEBUG: Start: Getting Results
-2016-09-06 10:39:37,464 DEBUG: Done: Getting Results
-2016-09-06 10:39:37,464 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,465 INFO: Done: Result Analysis
-2016-09-06 10:39:37,469 DEBUG: Info: Time for Training: 0.082640171051[s]
-2016-09-06 10:39:37,469 DEBUG: Done: Training
-2016-09-06 10:39:37,469 DEBUG: Start: Predicting
-2016-09-06 10:39:37,473 DEBUG: Done: Predicting
-2016-09-06 10:39:37,473 DEBUG: Start: Getting Results
-2016-09-06 10:39:37,474 DEBUG: Done: Getting Results
-2016-09-06 10:39:37,474 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,475 INFO: Done: Result Analysis
-2016-09-06 10:39:37,532 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,532 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,532 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:39:37,532 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:39:37,532 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:37,532 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:37,533 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:37,533 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 10:39:37,533 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:37,533 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 10:39:37,533 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:37,533 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:37,533 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,533 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,582 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:37,582 DEBUG: Start: Training
-2016-09-06 10:39:37,587 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:37,588 DEBUG: Start: Training
-2016-09-06 10:39:37,601 DEBUG: Info: Time for Training: 0.0697820186615[s]
-2016-09-06 10:39:37,601 DEBUG: Done: Training
-2016-09-06 10:39:37,601 DEBUG: Start: Predicting
-2016-09-06 10:39:37,607 DEBUG: Info: Time for Training: 0.0757231712341[s]
-2016-09-06 10:39:37,607 DEBUG: Done: Training
-2016-09-06 10:39:37,607 DEBUG: Start: Predicting
-2016-09-06 10:39:37,607 DEBUG: Done: Predicting
-2016-09-06 10:39:37,607 DEBUG: Start: Getting Results
-2016-09-06 10:39:37,609 DEBUG: Done: Getting Results
-2016-09-06 10:39:37,609 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,609 INFO: Done: Result Analysis
-2016-09-06 10:39:37,611 DEBUG: Done: Predicting
-2016-09-06 10:39:37,611 DEBUG: Start: Getting Results
-2016-09-06 10:39:37,613 DEBUG: Done: Getting Results
-2016-09-06 10:39:37,613 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,613 INFO: Done: Result Analysis
-2016-09-06 10:39:37,681 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,681 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,681 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:39:37,681 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:39:37,682 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:37,682 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:37,682 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:37,682 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:37,683 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:37,683 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:37,683 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:37,683 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,683 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:37,683 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,723 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:37,723 DEBUG: Start: Training
-2016-09-06 10:39:37,725 DEBUG: Info: Time for Training: 0.0450918674469[s]
-2016-09-06 10:39:37,726 DEBUG: Done: Training
-2016-09-06 10:39:37,726 DEBUG: Start: Predicting
-2016-09-06 10:39:37,728 DEBUG: Done: Predicting
-2016-09-06 10:39:37,728 DEBUG: Start: Getting Results
-2016-09-06 10:39:37,730 DEBUG: Done: Getting Results
-2016-09-06 10:39:37,730 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,730 INFO: Done: Result Analysis
-2016-09-06 10:39:37,739 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:37,739 DEBUG: Start: Training
-2016-09-06 10:39:37,743 DEBUG: Info: Time for Training: 0.0631110668182[s]
-2016-09-06 10:39:37,744 DEBUG: Done: Training
-2016-09-06 10:39:37,744 DEBUG: Start: Predicting
-2016-09-06 10:39:37,746 DEBUG: Done: Predicting
-2016-09-06 10:39:37,747 DEBUG: Start: Getting Results
-2016-09-06 10:39:37,748 DEBUG: Done: Getting Results
-2016-09-06 10:39:37,749 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,749 INFO: Done: Result Analysis
-2016-09-06 10:39:37,835 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,835 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:37,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:39:37,836 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:39:37,836 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:37,836 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:37,836 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:37,837 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:37,837 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:37,837 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:37,837 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,837 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:37,837 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:37,837 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:37,875 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:37,875 DEBUG: Start: Training
-2016-09-06 10:39:37,876 DEBUG: Info: Time for Training: 0.0416429042816[s]
-2016-09-06 10:39:37,876 DEBUG: Done: Training
-2016-09-06 10:39:37,877 DEBUG: Start: Predicting
-2016-09-06 10:39:37,885 DEBUG: Done: Predicting
-2016-09-06 10:39:37,886 DEBUG: Start: Getting Results
-2016-09-06 10:39:37,887 DEBUG: Done: Getting Results
-2016-09-06 10:39:37,887 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:39:37,887 INFO: Done: Result Analysis
-2016-09-06 10:39:38,178 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:38,178 DEBUG: Start: Training
-2016-09-06 10:39:38,218 DEBUG: Info: Time for Training: 0.383457183838[s]
-2016-09-06 10:39:38,218 DEBUG: Done: Training
-2016-09-06 10:39:38,218 DEBUG: Start: Predicting
-2016-09-06 10:39:38,224 DEBUG: Done: Predicting
-2016-09-06 10:39:38,224 DEBUG: Start: Getting Results
-2016-09-06 10:39:38,225 DEBUG: Done: Getting Results
-2016-09-06 10:39:38,226 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 15, max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:39:38,226 INFO: Done: Result Analysis
-2016-09-06 10:39:38,289 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:38,289 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:39:38,289 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:39:38,289 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:39:38,289 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:38,289 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:39:38,290 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:38,290 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-06 10:39:38,291 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:38,291 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-06 10:39:38,291 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:38,291 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:39:38,291 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:38,291 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:39:38,363 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:38,363 DEBUG: Start: Training
-2016-09-06 10:39:38,365 DEBUG: Info: Time for Training: 0.076936006546[s]
-2016-09-06 10:39:38,365 DEBUG: Done: Training
-2016-09-06 10:39:38,365 DEBUG: Start: Predicting
-2016-09-06 10:39:38,371 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:39:38,371 DEBUG: Start: Training
-2016-09-06 10:39:38,377 DEBUG: Done: Predicting
-2016-09-06 10:39:38,378 DEBUG: Start: Getting Results
-2016-09-06 10:39:38,380 DEBUG: Done: Getting Results
-2016-09-06 10:39:38,380 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:39:38,380 INFO: Done: Result Analysis
-2016-09-06 10:39:38,396 DEBUG: Info: Time for Training: 0.108675003052[s]
-2016-09-06 10:39:38,397 DEBUG: Done: Training
-2016-09-06 10:39:38,397 DEBUG: Start: Predicting
-2016-09-06 10:39:38,400 DEBUG: Done: Predicting
-2016-09-06 10:39:38,401 DEBUG: Start: Getting Results
-2016-09-06 10:39:38,402 DEBUG: Done: Getting Results
-2016-09-06 10:39:38,402 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.409523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:39:38,402 INFO: Done: Result Analysis
-2016-09-06 10:39:38,680 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:39:38,680 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:39:38,681 INFO: Info: Shape of View0 :(300, 18)
-2016-09-06 10:39:38,681 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:39:38,682 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:39:38,682 INFO: Info: Shape of View1 :(300, 8)
-2016-09-06 10:39:38,683 INFO: Info: Shape of View0 :(300, 18)
-2016-09-06 10:39:38,683 INFO: Info: Shape of View2 :(300, 19)
-2016-09-06 10:39:38,684 INFO: Info: Shape of View1 :(300, 8)
-2016-09-06 10:39:38,684 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 10:39:38,684 INFO: Done: Read Database Files
-2016-09-06 10:39:38,684 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:39:38,684 INFO: Info: Shape of View2 :(300, 19)
-2016-09-06 10:39:38,685 INFO: Info: Shape of View3 :(300, 20)
-2016-09-06 10:39:38,685 INFO: Done: Read Database Files
-2016-09-06 10:39:38,685 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:39:38,691 INFO: Done: Determine validation split
-2016-09-06 10:39:38,691 INFO: Start: Determine 5 folds
-2016-09-06 10:39:38,691 INFO: Done: Determine validation split
-2016-09-06 10:39:38,692 INFO: Start: Determine 5 folds
-2016-09-06 10:39:38,707 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:39:38,707 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:39:38,707 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:39:38,707 INFO: Info: Length of Learning Sets: 169
-2016-09-06 10:39:38,707 INFO: Done: Determine folds
-2016-09-06 10:39:38,707 INFO: Info: Length of Testing Sets: 42
-2016-09-06 10:39:38,707 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:39:38,707 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:39:38,708 INFO: Done: Determine folds
-2016-09-06 10:39:38,707 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 10:39:38,708 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:39:38,708 DEBUG: Start: Random search for Adaboost with 30 iterations
-2016-09-06 10:39:38,708 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 10:39:38,708 DEBUG: Start: Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7c2f9b7f068dc41865f952bd22adf036738981eb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 225bfa229cbfdec4a1a9eb45196312ca594631fa..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103934Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ce956518238614b7bdebdc2ea0858a235eba8d6c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e4d36bfb3c501b5adc716ae51fd80d555ceb199e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9d6ed1f4dc03c718e10b09c65b43943246e85fff..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ce22fa37f74da0d79613287ea2a28f0367ed90e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 15, max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 00f4dc533a8b52cd78f3ad418f40ac251c3b3109..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cdf61b88a412bc9efb53e31bc431b6ac7629b8e8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5a41a5a4353831223af4d37c3d6fd0cdecc76138..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6ccf359244f5b7c816e205a2cf060cecb7090d86..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103935Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 13e6985553abef9ce2250d0156f7c02204751629..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 50dc139eca46ddd727f57817a35d397e477c5b0c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3df2f2764f2a3b6912df0406acf0debe4ff0cee2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c50be0faaab517f9f476055f93dc8827f4b5fe42..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 15, max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9737bc60b6a81ef8306e51bf1768f3370403522c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3775503636a828deac35d0d630a88a7e4f7596be..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.377777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c60418d9c7280dac254d09a2e03db377d1a60735..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 61f4324fe4d1830886a306540ebba36706bd822b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103936Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 291654d0a64ba132f456a08ec9d2eb54e89ff880..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 11, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d4f311472f5bcd36461ba35eaf04b948491f1dee..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2f3557c25b5505ac1da96d7cdc8b49e97d45637a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 43
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 38a3f4d6dbb92e253780af0c629ce97b0b49083d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 15, max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8c91b51953caa589c23da90aae8580f4406fa914..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 006bed3d21a945338e0ed75c8de6b4d081e2bb50..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9d41e795b9288b98ba77f6acd82a3dac1be247c4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 482a6b43b8b1122f38971932f15ab11324b5322b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103937Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9df5e00e4c3b91debe6c6b59f12e9b173aa4f000..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 15, max_depth : 11
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 69d5a4ea222aed7c1bae135c20aa3d0b5fabba20..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 241a8b39d0fed51e6b032a01cdc8f26f96afe58f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-103938Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.409523809524
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7957
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 12848a0f76e3885f881bd72b569fe95bb365c8d7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,1250 +0,0 @@
-2016-09-06 10:40:15,413 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 10:40:15,414 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000114625 Gbytes /!\
-2016-09-06 10:40:20,424 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 10:40:20,425 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 10:40:20,483 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:20,483 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:20,484 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:40:20,484 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:40:20,484 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:20,484 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:20,485 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:20,485 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:20,485 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:20,485 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:20,485 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:20,485 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:20,485 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:20,486 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:20,546 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:20,546 DEBUG: Start: Training
-2016-09-06 10:40:20,549 DEBUG: Info: Time for Training: 0.0666189193726[s]
-2016-09-06 10:40:20,549 DEBUG: Done: Training
-2016-09-06 10:40:20,549 DEBUG: Start: Predicting
-2016-09-06 10:40:20,553 DEBUG: Done: Predicting
-2016-09-06 10:40:20,553 DEBUG: Start: Getting Results
-2016-09-06 10:40:20,556 DEBUG: Done: Getting Results
-2016-09-06 10:40:20,556 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.933333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.933333333333
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:20,556 INFO: Done: Result Analysis
-2016-09-06 10:40:20,558 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:20,558 DEBUG: Start: Training
-2016-09-06 10:40:20,565 DEBUG: Info: Time for Training: 0.0827748775482[s]
-2016-09-06 10:40:20,565 DEBUG: Done: Training
-2016-09-06 10:40:20,566 DEBUG: Start: Predicting
-2016-09-06 10:40:20,570 DEBUG: Done: Predicting
-2016-09-06 10:40:20,570 DEBUG: Start: Getting Results
-2016-09-06 10:40:20,573 DEBUG: Done: Getting Results
-2016-09-06 10:40:20,573 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:40:20,574 INFO: Done: Result Analysis
-2016-09-06 10:40:20,633 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:20,633 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:20,633 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:40:20,633 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:40:20,633 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:20,633 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:20,634 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:20,634 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:20,634 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:20,634 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:20,634 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:20,634 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:20,634 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:20,634 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:20,688 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:20,688 DEBUG: Start: Training
-2016-09-06 10:40:20,689 DEBUG: Info: Time for Training: 0.0570049285889[s]
-2016-09-06 10:40:20,689 DEBUG: Done: Training
-2016-09-06 10:40:20,689 DEBUG: Start: Predicting
-2016-09-06 10:40:20,698 DEBUG: Done: Predicting
-2016-09-06 10:40:20,698 DEBUG: Start: Getting Results
-2016-09-06 10:40:20,700 DEBUG: Done: Getting Results
-2016-09-06 10:40:20,701 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:40:20,701 INFO: Done: Result Analysis
-2016-09-06 10:40:21,198 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:21,198 DEBUG: Start: Training
-2016-09-06 10:40:21,275 DEBUG: Info: Time for Training: 0.643486976624[s]
-2016-09-06 10:40:21,276 DEBUG: Done: Training
-2016-09-06 10:40:21,276 DEBUG: Start: Predicting
-2016-09-06 10:40:21,285 DEBUG: Done: Predicting
-2016-09-06 10:40:21,286 DEBUG: Start: Getting Results
-2016-09-06 10:40:21,287 DEBUG: Done: Getting Results
-2016-09-06 10:40:21,287 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 29, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,287 INFO: Done: Result Analysis
-2016-09-06 10:40:21,386 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,386 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:40:21,386 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:21,387 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,387 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:21,387 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:21,387 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:40:21,388 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:21,388 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:21,388 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,389 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:21,389 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:21,389 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:21,389 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,433 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:21,433 DEBUG: Start: Training
-2016-09-06 10:40:21,434 DEBUG: Info: Time for Training: 0.0487298965454[s]
-2016-09-06 10:40:21,434 DEBUG: Done: Training
-2016-09-06 10:40:21,434 DEBUG: Start: Predicting
-2016-09-06 10:40:21,446 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:21,446 DEBUG: Start: Training
-2016-09-06 10:40:21,453 DEBUG: Done: Predicting
-2016-09-06 10:40:21,453 DEBUG: Start: Getting Results
-2016-09-06 10:40:21,455 DEBUG: Done: Getting Results
-2016-09-06 10:40:21,455 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,455 INFO: Done: Result Analysis
-2016-09-06 10:40:21,468 DEBUG: Info: Time for Training: 0.0820069313049[s]
-2016-09-06 10:40:21,468 DEBUG: Done: Training
-2016-09-06 10:40:21,468 DEBUG: Start: Predicting
-2016-09-06 10:40:21,472 DEBUG: Done: Predicting
-2016-09-06 10:40:21,472 DEBUG: Start: Getting Results
-2016-09-06 10:40:21,473 DEBUG: Done: Getting Results
-2016-09-06 10:40:21,473 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,473 INFO: Done: Result Analysis
-2016-09-06 10:40:21,527 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,527 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,527 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:40:21,527 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:40:21,527 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:21,527 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:21,528 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:21,528 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 10:40:21,528 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:21,528 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 10:40:21,528 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:21,528 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:21,529 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,529 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,580 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:21,580 DEBUG: Start: Training
-2016-09-06 10:40:21,590 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:21,590 DEBUG: Start: Training
-2016-09-06 10:40:21,597 DEBUG: Info: Time for Training: 0.0706040859222[s]
-2016-09-06 10:40:21,597 DEBUG: Done: Training
-2016-09-06 10:40:21,597 DEBUG: Start: Predicting
-2016-09-06 10:40:21,603 DEBUG: Done: Predicting
-2016-09-06 10:40:21,603 DEBUG: Start: Getting Results
-2016-09-06 10:40:21,605 DEBUG: Done: Getting Results
-2016-09-06 10:40:21,605 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,605 INFO: Done: Result Analysis
-2016-09-06 10:40:21,610 DEBUG: Info: Time for Training: 0.0829930305481[s]
-2016-09-06 10:40:21,610 DEBUG: Done: Training
-2016-09-06 10:40:21,610 DEBUG: Start: Predicting
-2016-09-06 10:40:21,614 DEBUG: Done: Predicting
-2016-09-06 10:40:21,614 DEBUG: Start: Getting Results
-2016-09-06 10:40:21,615 DEBUG: Done: Getting Results
-2016-09-06 10:40:21,615 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,615 INFO: Done: Result Analysis
-2016-09-06 10:40:21,678 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,678 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:40:21,678 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:21,678 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,679 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:40:21,679 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:21,679 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:21,679 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:21,679 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:21,679 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:21,679 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:21,679 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,679 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:21,679 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,715 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:21,715 DEBUG: Start: Training
-2016-09-06 10:40:21,717 DEBUG: Info: Time for Training: 0.0389919281006[s]
-2016-09-06 10:40:21,717 DEBUG: Done: Training
-2016-09-06 10:40:21,717 DEBUG: Start: Predicting
-2016-09-06 10:40:21,720 DEBUG: Done: Predicting
-2016-09-06 10:40:21,720 DEBUG: Start: Getting Results
-2016-09-06 10:40:21,721 DEBUG: Done: Getting Results
-2016-09-06 10:40:21,721 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.814285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.814285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,721 INFO: Done: Result Analysis
-2016-09-06 10:40:21,730 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:21,730 DEBUG: Start: Training
-2016-09-06 10:40:21,734 DEBUG: Info: Time for Training: 0.056380033493[s]
-2016-09-06 10:40:21,734 DEBUG: Done: Training
-2016-09-06 10:40:21,734 DEBUG: Start: Predicting
-2016-09-06 10:40:21,737 DEBUG: Done: Predicting
-2016-09-06 10:40:21,737 DEBUG: Start: Getting Results
-2016-09-06 10:40:21,740 DEBUG: Done: Getting Results
-2016-09-06 10:40:21,740 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,741 INFO: Done: Result Analysis
-2016-09-06 10:40:21,832 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,832 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:21,833 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:40:21,833 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:40:21,833 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:21,833 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:21,834 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:21,834 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:21,834 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:21,834 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:21,835 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:21,835 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:21,835 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,835 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:21,881 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:21,882 DEBUG: Start: Training
-2016-09-06 10:40:21,883 DEBUG: Info: Time for Training: 0.0516860485077[s]
-2016-09-06 10:40:21,883 DEBUG: Done: Training
-2016-09-06 10:40:21,883 DEBUG: Start: Predicting
-2016-09-06 10:40:21,889 DEBUG: Done: Predicting
-2016-09-06 10:40:21,890 DEBUG: Start: Getting Results
-2016-09-06 10:40:21,892 DEBUG: Done: Getting Results
-2016-09-06 10:40:21,892 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.652380952381
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:21,892 INFO: Done: Result Analysis
-2016-09-06 10:40:22,343 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:22,343 DEBUG: Start: Training
-2016-09-06 10:40:22,417 DEBUG: Info: Time for Training: 0.585797071457[s]
-2016-09-06 10:40:22,417 DEBUG: Done: Training
-2016-09-06 10:40:22,417 DEBUG: Start: Predicting
-2016-09-06 10:40:22,425 DEBUG: Done: Predicting
-2016-09-06 10:40:22,425 DEBUG: Start: Getting Results
-2016-09-06 10:40:22,427 DEBUG: Done: Getting Results
-2016-09-06 10:40:22,427 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 29, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 10:40:22,427 INFO: Done: Result Analysis
-2016-09-06 10:40:22,574 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,574 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,574 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:40:22,574 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:40:22,574 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:22,574 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:22,575 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:22,575 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:22,575 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:22,575 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:22,575 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:22,575 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:22,575 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:22,575 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:22,619 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:22,619 DEBUG: Start: Training
-2016-09-06 10:40:22,620 DEBUG: Info: Time for Training: 0.0461812019348[s]
-2016-09-06 10:40:22,620 DEBUG: Done: Training
-2016-09-06 10:40:22,620 DEBUG: Start: Predicting
-2016-09-06 10:40:22,624 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:22,624 DEBUG: Start: Training
-2016-09-06 10:40:22,642 DEBUG: Done: Predicting
-2016-09-06 10:40:22,642 DEBUG: Start: Getting Results
-2016-09-06 10:40:22,643 DEBUG: Info: Time for Training: 0.0691771507263[s]
-2016-09-06 10:40:22,643 DEBUG: Done: Training
-2016-09-06 10:40:22,643 DEBUG: Start: Predicting
-2016-09-06 10:40:22,644 DEBUG: Done: Getting Results
-2016-09-06 10:40:22,644 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:40:22,644 INFO: Done: Result Analysis
-2016-09-06 10:40:22,646 DEBUG: Done: Predicting
-2016-09-06 10:40:22,647 DEBUG: Start: Getting Results
-2016-09-06 10:40:22,648 DEBUG: Done: Getting Results
-2016-09-06 10:40:22,648 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:22,648 INFO: Done: Result Analysis
-2016-09-06 10:40:22,725 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,725 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,726 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:40:22,726 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:40:22,726 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:22,726 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:22,727 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:22,727 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:22,727 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:22,727 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:22,727 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:22,727 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:22,727 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:22,727 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:22,798 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:22,798 DEBUG: Start: Training
-2016-09-06 10:40:22,807 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:22,807 DEBUG: Start: Training
-2016-09-06 10:40:22,822 DEBUG: Info: Time for Training: 0.0971689224243[s]
-2016-09-06 10:40:22,822 DEBUG: Done: Training
-2016-09-06 10:40:22,822 DEBUG: Start: Predicting
-2016-09-06 10:40:22,829 DEBUG: Done: Predicting
-2016-09-06 10:40:22,829 DEBUG: Start: Getting Results
-2016-09-06 10:40:22,831 DEBUG: Done: Getting Results
-2016-09-06 10:40:22,831 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:22,832 INFO: Done: Result Analysis
-2016-09-06 10:40:22,833 DEBUG: Info: Time for Training: 0.108348846436[s]
-2016-09-06 10:40:22,833 DEBUG: Done: Training
-2016-09-06 10:40:22,833 DEBUG: Start: Predicting
-2016-09-06 10:40:22,836 DEBUG: Done: Predicting
-2016-09-06 10:40:22,836 DEBUG: Start: Getting Results
-2016-09-06 10:40:22,837 DEBUG: Done: Getting Results
-2016-09-06 10:40:22,838 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:40:22,838 INFO: Done: Result Analysis
-2016-09-06 10:40:22,968 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,968 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:22,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:40:22,968 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:40:22,968 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:22,968 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:22,969 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:22,969 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:22,969 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:22,969 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:22,969 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:22,969 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:22,969 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:22,969 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:23,006 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:23,006 DEBUG: Start: Training
-2016-09-06 10:40:23,008 DEBUG: Info: Time for Training: 0.0404770374298[s]
-2016-09-06 10:40:23,008 DEBUG: Done: Training
-2016-09-06 10:40:23,008 DEBUG: Start: Predicting
-2016-09-06 10:40:23,010 DEBUG: Done: Predicting
-2016-09-06 10:40:23,011 DEBUG: Start: Getting Results
-2016-09-06 10:40:23,012 DEBUG: Done: Getting Results
-2016-09-06 10:40:23,012 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,012 INFO: Done: Result Analysis
-2016-09-06 10:40:23,021 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:23,021 DEBUG: Start: Training
-2016-09-06 10:40:23,025 DEBUG: Info: Time for Training: 0.0578100681305[s]
-2016-09-06 10:40:23,025 DEBUG: Done: Training
-2016-09-06 10:40:23,025 DEBUG: Start: Predicting
-2016-09-06 10:40:23,028 DEBUG: Done: Predicting
-2016-09-06 10:40:23,028 DEBUG: Start: Getting Results
-2016-09-06 10:40:23,030 DEBUG: Done: Getting Results
-2016-09-06 10:40:23,030 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,031 INFO: Done: Result Analysis
-2016-09-06 10:40:23,122 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:23,122 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:23,122 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:40:23,122 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:40:23,123 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:23,123 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:23,124 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:23,124 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:23,124 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:23,124 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:23,124 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:23,124 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:23,124 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:23,124 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:23,174 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:23,175 DEBUG: Start: Training
-2016-09-06 10:40:23,175 DEBUG: Info: Time for Training: 0.0543978214264[s]
-2016-09-06 10:40:23,176 DEBUG: Done: Training
-2016-09-06 10:40:23,176 DEBUG: Start: Predicting
-2016-09-06 10:40:23,184 DEBUG: Done: Predicting
-2016-09-06 10:40:23,184 DEBUG: Start: Getting Results
-2016-09-06 10:40:23,186 DEBUG: Done: Getting Results
-2016-09-06 10:40:23,186 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,187 INFO: Done: Result Analysis
-2016-09-06 10:40:23,643 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:23,643 DEBUG: Start: Training
-2016-09-06 10:40:23,719 DEBUG: Info: Time for Training: 0.597998142242[s]
-2016-09-06 10:40:23,719 DEBUG: Done: Training
-2016-09-06 10:40:23,719 DEBUG: Start: Predicting
-2016-09-06 10:40:23,727 DEBUG: Done: Predicting
-2016-09-06 10:40:23,727 DEBUG: Start: Getting Results
-2016-09-06 10:40:23,729 DEBUG: Done: Getting Results
-2016-09-06 10:40:23,729 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 29, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,729 INFO: Done: Result Analysis
-2016-09-06 10:40:23,867 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:23,868 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:40:23,868 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:23,869 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:23,869 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:23,869 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:23,869 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:23,873 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:23,873 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:40:23,874 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:23,875 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:23,875 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:23,875 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:23,875 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:23,915 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:23,915 DEBUG: Start: Training
-2016-09-06 10:40:23,916 DEBUG: Info: Time for Training: 0.0492420196533[s]
-2016-09-06 10:40:23,916 DEBUG: Done: Training
-2016-09-06 10:40:23,916 DEBUG: Start: Predicting
-2016-09-06 10:40:23,927 DEBUG: Done: Predicting
-2016-09-06 10:40:23,928 DEBUG: Start: Getting Results
-2016-09-06 10:40:23,929 DEBUG: Done: Getting Results
-2016-09-06 10:40:23,929 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,929 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:23,929 INFO: Done: Result Analysis
-2016-09-06 10:40:23,929 DEBUG: Start: Training
-2016-09-06 10:40:23,950 DEBUG: Info: Time for Training: 0.0777189731598[s]
-2016-09-06 10:40:23,950 DEBUG: Done: Training
-2016-09-06 10:40:23,950 DEBUG: Start: Predicting
-2016-09-06 10:40:23,953 DEBUG: Done: Predicting
-2016-09-06 10:40:23,953 DEBUG: Start: Getting Results
-2016-09-06 10:40:23,955 DEBUG: Done: Getting Results
-2016-09-06 10:40:23,955 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 10:40:23,955 INFO: Done: Result Analysis
-2016-09-06 10:40:24,016 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,016 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,016 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 10:40:24,016 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 10:40:24,016 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:24,016 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:24,017 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:24,017 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 10:40:24,017 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:24,017 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 10:40:24,017 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:24,017 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:24,017 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,017 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,065 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:24,065 DEBUG: Start: Training
-2016-09-06 10:40:24,072 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:24,072 DEBUG: Start: Training
-2016-09-06 10:40:24,082 DEBUG: Info: Time for Training: 0.066547870636[s]
-2016-09-06 10:40:24,082 DEBUG: Done: Training
-2016-09-06 10:40:24,082 DEBUG: Start: Predicting
-2016-09-06 10:40:24,088 DEBUG: Done: Predicting
-2016-09-06 10:40:24,088 DEBUG: Start: Getting Results
-2016-09-06 10:40:24,090 DEBUG: Done: Getting Results
-2016-09-06 10:40:24,090 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:24,090 INFO: Done: Result Analysis
-2016-09-06 10:40:24,093 DEBUG: Info: Time for Training: 0.0777978897095[s]
-2016-09-06 10:40:24,093 DEBUG: Done: Training
-2016-09-06 10:40:24,093 DEBUG: Start: Predicting
-2016-09-06 10:40:24,099 DEBUG: Done: Predicting
-2016-09-06 10:40:24,099 DEBUG: Start: Getting Results
-2016-09-06 10:40:24,100 DEBUG: Done: Getting Results
-2016-09-06 10:40:24,100 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 10:40:24,101 INFO: Done: Result Analysis
-2016-09-06 10:40:24,162 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,163 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 10:40:24,163 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:24,163 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,163 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 10:40:24,163 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:24,164 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:24,164 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:24,164 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:24,164 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:24,164 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:24,164 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:24,164 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,164 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,198 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:24,198 DEBUG: Start: Training
-2016-09-06 10:40:24,200 DEBUG: Info: Time for Training: 0.0375709533691[s]
-2016-09-06 10:40:24,200 DEBUG: Done: Training
-2016-09-06 10:40:24,200 DEBUG: Start: Predicting
-2016-09-06 10:40:24,202 DEBUG: Done: Predicting
-2016-09-06 10:40:24,202 DEBUG: Start: Getting Results
-2016-09-06 10:40:24,204 DEBUG: Done: Getting Results
-2016-09-06 10:40:24,204 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:40:24,204 INFO: Done: Result Analysis
-2016-09-06 10:40:24,213 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:24,213 DEBUG: Start: Training
-2016-09-06 10:40:24,216 DEBUG: Info: Time for Training: 0.0543420314789[s]
-2016-09-06 10:40:24,216 DEBUG: Done: Training
-2016-09-06 10:40:24,217 DEBUG: Start: Predicting
-2016-09-06 10:40:24,220 DEBUG: Done: Predicting
-2016-09-06 10:40:24,220 DEBUG: Start: Getting Results
-2016-09-06 10:40:24,222 DEBUG: Done: Getting Results
-2016-09-06 10:40:24,222 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 10:40:24,222 INFO: Done: Result Analysis
-2016-09-06 10:40:24,312 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,312 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:24,312 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 10:40:24,312 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 10:40:24,312 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:24,312 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:24,313 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:24,313 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:24,313 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:24,313 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:24,313 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:24,313 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:24,313 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,313 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:24,346 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:24,346 DEBUG: Start: Training
-2016-09-06 10:40:24,346 DEBUG: Info: Time for Training: 0.0354690551758[s]
-2016-09-06 10:40:24,347 DEBUG: Done: Training
-2016-09-06 10:40:24,347 DEBUG: Start: Predicting
-2016-09-06 10:40:24,352 DEBUG: Done: Predicting
-2016-09-06 10:40:24,352 DEBUG: Start: Getting Results
-2016-09-06 10:40:24,353 DEBUG: Done: Getting Results
-2016-09-06 10:40:24,353 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.67619047619
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 10:40:24,353 INFO: Done: Result Analysis
-2016-09-06 10:40:24,910 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:24,910 DEBUG: Start: Training
-2016-09-06 10:40:24,992 DEBUG: Info: Time for Training: 0.680758953094[s]
-2016-09-06 10:40:24,992 DEBUG: Done: Training
-2016-09-06 10:40:24,992 DEBUG: Start: Predicting
-2016-09-06 10:40:25,001 DEBUG: Done: Predicting
-2016-09-06 10:40:25,001 DEBUG: Start: Getting Results
-2016-09-06 10:40:25,003 DEBUG: Done: Getting Results
-2016-09-06 10:40:25,003 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 29, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:40:25,003 INFO: Done: Result Analysis
-2016-09-06 10:40:25,067 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:25,067 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 10:40:25,067 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:25,068 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:25,068 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:25,068 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 10:40:25,068 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:25,068 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 10:40:25,068 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:25,068 DEBUG: Start: Determine Train/Test split
-2016-09-06 10:40:25,069 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 10:40:25,069 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 10:40:25,069 DEBUG: Done: Determine Train/Test split
-2016-09-06 10:40:25,069 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 10:40:25,122 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:25,122 DEBUG: Start: Training
-2016-09-06 10:40:25,122 DEBUG: Done: RandomSearch best settings
-2016-09-06 10:40:25,123 DEBUG: Start: Training
-2016-09-06 10:40:25,123 DEBUG: Info: Time for Training: 0.056645154953[s]
-2016-09-06 10:40:25,123 DEBUG: Done: Training
-2016-09-06 10:40:25,123 DEBUG: Start: Predicting
-2016-09-06 10:40:25,137 DEBUG: Done: Predicting
-2016-09-06 10:40:25,137 DEBUG: Start: Getting Results
-2016-09-06 10:40:25,139 DEBUG: Done: Getting Results
-2016-09-06 10:40:25,139 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-06 10:40:25,139 INFO: Done: Result Analysis
-2016-09-06 10:40:25,140 DEBUG: Info: Time for Training: 0.0731339454651[s]
-2016-09-06 10:40:25,140 DEBUG: Done: Training
-2016-09-06 10:40:25,141 DEBUG: Start: Predicting
-2016-09-06 10:40:25,143 DEBUG: Done: Predicting
-2016-09-06 10:40:25,144 DEBUG: Start: Getting Results
-2016-09-06 10:40:25,145 DEBUG: Done: Getting Results
-2016-09-06 10:40:25,145 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.442857142857
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.442857142857
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 10:40:25,145 INFO: Done: Result Analysis
-2016-09-06 10:40:25,362 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:40:25,362 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 10:40:25,363 INFO: ### Main Programm for Multiview Classification
-2016-09-06 10:40:25,363 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 10:40:25,363 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 10:40:25,363 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 10:40:25,364 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 10:40:25,364 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 10:40:25,364 INFO: Info: Shape of View1 :(300, 7)
-2016-09-06 10:40:25,364 INFO: Info: Shape of View3 :(300, 7)
-2016-09-06 10:40:25,364 INFO: Done: Read Database Files
-2016-09-06 10:40:25,365 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:40:25,365 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 10:40:25,365 INFO: Info: Shape of View3 :(300, 7)
-2016-09-06 10:40:25,365 INFO: Done: Read Database Files
-2016-09-06 10:40:25,365 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 10:40:25,370 INFO: Done: Determine validation split
-2016-09-06 10:40:25,370 INFO: Start: Determine 5 folds
-2016-09-06 10:40:25,371 INFO: Done: Determine validation split
-2016-09-06 10:40:25,371 INFO: Start: Determine 5 folds
-2016-09-06 10:40:25,381 INFO: Info: Length of Learning Sets: 170
-2016-09-06 10:40:25,382 INFO: Info: Length of Testing Sets: 41
-2016-09-06 10:40:25,382 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:40:25,382 INFO: Done: Determine folds
-2016-09-06 10:40:25,382 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 10:40:25,382 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 10:40:25,382 DEBUG: Start: Random search for Adaboost with 30 iterations
-2016-09-06 10:40:25,383 INFO: Info: Length of Learning Sets: 170
-2016-09-06 10:40:25,383 INFO: Info: Length of Testing Sets: 41
-2016-09-06 10:40:25,383 INFO: Info: Length of Validation Set: 89
-2016-09-06 10:40:25,383 INFO: Done: Determine folds
-2016-09-06 10:40:25,383 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 10:40:25,384 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 10:40:25,384 DEBUG: Start: Gridsearch for DecisionTree on View0
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3faa796576d558f78a91ea7e1fcc884b4df78b56..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c74ef47df5a9d17b3ef6810dbfaf5c0f69306bf8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.933333333333
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.933333333333
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5e8145f8e66ad1c25e8ad743b60b7a89ccb42aec..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104020Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c2e5210e65ea676ff7ab308742343f8291ebe4fa..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0468fed69893928d9a0e609f6f265c2c932f8cdb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.814285714286
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.814285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 879e5454c0541eef300a785b6871747a3bf7b62c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.652380952381
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.652380952381
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8c5ade6564fa78ae86fb39df42234b8abf5b0899..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.990476190476
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 29, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.990476190476
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0a0d904ea21aad97c00fffecfda172877be03202..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 11b4dc01c8191d5d64ae5e5669e61ef2a9f86b9f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bc68c9fa8356059b50fa99b98d626856cb83a143..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bc4d679a3a2252b5604aaaf36013ca93d60429a7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104021Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4fe0cec465267a89c8faffefa17445874d72b701..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 29, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5b99d1af360e6d71a2416f27d48c428a2a2aba87..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0fa62be2094cdf9cf2ed8abfa1734a60de50397b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9e75865c785a0b71bcd00e7370bab5fea3e396a0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 20513448dffe5923ca34fc178ba15c868cca2989..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104022Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c9dca6930f629808ed526069e3992b0842cf0e32..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 262200771f213e3c39ec5f8692ad942a31f4a639..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9a5e6d349a402940e8e5d4c7c827f8df2e989361..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bf83d84c1f07dab88b28134ffe909e977f983cbf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 29, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 05e8489bcab51ae71edcc8c3f2ad0ee121ba488a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c1e2e693e97755134b7a4a6dc3a6f5b6e693c6a5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104023Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8bffe4939db8534a4391137578ffe51b2a204076..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f1942cf7f9990b57a25f4c3284ca51a554a3b4e8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0717704089f5c6d64549167b0332d562cbe0cb5c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.67619047619
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.67619047619
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cb100076475a0ca7317ca90c36f42e9c7b105095..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 29, max_depth : 9
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f433c885fd1da688509f9c1bac3bebca2241bd4b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d0ca9fbd591e2f11dfe0c2792c39610f1feb7946..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104024Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3a1843267da6d3a46cd3f328763f538046af018a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.585714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.585714285714
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 76add94decf94c94beb9f16d628433333aea7465..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-104025Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.442857142857
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 1181
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.442857142857
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110752-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-110752-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 16da245624e69fc841fd1ad1f6ddeca95e7511a5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110752-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,656 +0,0 @@
-2016-09-06 11:07:52,942 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:07:52,943 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000152125 Gbytes /!\
-2016-09-06 11:07:57,972 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 11:07:57,974 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 11:07:58,020 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,020 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,020 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:07:58,020 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:07:58,020 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:58,020 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:58,021 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,021 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,021 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,021 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,021 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:58,021 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:58,021 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,021 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,056 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:58,056 DEBUG: Start: Training
-2016-09-06 11:07:58,058 DEBUG: Info: Time for Training: 0.0386440753937[s]
-2016-09-06 11:07:58,058 DEBUG: Done: Training
-2016-09-06 11:07:58,058 DEBUG: Start: Predicting
-2016-09-06 11:07:58,061 DEBUG: Done: Predicting
-2016-09-06 11:07:58,061 DEBUG: Start: Getting Results
-2016-09-06 11:07:58,062 DEBUG: Done: Getting Results
-2016-09-06 11:07:58,062 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.961904761905
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.961904761905
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,063 INFO: Done: Result Analysis
-2016-09-06 11:07:58,071 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:58,071 DEBUG: Start: Training
-2016-09-06 11:07:58,075 DEBUG: Info: Time for Training: 0.0555868148804[s]
-2016-09-06 11:07:58,075 DEBUG: Done: Training
-2016-09-06 11:07:58,075 DEBUG: Start: Predicting
-2016-09-06 11:07:58,078 DEBUG: Done: Predicting
-2016-09-06 11:07:58,078 DEBUG: Start: Getting Results
-2016-09-06 11:07:58,080 DEBUG: Done: Getting Results
-2016-09-06 11:07:58,080 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,080 INFO: Done: Result Analysis
-2016-09-06 11:07:58,167 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,167 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,167 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:07:58,167 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:07:58,167 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:58,167 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:58,168 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,168 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,168 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,168 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,168 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:58,168 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:58,168 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,168 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,230 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:58,231 DEBUG: Start: Training
-2016-09-06 11:07:58,231 DEBUG: Info: Time for Training: 0.065171957016[s]
-2016-09-06 11:07:58,231 DEBUG: Done: Training
-2016-09-06 11:07:58,231 DEBUG: Start: Predicting
-2016-09-06 11:07:58,239 DEBUG: Done: Predicting
-2016-09-06 11:07:58,239 DEBUG: Start: Getting Results
-2016-09-06 11:07:58,241 DEBUG: Done: Getting Results
-2016-09-06 11:07:58,241 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,241 INFO: Done: Result Analysis
-2016-09-06 11:07:58,382 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:58,382 DEBUG: Start: Training
-2016-09-06 11:07:58,406 DEBUG: Info: Time for Training: 0.239724874496[s]
-2016-09-06 11:07:58,406 DEBUG: Done: Training
-2016-09-06 11:07:58,406 DEBUG: Start: Predicting
-2016-09-06 11:07:58,410 DEBUG: Done: Predicting
-2016-09-06 11:07:58,411 DEBUG: Start: Getting Results
-2016-09-06 11:07:58,413 DEBUG: Done: Getting Results
-2016-09-06 11:07:58,413 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 9, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,413 INFO: Done: Result Analysis
-2016-09-06 11:07:58,512 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,512 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:07:58,513 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:58,513 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,514 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,514 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:58,514 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,515 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,515 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:07:58,516 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:58,517 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,517 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,518 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:58,518 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,559 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:58,560 DEBUG: Start: Training
-2016-09-06 11:07:58,560 DEBUG: Info: Time for Training: 0.0485830307007[s]
-2016-09-06 11:07:58,560 DEBUG: Done: Training
-2016-09-06 11:07:58,560 DEBUG: Start: Predicting
-2016-09-06 11:07:58,574 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:58,574 DEBUG: Start: Training
-2016-09-06 11:07:58,576 DEBUG: Done: Predicting
-2016-09-06 11:07:58,576 DEBUG: Start: Getting Results
-2016-09-06 11:07:58,578 DEBUG: Done: Getting Results
-2016-09-06 11:07:58,578 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,579 INFO: Done: Result Analysis
-2016-09-06 11:07:58,600 DEBUG: Info: Time for Training: 0.0860497951508[s]
-2016-09-06 11:07:58,600 DEBUG: Done: Training
-2016-09-06 11:07:58,600 DEBUG: Start: Predicting
-2016-09-06 11:07:58,604 DEBUG: Done: Predicting
-2016-09-06 11:07:58,604 DEBUG: Start: Getting Results
-2016-09-06 11:07:58,606 DEBUG: Done: Getting Results
-2016-09-06 11:07:58,606 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,606 INFO: Done: Result Analysis
-2016-09-06 11:07:58,767 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,767 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:07:58,767 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:58,767 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,768 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:07:58,768 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:58,768 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,769 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,769 DEBUG: Info: Shape X_train:(210, 14), Length of y_train:210
-2016-09-06 11:07:58,769 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:58,769 DEBUG: Info: Shape X_test:(90, 14), Length of y_test:90
-2016-09-06 11:07:58,769 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,769 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:58,769 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,824 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:58,824 DEBUG: Start: Training
-2016-09-06 11:07:58,831 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:58,832 DEBUG: Start: Training
-2016-09-06 11:07:58,841 DEBUG: Info: Time for Training: 0.0746459960938[s]
-2016-09-06 11:07:58,841 DEBUG: Done: Training
-2016-09-06 11:07:58,841 DEBUG: Start: Predicting
-2016-09-06 11:07:58,846 DEBUG: Done: Predicting
-2016-09-06 11:07:58,846 DEBUG: Start: Getting Results
-2016-09-06 11:07:58,847 DEBUG: Done: Getting Results
-2016-09-06 11:07:58,847 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.388888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.388888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,848 INFO: Done: Result Analysis
-2016-09-06 11:07:58,850 DEBUG: Info: Time for Training: 0.0830399990082[s]
-2016-09-06 11:07:58,850 DEBUG: Done: Training
-2016-09-06 11:07:58,850 DEBUG: Start: Predicting
-2016-09-06 11:07:58,856 DEBUG: Done: Predicting
-2016-09-06 11:07:58,856 DEBUG: Start: Getting Results
-2016-09-06 11:07:58,857 DEBUG: Done: Getting Results
-2016-09-06 11:07:58,858 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,858 INFO: Done: Result Analysis
-2016-09-06 11:07:58,917 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,917 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:07:58,918 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:58,918 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:58,918 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:07:58,918 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:58,918 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:58,918 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:58,919 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:58,919 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,919 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:58,919 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:58,919 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:58,919 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:58,961 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:58,962 DEBUG: Start: Training
-2016-09-06 11:07:58,963 DEBUG: Info: Time for Training: 0.045686006546[s]
-2016-09-06 11:07:58,963 DEBUG: Done: Training
-2016-09-06 11:07:58,963 DEBUG: Start: Predicting
-2016-09-06 11:07:58,966 DEBUG: Done: Predicting
-2016-09-06 11:07:58,966 DEBUG: Start: Getting Results
-2016-09-06 11:07:58,967 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:58,967 DEBUG: Start: Training
-2016-09-06 11:07:58,967 DEBUG: Done: Getting Results
-2016-09-06 11:07:58,967 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.919047619048
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,968 INFO: Done: Result Analysis
-2016-09-06 11:07:58,971 DEBUG: Info: Time for Training: 0.0543868541718[s]
-2016-09-06 11:07:58,971 DEBUG: Done: Training
-2016-09-06 11:07:58,971 DEBUG: Start: Predicting
-2016-09-06 11:07:58,974 DEBUG: Done: Predicting
-2016-09-06 11:07:58,975 DEBUG: Start: Getting Results
-2016-09-06 11:07:58,977 DEBUG: Done: Getting Results
-2016-09-06 11:07:58,977 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:07:58,977 INFO: Done: Result Analysis
-2016-09-06 11:07:59,060 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,060 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:07:59,060 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,060 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:59,060 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:07:59,060 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:59,061 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,061 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,061 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,061 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:59,061 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,061 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,062 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:59,062 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,096 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:59,096 DEBUG: Start: Training
-2016-09-06 11:07:59,096 DEBUG: Info: Time for Training: 0.037360906601[s]
-2016-09-06 11:07:59,096 DEBUG: Done: Training
-2016-09-06 11:07:59,096 DEBUG: Start: Predicting
-2016-09-06 11:07:59,102 DEBUG: Done: Predicting
-2016-09-06 11:07:59,103 DEBUG: Start: Getting Results
-2016-09-06 11:07:59,104 DEBUG: Done: Getting Results
-2016-09-06 11:07:59,104 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,104 INFO: Done: Result Analysis
-2016-09-06 11:07:59,236 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:59,236 DEBUG: Start: Training
-2016-09-06 11:07:59,259 DEBUG: Info: Time for Training: 0.199690103531[s]
-2016-09-06 11:07:59,259 DEBUG: Done: Training
-2016-09-06 11:07:59,259 DEBUG: Start: Predicting
-2016-09-06 11:07:59,263 DEBUG: Done: Predicting
-2016-09-06 11:07:59,264 DEBUG: Start: Getting Results
-2016-09-06 11:07:59,265 DEBUG: Done: Getting Results
-2016-09-06 11:07:59,265 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 9, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.952380952381
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,265 INFO: Done: Result Analysis
-2016-09-06 11:07:59,404 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,404 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,405 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:07:59,405 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:07:59,405 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:59,405 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:59,405 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,405 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,405 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,405 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,405 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:59,406 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:59,406 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,406 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,451 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:59,451 DEBUG: Start: Training
-2016-09-06 11:07:59,452 DEBUG: Info: Time for Training: 0.0481390953064[s]
-2016-09-06 11:07:59,452 DEBUG: Done: Training
-2016-09-06 11:07:59,452 DEBUG: Start: Predicting
-2016-09-06 11:07:59,456 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:59,456 DEBUG: Start: Training
-2016-09-06 11:07:59,474 DEBUG: Info: Time for Training: 0.0696890354156[s]
-2016-09-06 11:07:59,474 DEBUG: Done: Training
-2016-09-06 11:07:59,474 DEBUG: Start: Predicting
-2016-09-06 11:07:59,477 DEBUG: Done: Predicting
-2016-09-06 11:07:59,477 DEBUG: Start: Getting Results
-2016-09-06 11:07:59,478 DEBUG: Done: Getting Results
-2016-09-06 11:07:59,478 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,479 INFO: Done: Result Analysis
-2016-09-06 11:07:59,482 DEBUG: Done: Predicting
-2016-09-06 11:07:59,482 DEBUG: Start: Getting Results
-2016-09-06 11:07:59,485 DEBUG: Done: Getting Results
-2016-09-06 11:07:59,485 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,485 INFO: Done: Result Analysis
-2016-09-06 11:07:59,567 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,567 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:07:59,567 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:59,568 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,568 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:07:59,568 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:59,568 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,568 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,568 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:59,568 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,568 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:07:59,569 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:07:59,569 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:07:59,569 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:07:59,618 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:59,618 DEBUG: Start: Training
-2016-09-06 11:07:59,623 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:07:59,623 DEBUG: Start: Training
-2016-09-06 11:07:59,635 DEBUG: Info: Time for Training: 0.0680291652679[s]
-2016-09-06 11:07:59,635 DEBUG: Done: Training
-2016-09-06 11:07:59,635 DEBUG: Start: Predicting
-2016-09-06 11:07:59,640 DEBUG: Done: Predicting
-2016-09-06 11:07:59,640 DEBUG: Start: Getting Results
-2016-09-06 11:07:59,641 DEBUG: Done: Getting Results
-2016-09-06 11:07:59,642 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,642 INFO: Done: Result Analysis
-2016-09-06 11:07:59,643 DEBUG: Info: Time for Training: 0.076847076416[s]
-2016-09-06 11:07:59,643 DEBUG: Done: Training
-2016-09-06 11:07:59,644 DEBUG: Start: Predicting
-2016-09-06 11:07:59,648 DEBUG: Done: Predicting
-2016-09-06 11:07:59,648 DEBUG: Start: Getting Results
-2016-09-06 11:07:59,650 DEBUG: Done: Getting Results
-2016-09-06 11:07:59,650 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:07:59,651 INFO: Done: Result Analysis
-2016-09-06 11:07:59,711 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,711 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:07:59,711 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:07:59,711 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:07:59,711 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:07:59,711 DEBUG: Start: Determine Train/Test split
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b61753b0ea9f3d0f469e7cd324869e3fd2461566..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index da9c6aad4a9409578c6966fd8a7b26a9c7f98e06..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.919047619048
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.919047619048
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9642e2c6c0dae2c9831397ab59fec2987f3dc576..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 400c77ab5c11212fa3a1fbfcbae52b4db40b275b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 9, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4fc8ecdc18f80ebebec192989f5a151bb86a1938..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.633333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 62a99e6b26e1b970b8d05f2c1aced1027eb13d87..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.538095238095
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.538095238095
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1c8ccb4cab717ee57a3fd58e2d6254ce53086bf1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.388888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.388888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d3d4db5fde9245b359f8c096ab728d97addd4723..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110758Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 14)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 80d058bdab47303cff7883b5202044d99f1f9c54..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1dc9c42a24a30081d22c7d0d56e6258bc1741dd1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.952380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 9, max_depth : 8
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.952380952381
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 48b1c2f434269ef0107709d7ae0bdc018c590ae0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.504761904762
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cc29d1a940982a8b6574ce7d367111557ea12080..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.466666666667
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ee1356b6b33dd27d79cf87fd9eb967fe183e9b0d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8d53f61e2cb44938d6e9e943984984563e72f902..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110759Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 5160
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110833-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-110833-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 80fcf978c6d655b44df2b61f4f1f898d747b03be..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110833-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,134 +0,0 @@
-2016-09-06 11:08:33,832 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:08:33,832 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00014040625 Gbytes /!\
-2016-09-06 11:08:38,847 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 11:08:38,850 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 11:08:38,908 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:08:38,908 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:08:38,908 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:08:38,908 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:08:38,909 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:08:38,909 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:08:38,909 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 11:08:38,910 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 11:08:38,910 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:08:38,910 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:08:38,910 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 11:08:38,911 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 11:08:38,911 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:08:38,911 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:08:38,951 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:08:38,952 DEBUG: Start: Training
-2016-09-06 11:08:38,954 DEBUG: Info: Time for Training: 0.0467619895935[s]
-2016-09-06 11:08:38,954 DEBUG: Done: Training
-2016-09-06 11:08:38,954 DEBUG: Start: Predicting
-2016-09-06 11:08:38,957 DEBUG: Done: Predicting
-2016-09-06 11:08:38,957 DEBUG: Start: Getting Results
-2016-09-06 11:08:38,958 DEBUG: Done: Getting Results
-2016-09-06 11:08:38,958 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 12
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:08:38,958 INFO: Done: Result Analysis
-2016-09-06 11:08:38,964 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:08:38,964 DEBUG: Start: Training
-2016-09-06 11:08:38,969 DEBUG: Info: Time for Training: 0.0624799728394[s]
-2016-09-06 11:08:38,969 DEBUG: Done: Training
-2016-09-06 11:08:38,969 DEBUG: Start: Predicting
-2016-09-06 11:08:38,972 DEBUG: Done: Predicting
-2016-09-06 11:08:38,972 DEBUG: Start: Getting Results
-2016-09-06 11:08:38,974 DEBUG: Done: Getting Results
-2016-09-06 11:08:38,974 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:08:38,974 INFO: Done: Result Analysis
-2016-09-06 11:08:39,063 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:08:39,063 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:08:39,063 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:08:39,063 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:08:39,063 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:08:39,063 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:08:39,064 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 11:08:39,064 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-06 11:08:39,064 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 11:08:39,064 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-06 11:08:39,064 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:08:39,064 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:08:39,064 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:08:39,064 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:08:39,094 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:08:39,094 DEBUG: Start: Training
-2016-09-06 11:08:39,094 DEBUG: Info: Time for Training: 0.0320620536804[s]
-2016-09-06 11:08:39,094 DEBUG: Done: Training
-2016-09-06 11:08:39,094 DEBUG: Start: Predicting
-2016-09-06 11:08:39,100 DEBUG: Done: Predicting
-2016-09-06 11:08:39,100 DEBUG: Start: Getting Results
-2016-09-06 11:08:39,101 DEBUG: Done: Getting Results
-2016-09-06 11:08:39,101 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 12
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.661904761905
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:08:39,102 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 224ed3b58e0627f5e4aa89951632a90e014a7b4a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 12, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5c22b4f6e1ae619c2d9ba16ece4a43abfa2df93b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110838Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 12
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110839Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0542160b898881703550af27a6e7b8a5575af477..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110839Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 12
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.661904761905
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110946-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-110946-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 42c0bc0393a04efd3532fdb480a94d37c93e6c81..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110946-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,134 +0,0 @@
-2016-09-06 11:09:46,919 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:09:46,919 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00013103125 Gbytes /!\
-2016-09-06 11:09:51,934 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 11:09:51,938 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 11:09:51,991 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:09:51,992 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:09:51,992 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:09:51,992 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:09:51,992 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:09:51,992 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:09:51,993 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 11:09:51,993 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 11:09:51,993 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 11:09:51,993 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 11:09:51,994 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:09:51,994 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:09:51,994 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:09:51,994 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:09:52,028 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:09:52,028 DEBUG: Start: Training
-2016-09-06 11:09:52,030 DEBUG: Info: Time for Training: 0.0394358634949[s]
-2016-09-06 11:09:52,030 DEBUG: Done: Training
-2016-09-06 11:09:52,030 DEBUG: Start: Predicting
-2016-09-06 11:09:52,033 DEBUG: Done: Predicting
-2016-09-06 11:09:52,033 DEBUG: Start: Getting Results
-2016-09-06 11:09:52,034 DEBUG: Done: Getting Results
-2016-09-06 11:09:52,034 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 22
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:09:52,035 INFO: Done: Result Analysis
-2016-09-06 11:09:52,043 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:09:52,043 DEBUG: Start: Training
-2016-09-06 11:09:52,047 DEBUG: Info: Time for Training: 0.0563831329346[s]
-2016-09-06 11:09:52,047 DEBUG: Done: Training
-2016-09-06 11:09:52,047 DEBUG: Start: Predicting
-2016-09-06 11:09:52,049 DEBUG: Done: Predicting
-2016-09-06 11:09:52,050 DEBUG: Start: Getting Results
-2016-09-06 11:09:52,051 DEBUG: Done: Getting Results
-2016-09-06 11:09:52,051 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 6, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:09:52,052 INFO: Done: Result Analysis
-2016-09-06 11:09:52,136 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:09:52,136 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:09:52,137 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:09:52,137 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:09:52,137 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:09:52,137 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:09:52,138 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 11:09:52,138 DEBUG: Info: Shape X_train:(210, 11), Length of y_train:210
-2016-09-06 11:09:52,138 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 11:09:52,138 DEBUG: Info: Shape X_test:(90, 11), Length of y_test:90
-2016-09-06 11:09:52,138 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:09:52,138 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:09:52,138 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:09:52,138 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:09:52,188 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:09:52,188 DEBUG: Start: Training
-2016-09-06 11:09:52,189 DEBUG: Info: Time for Training: 0.0529510974884[s]
-2016-09-06 11:09:52,189 DEBUG: Done: Training
-2016-09-06 11:09:52,189 DEBUG: Start: Predicting
-2016-09-06 11:09:52,198 DEBUG: Done: Predicting
-2016-09-06 11:09:52,199 DEBUG: Start: Getting Results
-2016-09-06 11:09:52,201 DEBUG: Done: Getting Results
-2016-09-06 11:09:52,201 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 29
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.642857142857
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:09:52,201 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2d4ed4e35a45e50f78b1b2b1a32e03b61cc09f1e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 6, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3f0d5254a5a3d0cd2630ee552758001a9d8aacfa..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 22
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5deeea39a1d5419c73d337620b57cc100670b565..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-110952Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 11)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 29
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.642857142857
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111029-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-111029-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 33c3b5eb361cfde75a0755fbe25e20164fba7ca0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111029-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,263 +0,0 @@
-2016-09-06 11:10:29,444 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:10:29,444 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00011228125 Gbytes /!\
-2016-09-06 11:10:34,459 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 11:10:34,463 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 11:10:34,517 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,518 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:10:34,518 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:10:34,518 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,518 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:10:34,518 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:10:34,519 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,519 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,519 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,519 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,519 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:10:34,519 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:10:34,519 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,519 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,552 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:10:34,552 DEBUG: Start: Training
-2016-09-06 11:10:34,553 DEBUG: Info: Time for Training: 0.0359370708466[s]
-2016-09-06 11:10:34,553 DEBUG: Done: Training
-2016-09-06 11:10:34,553 DEBUG: Start: Predicting
-2016-09-06 11:10:34,556 DEBUG: Done: Predicting
-2016-09-06 11:10:34,556 DEBUG: Start: Getting Results
-2016-09-06 11:10:34,557 DEBUG: Done: Getting Results
-2016-09-06 11:10:34,557 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 22
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,557 INFO: Done: Result Analysis
-2016-09-06 11:10:34,566 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:10:34,567 DEBUG: Start: Training
-2016-09-06 11:10:34,569 DEBUG: Info: Time for Training: 0.0532448291779[s]
-2016-09-06 11:10:34,570 DEBUG: Done: Training
-2016-09-06 11:10:34,570 DEBUG: Start: Predicting
-2016-09-06 11:10:34,572 DEBUG: Done: Predicting
-2016-09-06 11:10:34,572 DEBUG: Start: Getting Results
-2016-09-06 11:10:34,574 DEBUG: Done: Getting Results
-2016-09-06 11:10:34,574 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 6, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,575 INFO: Done: Result Analysis
-2016-09-06 11:10:34,658 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,658 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,658 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:10:34,658 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:10:34,658 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:10:34,658 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:10:34,659 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,659 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,659 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,659 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,659 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:10:34,659 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:10:34,659 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,659 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,689 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:10:34,689 DEBUG: Start: Training
-2016-09-06 11:10:34,690 DEBUG: Info: Time for Training: 0.0322139263153[s]
-2016-09-06 11:10:34,690 DEBUG: Done: Training
-2016-09-06 11:10:34,690 DEBUG: Start: Predicting
-2016-09-06 11:10:34,694 DEBUG: Done: Predicting
-2016-09-06 11:10:34,694 DEBUG: Start: Getting Results
-2016-09-06 11:10:34,695 DEBUG: Done: Getting Results
-2016-09-06 11:10:34,695 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.7
-accuracy_score on test : 0.388888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.7
- - Score on test : 0.388888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,696 INFO: Done: Result Analysis
-2016-09-06 11:10:34,757 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:10:34,757 DEBUG: Start: Training
-2016-09-06 11:10:34,769 DEBUG: Info: Time for Training: 0.111187934875[s]
-2016-09-06 11:10:34,769 DEBUG: Done: Training
-2016-09-06 11:10:34,769 DEBUG: Start: Predicting
-2016-09-06 11:10:34,773 DEBUG: Done: Predicting
-2016-09-06 11:10:34,773 DEBUG: Start: Getting Results
-2016-09-06 11:10:34,774 DEBUG: Done: Getting Results
-2016-09-06 11:10:34,774 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.880952380952
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 22
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.880952380952
- - Score on test : 0.377777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,775 INFO: Done: Result Analysis
-2016-09-06 11:10:34,909 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,909 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:34,909 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:10:34,909 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:10:34,909 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:10:34,909 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:10:34,910 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,910 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:34,910 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,910 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:34,910 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:10:34,910 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:10:34,911 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,911 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:10:34,957 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:10:34,957 DEBUG: Start: Training
-2016-09-06 11:10:34,958 DEBUG: Info: Time for Training: 0.0501399040222[s]
-2016-09-06 11:10:34,958 DEBUG: Done: Training
-2016-09-06 11:10:34,958 DEBUG: Start: Predicting
-2016-09-06 11:10:34,959 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:10:34,960 DEBUG: Start: Training
-2016-09-06 11:10:34,974 DEBUG: Done: Predicting
-2016-09-06 11:10:34,975 DEBUG: Start: Getting Results
-2016-09-06 11:10:34,976 DEBUG: Done: Getting Results
-2016-09-06 11:10:34,976 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,976 INFO: Done: Result Analysis
-2016-09-06 11:10:34,978 DEBUG: Info: Time for Training: 0.0697031021118[s]
-2016-09-06 11:10:34,978 DEBUG: Done: Training
-2016-09-06 11:10:34,978 DEBUG: Start: Predicting
-2016-09-06 11:10:34,981 DEBUG: Done: Predicting
-2016-09-06 11:10:34,981 DEBUG: Start: Getting Results
-2016-09-06 11:10:34,983 DEBUG: Done: Getting Results
-2016-09-06 11:10:34,983 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7236
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.471428571429
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:10:34,983 INFO: Done: Result Analysis
-2016-09-06 11:10:35,052 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:35,052 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:10:35,052 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:10:35,052 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:10:35,052 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:10:35,052 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:10:35,053 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:35,053 DEBUG: Info: Shape X_train:(210, 5), Length of y_train:210
-2016-09-06 11:10:35,053 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:35,053 DEBUG: Info: Shape X_test:(90, 5), Length of y_test:90
-2016-09-06 11:10:35,053 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:10:35,053 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:10:35,054 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:10:35,054 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:10:35,132 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:10:35,133 DEBUG: Start: Training
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1f6a86217e28d915e0e4fd6aad75d3986f67160d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.622222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 6, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8889d71ade6ec0fa4045d5b86c0f555c947976e3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 22
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cb5b09995be6069634d9dc5ccd9c7d5b9bd9bb24..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.7
-accuracy_score on test : 0.388888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 4
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.7
- - Score on test : 0.388888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4a47344770332ae6b3fac9bf4ab67fee9013b5c1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.880952380952
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 22
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.880952380952
- - Score on test : 0.377777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3d8ebb56f4334429968caa0121064a75a6bbd9b6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0ec8d3c8b30840d5c05111608e6a7aaa34c7db43..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111034Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.471428571429
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 5)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7236
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.471428571429
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111106-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-111106-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index dc730d4e9a39fb7afd4f9a18e9f9ffb5ceaf4358..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111106-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,253 +0,0 @@
-2016-09-06 11:11:06,795 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:11:06,795 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00014978125 Gbytes /!\
-2016-09-06 11:11:11,810 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 11:11:11,814 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 11:11:11,860 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:11,860 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:11,860 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:11:11,860 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:11:11,860 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:11:11,860 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:11:11,861 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:11,861 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:11,861 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:11,861 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:11,861 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:11:11,861 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:11:11,861 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:11:11,861 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:11:11,894 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:11:11,894 DEBUG: Start: Training
-2016-09-06 11:11:11,896 DEBUG: Info: Time for Training: 0.036553144455[s]
-2016-09-06 11:11:11,896 DEBUG: Done: Training
-2016-09-06 11:11:11,896 DEBUG: Start: Predicting
-2016-09-06 11:11:11,898 DEBUG: Done: Predicting
-2016-09-06 11:11:11,898 DEBUG: Start: Getting Results
-2016-09-06 11:11:11,900 DEBUG: Done: Getting Results
-2016-09-06 11:11:11,900 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:11:11,900 INFO: Done: Result Analysis
-2016-09-06 11:11:11,909 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:11:11,909 DEBUG: Start: Training
-2016-09-06 11:11:11,912 DEBUG: Info: Time for Training: 0.0533759593964[s]
-2016-09-06 11:11:11,913 DEBUG: Done: Training
-2016-09-06 11:11:11,913 DEBUG: Start: Predicting
-2016-09-06 11:11:11,915 DEBUG: Done: Predicting
-2016-09-06 11:11:11,916 DEBUG: Start: Getting Results
-2016-09-06 11:11:11,917 DEBUG: Done: Getting Results
-2016-09-06 11:11:11,917 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:11:11,918 INFO: Done: Result Analysis
-2016-09-06 11:11:12,007 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,007 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,007 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:11:12,007 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:11:12,007 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:11:12,007 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:11:12,008 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:12,008 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:12,008 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:12,008 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:12,008 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:11:12,008 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:11:12,008 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:11:12,008 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:11:12,040 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:11:12,041 DEBUG: Start: Training
-2016-09-06 11:11:12,041 DEBUG: Info: Time for Training: 0.034646987915[s]
-2016-09-06 11:11:12,041 DEBUG: Done: Training
-2016-09-06 11:11:12,041 DEBUG: Start: Predicting
-2016-09-06 11:11:12,047 DEBUG: Done: Predicting
-2016-09-06 11:11:12,047 DEBUG: Start: Getting Results
-2016-09-06 11:11:12,049 DEBUG: Done: Getting Results
-2016-09-06 11:11:12,049 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:11:12,049 INFO: Done: Result Analysis
-2016-09-06 11:11:12,172 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:11:12,172 DEBUG: Start: Training
-2016-09-06 11:11:12,193 DEBUG: Info: Time for Training: 0.186748981476[s]
-2016-09-06 11:11:12,193 DEBUG: Done: Training
-2016-09-06 11:11:12,193 DEBUG: Start: Predicting
-2016-09-06 11:11:12,197 DEBUG: Done: Predicting
-2016-09-06 11:11:12,198 DEBUG: Start: Getting Results
-2016-09-06 11:11:12,199 DEBUG: Done: Getting Results
-2016-09-06 11:11:12,199 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:11:12,199 INFO: Done: Result Analysis
-2016-09-06 11:11:12,253 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,253 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,253 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:11:12,253 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:11:12,253 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:11:12,253 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:11:12,254 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:12,254 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-06 11:11:12,254 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:12,254 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-06 11:11:12,254 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:11:12,254 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:11:12,254 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:11:12,254 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:11:12,306 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:11:12,306 DEBUG: Start: Training
-2016-09-06 11:11:12,317 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:11:12,317 DEBUG: Start: Training
-2016-09-06 11:11:12,318 DEBUG: Info: Time for Training: 0.0656809806824[s]
-2016-09-06 11:11:12,318 DEBUG: Done: Training
-2016-09-06 11:11:12,318 DEBUG: Start: Predicting
-2016-09-06 11:11:12,324 DEBUG: Info: Time for Training: 0.071897983551[s]
-2016-09-06 11:11:12,324 DEBUG: Done: Training
-2016-09-06 11:11:12,324 DEBUG: Start: Predicting
-2016-09-06 11:11:12,328 DEBUG: Done: Predicting
-2016-09-06 11:11:12,328 DEBUG: Start: Getting Results
-2016-09-06 11:11:12,329 DEBUG: Done: Getting Results
-2016-09-06 11:11:12,329 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3624
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-06 11:11:12,330 INFO: Done: Result Analysis
-2016-09-06 11:11:12,335 DEBUG: Done: Predicting
-2016-09-06 11:11:12,335 DEBUG: Start: Getting Results
-2016-09-06 11:11:12,338 DEBUG: Done: Getting Results
-2016-09-06 11:11:12,338 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:11:12,338 INFO: Done: Result Analysis
-2016-09-06 11:11:12,399 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,399 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:11:12,399 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:11:12,399 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:11:12,400 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:11:12,400 DEBUG: Start: Determine Train/Test split
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 07969b8c8662925693b4cd3a135952e73b211181..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 14, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 560ae93efef353c6a619db2bcb37f0af52d9b661..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111111Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ff064c80c123c5050d49724e3d9f91eb975ba1dc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 40
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6f837cad5366a7a8bbc865772302b7df1e3d9822..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.971428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 8, max_depth : 27
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 909d20c0ec93201ecdee713284d808c20afd4d72..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.62380952381
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.62380952381
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b2fe01c37560e0d52dabf97fac764534ae9abd96..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111112Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,24 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 3624
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-111203-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-111203-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 71be916ac87d043785dae27b35e59a7f42ad6620..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-111203-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,32 +0,0 @@
-2016-09-06 11:12:03,520 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:12:03,520 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00015446875 Gbytes /!\
-2016-09-06 11:12:08,535 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 11:12:08,539 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 11:12:08,590 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:12:08,590 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:12:08,590 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:12:08,591 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:12:08,591 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:12:08,591 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:12:08,592 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 11:12:08,592 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-06 11:12:08,592 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 11:12:08,592 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-06 11:12:08,592 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:12:08,592 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:12:08,592 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:12:08,592 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:12:08,648 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:12:08,648 DEBUG: Start: Training
-2016-09-06 11:12:08,652 DEBUG: Info: Time for Training: 0.0629100799561[s]
-2016-09-06 11:12:08,653 DEBUG: Done: Training
-2016-09-06 11:12:08,653 DEBUG: Start: Predicting
-2016-09-06 11:12:08,655 DEBUG: Done: Predicting
-2016-09-06 11:12:08,655 DEBUG: Start: Getting Results
-2016-09-06 11:12:08,656 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:12:08,656 DEBUG: Start: Training
-2016-09-06 11:12:08,659 DEBUG: Info: Time for Training: 0.0693309307098[s]
-2016-09-06 11:12:08,659 DEBUG: Done: Training
-2016-09-06 11:12:08,659 DEBUG: Start: Predicting
-2016-09-06 11:12:08,662 DEBUG: Done: Predicting
-2016-09-06 11:12:08,662 DEBUG: Start: Getting Results
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112150-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-112150-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 5f0724c6443589f2e3804127a17e4a946a479810..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112150-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2150 +0,0 @@
-2016-09-06 11:21:50,451 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 11:21:50,452 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.000124 Gbytes /!\
-2016-09-06 11:21:55,466 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 11:21:55,470 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 11:21:55,524 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:55,525 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:21:55,525 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:55,525 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:55,525 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:21:55,525 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:55,526 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:55,526 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:55,526 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:55,526 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:55,526 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:55,526 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:55,526 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:55,526 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:55,561 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:55,561 DEBUG: Start: Training
-2016-09-06 11:21:55,563 DEBUG: Info: Time for Training: 0.0388250350952[s]
-2016-09-06 11:21:55,563 DEBUG: Done: Training
-2016-09-06 11:21:55,563 DEBUG: Start: Predicting
-2016-09-06 11:21:55,565 DEBUG: Done: Predicting
-2016-09-06 11:21:55,565 DEBUG: Start: Getting Results
-2016-09-06 11:21:55,575 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:55,575 DEBUG: Start: Training
-2016-09-06 11:21:55,579 DEBUG: Info: Time for Training: 0.0551791191101[s]
-2016-09-06 11:21:55,579 DEBUG: Done: Training
-2016-09-06 11:21:55,579 DEBUG: Start: Predicting
-2016-09-06 11:21:55,582 DEBUG: Done: Predicting
-2016-09-06 11:21:55,582 DEBUG: Start: Getting Results
-2016-09-06 11:21:55,604 DEBUG: Done: Getting Results
-2016-09-06 11:21:55,605 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.413793103448
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.413793103448
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.131912640639
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.391304347826
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.439024390244
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433797909408
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:55,605 INFO: Done: Result Analysis
-2016-09-06 11:21:55,619 DEBUG: Done: Getting Results
-2016-09-06 11:21:55,619 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.494382022472
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.494382022472
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.5
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.00596274193664
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.458333333333
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.536585365854
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.502986560478
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:21:55,619 INFO: Done: Result Analysis
-2016-09-06 11:21:55,774 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:55,774 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:55,774 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:21:55,774 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:21:55,774 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:55,774 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:55,774 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:55,774 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:55,775 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:55,775 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:55,775 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:55,775 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:55,775 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:55,775 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:55,806 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:55,806 DEBUG: Start: Training
-2016-09-06 11:21:55,807 DEBUG: Info: Time for Training: 0.0337290763855[s]
-2016-09-06 11:21:55,807 DEBUG: Done: Training
-2016-09-06 11:21:55,807 DEBUG: Start: Predicting
-2016-09-06 11:21:55,814 DEBUG: Done: Predicting
-2016-09-06 11:21:55,814 DEBUG: Start: Getting Results
-2016-09-06 11:21:55,852 DEBUG: Done: Getting Results
-2016-09-06 11:21:55,852 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 47
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.550724637681
- - Score on test : 0.315789473684
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.550724637681
- - Score on test : 0.315789473684
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.442857142857
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.11473701202
- - Score on test : -0.180519041032
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.564356435644
- - Score on test : 0.342857142857
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.537735849057
- - Score on test : 0.292682926829
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.55732946299
- - Score on test : 0.411647585864
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.442857142857
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:21:55,852 INFO: Done: Result Analysis
-2016-09-06 11:21:55,945 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:55,946 DEBUG: Start: Training
-2016-09-06 11:21:55,964 DEBUG: Info: Time for Training: 0.190694093704[s]
-2016-09-06 11:21:55,964 DEBUG: Done: Training
-2016-09-06 11:21:55,964 DEBUG: Start: Predicting
-2016-09-06 11:21:55,968 DEBUG: Done: Predicting
-2016-09-06 11:21:55,968 DEBUG: Start: Getting Results
-2016-09-06 11:21:55,996 DEBUG: Done: Getting Results
-2016-09-06 11:21:55,996 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.957142857143
- - Score on test : 0.4
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.956937799043
- - Score on test : 0.325
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.956937799043
- - Score on test : 0.325
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0428571428571
- - Score on test : 0.6
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.957142857143
- - Score on test : 0.4
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.914674537841
- - Score on test : -0.214609988978
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.970873786408
- - Score on test : 0.333333333333
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.943396226415
- - Score on test : 0.317073170732
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.957275036284
- - Score on test : 0.393230462917
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0428571428571
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-06 11:21:55,997 INFO: Done: Result Analysis
-2016-09-06 11:21:56,124 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,125 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:21:56,125 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,125 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:56,125 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:21:56,125 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:56,125 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:56,125 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:56,126 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:56,126 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:56,126 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:56,126 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:56,126 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,126 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,173 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:56,173 DEBUG: Start: Training
-2016-09-06 11:21:56,174 DEBUG: Info: Time for Training: 0.0500919818878[s]
-2016-09-06 11:21:56,174 DEBUG: Done: Training
-2016-09-06 11:21:56,174 DEBUG: Start: Predicting
-2016-09-06 11:21:56,181 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:56,181 DEBUG: Start: Training
-2016-09-06 11:21:56,185 DEBUG: Done: Predicting
-2016-09-06 11:21:56,185 DEBUG: Start: Getting Results
-2016-09-06 11:21:56,202 DEBUG: Info: Time for Training: 0.0777740478516[s]
-2016-09-06 11:21:56,202 DEBUG: Done: Training
-2016-09-06 11:21:56,202 DEBUG: Start: Predicting
-2016-09-06 11:21:56,206 DEBUG: Done: Predicting
-2016-09-06 11:21:56,206 DEBUG: Start: Getting Results
-2016-09-06 11:21:56,213 DEBUG: Done: Getting Results
-2016-09-06 11:21:56,213 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.433333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.62443438914
- - Score on test : 0.43956043956
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.62443438914
- - Score on test : 0.43956043956
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.395238095238
- - Score on test : 0.566666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.433333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.209578877963
- - Score on test : -0.124719695673
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.4
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.650943396226
- - Score on test : 0.487804878049
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.604317851959
- - Score on test : 0.437779990045
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.395238095238
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,214 INFO: Done: Result Analysis
-2016-09-06 11:21:56,250 DEBUG: Done: Getting Results
-2016-09-06 11:21:56,251 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.504672897196
- - Score on test : 0.404761904762
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.504672897196
- - Score on test : 0.404761904762
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.00980036362201
- - Score on test : -0.115633266975
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.395348837209
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.509433962264
- - Score on test : 0.414634146341
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.495101596517
- - Score on test : 0.442010950722
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,251 INFO: Done: Result Analysis
-2016-09-06 11:21:56,375 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,375 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,376 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:21:56,376 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:21:56,376 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:56,376 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:56,377 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:56,377 DEBUG: Info: Shape X_train:(210, 13), Length of y_train:210
-2016-09-06 11:21:56,377 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:56,377 DEBUG: Info: Shape X_test:(90, 13), Length of y_test:90
-2016-09-06 11:21:56,377 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:56,377 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:56,377 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,377 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,447 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:56,448 DEBUG: Start: Training
-2016-09-06 11:21:56,458 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:56,458 DEBUG: Start: Training
-2016-09-06 11:21:56,471 DEBUG: Info: Time for Training: 0.0959920883179[s]
-2016-09-06 11:21:56,471 DEBUG: Done: Training
-2016-09-06 11:21:56,471 DEBUG: Start: Predicting
-2016-09-06 11:21:56,479 DEBUG: Done: Predicting
-2016-09-06 11:21:56,479 DEBUG: Start: Getting Results
-2016-09-06 11:21:56,482 DEBUG: Info: Time for Training: 0.107470989227[s]
-2016-09-06 11:21:56,482 DEBUG: Done: Training
-2016-09-06 11:21:56,483 DEBUG: Start: Predicting
-2016-09-06 11:21:56,488 DEBUG: Done: Predicting
-2016-09-06 11:21:56,488 DEBUG: Start: Getting Results
-2016-09-06 11:21:56,517 DEBUG: Done: Getting Results
-2016-09-06 11:21:56,517 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.345679012346
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.345679012346
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.588888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.189573937423
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.35
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.341463414634
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.405425584868
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,518 INFO: Done: Result Analysis
-2016-09-06 11:21:56,536 DEBUG: Done: Getting Results
-2016-09-06 11:21:56,537 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.150943396226
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.150943396226
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.5
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.096260040145
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0975609756098
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.467147834744
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,537 INFO: Done: Result Analysis
-2016-09-06 11:21:56,625 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,625 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,625 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:21:56,625 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:21:56,626 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:56,626 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:56,626 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:56,626 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:56,626 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:56,626 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:56,626 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:56,626 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:56,626 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,626 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,664 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:56,664 DEBUG: Start: Training
-2016-09-06 11:21:56,666 DEBUG: Info: Time for Training: 0.0415709018707[s]
-2016-09-06 11:21:56,666 DEBUG: Done: Training
-2016-09-06 11:21:56,666 DEBUG: Start: Predicting
-2016-09-06 11:21:56,669 DEBUG: Done: Predicting
-2016-09-06 11:21:56,669 DEBUG: Start: Getting Results
-2016-09-06 11:21:56,678 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:56,678 DEBUG: Start: Training
-2016-09-06 11:21:56,684 DEBUG: Info: Time for Training: 0.059385061264[s]
-2016-09-06 11:21:56,684 DEBUG: Done: Training
-2016-09-06 11:21:56,684 DEBUG: Start: Predicting
-2016-09-06 11:21:56,688 DEBUG: Done: Predicting
-2016-09-06 11:21:56,688 DEBUG: Start: Getting Results
-2016-09-06 11:21:56,718 DEBUG: Done: Getting Results
-2016-09-06 11:21:56,718 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.418604651163
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.418604651163
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.111552687063
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.439024390244
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.44400199104
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,719 INFO: Done: Result Analysis
-2016-09-06 11:21:56,731 DEBUG: Done: Getting Results
-2016-09-06 11:21:56,731 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.404761904762
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.404761904762
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.115633266975
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.395348837209
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.414634146341
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.442010950722
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,731 INFO: Done: Result Analysis
-2016-09-06 11:21:56,878 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,878 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:21:56,878 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:56,879 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:56,879 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:56,879 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:56,879 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,879 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:56,880 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:21:56,880 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:56,880 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:56,881 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:56,881 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:56,881 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:56,909 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:56,909 DEBUG: Start: Training
-2016-09-06 11:21:56,910 DEBUG: Info: Time for Training: 0.032821893692[s]
-2016-09-06 11:21:56,910 DEBUG: Done: Training
-2016-09-06 11:21:56,910 DEBUG: Start: Predicting
-2016-09-06 11:21:56,917 DEBUG: Done: Predicting
-2016-09-06 11:21:56,918 DEBUG: Start: Getting Results
-2016-09-06 11:21:56,972 DEBUG: Done: Getting Results
-2016-09-06 11:21:56,973 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 47
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.633204633205
- - Score on test : 0.558558558559
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.633204633205
- - Score on test : 0.558558558559
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.452380952381
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.102191547553
- - Score on test : -0.0477019354931
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.535947712418
- - Score on test : 0.442857142857
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.77358490566
- - Score on test : 0.756097560976
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.545446298984
- - Score on test : 0.480089596814
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.452380952381
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:21:56,973 INFO: Done: Result Analysis
-2016-09-06 11:21:57,059 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:57,059 DEBUG: Start: Training
-2016-09-06 11:21:57,082 DEBUG: Info: Time for Training: 0.203009128571[s]
-2016-09-06 11:21:57,082 DEBUG: Done: Training
-2016-09-06 11:21:57,082 DEBUG: Start: Predicting
-2016-09-06 11:21:57,088 DEBUG: Done: Predicting
-2016-09-06 11:21:57,088 DEBUG: Start: Getting Results
-2016-09-06 11:21:57,136 DEBUG: Done: Getting Results
-2016-09-06 11:21:57,136 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.976525821596
- - Score on test : 0.516853932584
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.976525821596
- - Score on test : 0.516853932584
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0238095238095
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.952415522541
- - Score on test : 0.0506833064614
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.971962616822
- - Score on test : 0.479166666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.981132075472
- - Score on test : 0.560975609756
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.976142960813
- - Score on test : 0.525385764062
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0238095238095
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,136 INFO: Done: Result Analysis
-2016-09-06 11:21:57,224 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,224 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:21:57,224 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,225 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:57,225 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:21:57,225 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:57,225 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:57,225 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:57,225 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:57,225 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:57,225 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:57,226 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:57,226 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,226 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,271 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:57,271 DEBUG: Start: Training
-2016-09-06 11:21:57,272 DEBUG: Info: Time for Training: 0.0480880737305[s]
-2016-09-06 11:21:57,272 DEBUG: Done: Training
-2016-09-06 11:21:57,272 DEBUG: Start: Predicting
-2016-09-06 11:21:57,281 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:57,281 DEBUG: Start: Training
-2016-09-06 11:21:57,288 DEBUG: Done: Predicting
-2016-09-06 11:21:57,289 DEBUG: Start: Getting Results
-2016-09-06 11:21:57,310 DEBUG: Info: Time for Training: 0.0865099430084[s]
-2016-09-06 11:21:57,311 DEBUG: Done: Training
-2016-09-06 11:21:57,311 DEBUG: Start: Predicting
-2016-09-06 11:21:57,315 DEBUG: Done: Predicting
-2016-09-06 11:21:57,315 DEBUG: Start: Getting Results
-2016-09-06 11:21:57,316 DEBUG: Done: Getting Results
-2016-09-06 11:21:57,316 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.564593301435
- - Score on test : 0.516853932584
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.564593301435
- - Score on test : 0.516853932584
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.433333333333
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.133545022783
- - Score on test : 0.0506833064614
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.572815533981
- - Score on test : 0.479166666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.556603773585
- - Score on test : 0.560975609756
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.566763425254
- - Score on test : 0.525385764062
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.433333333333
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,316 INFO: Done: Result Analysis
-2016-09-06 11:21:57,383 DEBUG: Done: Getting Results
-2016-09-06 11:21:57,383 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.535211267606
- - Score on test : 0.475
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.535211267606
- - Score on test : 0.475
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.471428571429
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0569743711331
- - Score on test : 0.0555284586866
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.532710280374
- - Score on test : 0.487179487179
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.537735849057
- - Score on test : 0.463414634146
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.528483309144
- - Score on test : 0.52762568442
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.471428571429
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,383 INFO: Done: Result Analysis
-2016-09-06 11:21:57,473 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,473 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,474 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:21:57,474 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:21:57,474 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:57,474 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:57,475 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:57,475 DEBUG: Info: Shape X_train:(210, 18), Length of y_train:210
-2016-09-06 11:21:57,475 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:57,475 DEBUG: Info: Shape X_test:(90, 18), Length of y_test:90
-2016-09-06 11:21:57,475 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:57,475 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:57,475 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,475 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,549 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:57,549 DEBUG: Start: Training
-2016-09-06 11:21:57,561 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:57,561 DEBUG: Start: Training
-2016-09-06 11:21:57,573 DEBUG: Info: Time for Training: 0.100679159164[s]
-2016-09-06 11:21:57,574 DEBUG: Done: Training
-2016-09-06 11:21:57,574 DEBUG: Start: Predicting
-2016-09-06 11:21:57,582 DEBUG: Done: Predicting
-2016-09-06 11:21:57,582 DEBUG: Start: Getting Results
-2016-09-06 11:21:57,590 DEBUG: Info: Time for Training: 0.117436170578[s]
-2016-09-06 11:21:57,590 DEBUG: Done: Training
-2016-09-06 11:21:57,590 DEBUG: Start: Predicting
-2016-09-06 11:21:57,596 DEBUG: Done: Predicting
-2016-09-06 11:21:57,597 DEBUG: Start: Getting Results
-2016-09-06 11:21:57,627 DEBUG: Done: Getting Results
-2016-09-06 11:21:57,627 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511627906977
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511627906977
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.066931612238
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.536585365854
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533598805376
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,627 INFO: Done: Result Analysis
-2016-09-06 11:21:57,645 DEBUG: Done: Getting Results
-2016-09-06 11:21:57,645 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.561403508772
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.561403508772
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0715653145323
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.438356164384
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.780487804878
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4718765555
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,645 INFO: Done: Result Analysis
-2016-09-06 11:21:57,719 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,719 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:21:57,719 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,719 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:57,719 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:21:57,719 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:57,720 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:57,720 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:57,720 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:57,720 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:57,720 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:57,720 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:57,720 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,720 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,753 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:57,753 DEBUG: Start: Training
-2016-09-06 11:21:57,755 DEBUG: Info: Time for Training: 0.0360541343689[s]
-2016-09-06 11:21:57,755 DEBUG: Done: Training
-2016-09-06 11:21:57,755 DEBUG: Start: Predicting
-2016-09-06 11:21:57,757 DEBUG: Done: Predicting
-2016-09-06 11:21:57,757 DEBUG: Start: Getting Results
-2016-09-06 11:21:57,767 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:57,767 DEBUG: Start: Training
-2016-09-06 11:21:57,771 DEBUG: Info: Time for Training: 0.052619934082[s]
-2016-09-06 11:21:57,771 DEBUG: Done: Training
-2016-09-06 11:21:57,771 DEBUG: Start: Predicting
-2016-09-06 11:21:57,774 DEBUG: Done: Predicting
-2016-09-06 11:21:57,774 DEBUG: Start: Getting Results
-2016-09-06 11:21:57,802 DEBUG: Done: Getting Results
-2016-09-06 11:21:57,802 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.995305164319
- - Score on test : 0.511627906977
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.995305164319
- - Score on test : 0.511627906977
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.990519401324
- - Score on test : 0.066931612238
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.990654205607
- - Score on test : 0.488888888889
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.536585365854
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.995192307692
- - Score on test : 0.533598805376
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,803 INFO: Done: Result Analysis
-2016-09-06 11:21:57,820 DEBUG: Done: Getting Results
-2016-09-06 11:21:57,820 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.444444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.104031856645
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.552015928323
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
-2016-09-06 11:21:57,820 INFO: Done: Result Analysis
-2016-09-06 11:21:57,970 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,970 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:57,970 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:21:57,970 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:21:57,970 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:57,970 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:57,971 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:57,971 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:57,971 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:57,971 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:57,971 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:57,971 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:57,971 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:57,971 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,004 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:58,004 DEBUG: Start: Training
-2016-09-06 11:21:58,005 DEBUG: Info: Time for Training: 0.0356760025024[s]
-2016-09-06 11:21:58,005 DEBUG: Done: Training
-2016-09-06 11:21:58,005 DEBUG: Start: Predicting
-2016-09-06 11:21:58,013 DEBUG: Done: Predicting
-2016-09-06 11:21:58,013 DEBUG: Start: Getting Results
-2016-09-06 11:21:58,054 DEBUG: Done: Getting Results
-2016-09-06 11:21:58,054 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 47
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.477777777778
- - Score on test : 0.342105263158
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.477777777778
- - Score on test : 0.342105263158
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.447619047619
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.112597765671
- - Score on test : -0.134753650348
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.581081081081
- - Score on test : 0.371428571429
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.405660377358
- - Score on test : 0.317073170732
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.553791727141
- - Score on test : 0.434046789447
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.447619047619
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,054 INFO: Done: Result Analysis
-2016-09-06 11:21:58,134 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:58,134 DEBUG: Start: Training
-2016-09-06 11:21:58,151 DEBUG: Info: Time for Training: 0.181129932404[s]
-2016-09-06 11:21:58,151 DEBUG: Done: Training
-2016-09-06 11:21:58,151 DEBUG: Start: Predicting
-2016-09-06 11:21:58,154 DEBUG: Done: Predicting
-2016-09-06 11:21:58,155 DEBUG: Start: Getting Results
-2016-09-06 11:21:58,182 DEBUG: Done: Getting Results
-2016-09-06 11:21:58,182 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.96682464455
- - Score on test : 0.53164556962
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.96682464455
- - Score on test : 0.53164556962
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0333333333333
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.933375664255
- - Score on test : 0.166630556676
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.552631578947
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.962264150943
- - Score on test : 0.512195121951
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.966708998549
- - Score on test : 0.582628173221
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0333333333333
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,182 INFO: Done: Result Analysis
-2016-09-06 11:21:58,321 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,321 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:21:58,321 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,321 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:58,321 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:21:58,322 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:58,322 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:58,322 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:58,322 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:58,322 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:58,323 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:58,323 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:58,323 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,323 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,392 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:58,392 DEBUG: Start: Training
-2016-09-06 11:21:58,393 DEBUG: Info: Time for Training: 0.0729160308838[s]
-2016-09-06 11:21:58,393 DEBUG: Done: Training
-2016-09-06 11:21:58,393 DEBUG: Start: Predicting
-2016-09-06 11:21:58,396 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:58,396 DEBUG: Start: Training
-2016-09-06 11:21:58,406 DEBUG: Done: Predicting
-2016-09-06 11:21:58,406 DEBUG: Start: Getting Results
-2016-09-06 11:21:58,415 DEBUG: Info: Time for Training: 0.095123052597[s]
-2016-09-06 11:21:58,416 DEBUG: Done: Training
-2016-09-06 11:21:58,416 DEBUG: Start: Predicting
-2016-09-06 11:21:58,419 DEBUG: Done: Predicting
-2016-09-06 11:21:58,419 DEBUG: Start: Getting Results
-2016-09-06 11:21:58,431 DEBUG: Done: Getting Results
-2016-09-06 11:21:58,431 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.5
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.544554455446
- - Score on test : 0.470588235294
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.544554455446
- - Score on test : 0.470588235294
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.438095238095
- - Score on test : 0.5
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.5
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.125091870983
- - Score on test : -0.00198364873142
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.572916666667
- - Score on test : 0.454545454545
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.518867924528
- - Score on test : 0.487804878049
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.562318577649
- - Score on test : 0.499004479841
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.438095238095
- - Score on test : 0.5
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,431 INFO: Done: Result Analysis
-2016-09-06 11:21:58,448 DEBUG: Done: Getting Results
-2016-09-06 11:21:58,448 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.633333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.51376146789
- - Score on test : 0.637362637363
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.51376146789
- - Score on test : 0.637362637363
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.366666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.633333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.0101818424163
- - Score on test : 0.279372118308
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.58
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.528301886792
- - Score on test : 0.707317073171
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.494920174165
- - Score on test : 0.6393728223
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.366666666667
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,449 INFO: Done: Result Analysis
-2016-09-06 11:21:58,568 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,568 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,568 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-06 11:21:58,568 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-06 11:21:58,568 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:58,568 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:58,569 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:58,569 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-06 11:21:58,569 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:58,569 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-06 11:21:58,569 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:58,569 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:58,569 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,569 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,614 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:58,614 DEBUG: Start: Training
-2016-09-06 11:21:58,619 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:58,619 DEBUG: Start: Training
-2016-09-06 11:21:58,632 DEBUG: Info: Time for Training: 0.064493894577[s]
-2016-09-06 11:21:58,632 DEBUG: Done: Training
-2016-09-06 11:21:58,632 DEBUG: Start: Predicting
-2016-09-06 11:21:58,637 DEBUG: Done: Predicting
-2016-09-06 11:21:58,637 DEBUG: Start: Getting Results
-2016-09-06 11:21:58,638 DEBUG: Info: Time for Training: 0.0703361034393[s]
-2016-09-06 11:21:58,638 DEBUG: Done: Training
-2016-09-06 11:21:58,638 DEBUG: Start: Predicting
-2016-09-06 11:21:58,641 DEBUG: Done: Predicting
-2016-09-06 11:21:58,641 DEBUG: Start: Getting Results
-2016-09-06 11:21:58,668 DEBUG: Done: Getting Results
-2016-09-06 11:21:58,668 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0592334494774
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.428571428571
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.470383275261
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,668 INFO: Done: Result Analysis
-2016-09-06 11:21:58,680 DEBUG: Done: Getting Results
-2016-09-06 11:21:58,680 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.995260663507
- - Score on test : 0.479166666667
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.995260663507
- - Score on test : 0.479166666667
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.990521113087
- - Score on test : -0.0940733030728
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.418181818182
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.990566037736
- - Score on test : 0.560975609756
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.995283018868
- - Score on test : 0.453957192633
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,680 INFO: Done: Result Analysis
-2016-09-06 11:21:58,822 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,822 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:58,822 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-06 11:21:58,822 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-06 11:21:58,822 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:58,822 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:58,823 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:58,823 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:58,823 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:58,823 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:58,823 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:58,823 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:58,823 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,823 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:58,874 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:58,874 DEBUG: Start: Training
-2016-09-06 11:21:58,875 DEBUG: Info: Time for Training: 0.0545539855957[s]
-2016-09-06 11:21:58,876 DEBUG: Done: Training
-2016-09-06 11:21:58,876 DEBUG: Start: Predicting
-2016-09-06 11:21:58,880 DEBUG: Done: Predicting
-2016-09-06 11:21:58,880 DEBUG: Start: Getting Results
-2016-09-06 11:21:58,899 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:58,899 DEBUG: Start: Training
-2016-09-06 11:21:58,905 DEBUG: Info: Time for Training: 0.0836429595947[s]
-2016-09-06 11:21:58,905 DEBUG: Done: Training
-2016-09-06 11:21:58,905 DEBUG: Start: Predicting
-2016-09-06 11:21:58,908 DEBUG: Done: Predicting
-2016-09-06 11:21:58,908 DEBUG: Start: Getting Results
-2016-09-06 11:21:58,930 DEBUG: Done: Getting Results
-2016-09-06 11:21:58,930 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.428571428571
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.428571428571
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0709680565554
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.418604651163
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.439024390244
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.464410154306
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,931 INFO: Done: Result Analysis
-2016-09-06 11:21:58,947 DEBUG: Done: Getting Results
-2016-09-06 11:21:58,947 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.404761904762
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.404761904762
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.115633266975
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.395348837209
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.414634146341
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.442010950722
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-06 11:21:58,947 INFO: Done: Result Analysis
-2016-09-06 11:21:59,070 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:59,070 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:59,070 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-06 11:21:59,070 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-06 11:21:59,070 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:59,070 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:59,071 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:59,071 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:59,071 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:59,071 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:59,071 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:59,071 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:59,071 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:59,071 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:59,101 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:59,102 DEBUG: Start: Training
-2016-09-06 11:21:59,102 DEBUG: Info: Time for Training: 0.0325701236725[s]
-2016-09-06 11:21:59,102 DEBUG: Done: Training
-2016-09-06 11:21:59,102 DEBUG: Start: Predicting
-2016-09-06 11:21:59,108 DEBUG: Done: Predicting
-2016-09-06 11:21:59,108 DEBUG: Start: Getting Results
-2016-09-06 11:21:59,150 DEBUG: Done: Getting Results
-2016-09-06 11:21:59,150 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 47
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.411111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.562770562771
- - Score on test : 0.453608247423
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.562770562771
- - Score on test : 0.453608247423
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.480952380952
- - Score on test : 0.588888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.411111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0369594857345
- - Score on test : -0.161571085301
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.52
- - Score on test : 0.392857142857
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.61320754717
- - Score on test : 0.536585365854
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.518142235123
- - Score on test : 0.421353907417
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.480952380952
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
-2016-09-06 11:21:59,150 INFO: Done: Result Analysis
-2016-09-06 11:21:59,228 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:59,228 DEBUG: Start: Training
-2016-09-06 11:21:59,246 DEBUG: Info: Time for Training: 0.176759958267[s]
-2016-09-06 11:21:59,246 DEBUG: Done: Training
-2016-09-06 11:21:59,246 DEBUG: Start: Predicting
-2016-09-06 11:21:59,250 DEBUG: Done: Predicting
-2016-09-06 11:21:59,250 DEBUG: Start: Getting Results
-2016-09-06 11:21:59,282 DEBUG: Done: Getting Results
-2016-09-06 11:21:59,282 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.488888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.976303317536
- - Score on test : 0.410256410256
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.976303317536
- - Score on test : 0.410256410256
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0238095238095
- - Score on test : 0.511111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.488888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.952424147199
- - Score on test : -0.0387937676182
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.980952380952
- - Score on test : 0.432432432432
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.971698113208
- - Score on test : 0.390243902439
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.976233671988
- - Score on test : 0.480836236934
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0238095238095
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-06 11:21:59,283 INFO: Done: Result Analysis
-2016-09-06 11:21:59,419 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:59,419 DEBUG: ### Main Programm for Classification MonoView
-2016-09-06 11:21:59,419 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-06 11:21:59,419 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-06 11:21:59,419 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:59,419 DEBUG: Start: Determine Train/Test split
-2016-09-06 11:21:59,419 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:59,419 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-06 11:21:59,420 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:59,420 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-06 11:21:59,420 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:59,420 DEBUG: Done: Determine Train/Test split
-2016-09-06 11:21:59,420 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:59,420 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-06 11:21:59,464 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:59,464 DEBUG: Start: Training
-2016-09-06 11:21:59,465 DEBUG: Info: Time for Training: 0.0465881824493[s]
-2016-09-06 11:21:59,465 DEBUG: Done: Training
-2016-09-06 11:21:59,465 DEBUG: Start: Predicting
-2016-09-06 11:21:59,468 DEBUG: Done: RandomSearch best settings
-2016-09-06 11:21:59,468 DEBUG: Start: Training
-2016-09-06 11:21:59,481 DEBUG: Done: Predicting
-2016-09-06 11:21:59,481 DEBUG: Start: Getting Results
-2016-09-06 11:21:59,487 DEBUG: Info: Time for Training: 0.0686330795288[s]
-2016-09-06 11:21:59,487 DEBUG: Done: Training
-2016-09-06 11:21:59,487 DEBUG: Start: Predicting
-2016-09-06 11:21:59,490 DEBUG: Done: Predicting
-2016-09-06 11:21:59,490 DEBUG: Start: Getting Results
-2016-09-06 11:21:59,505 DEBUG: Done: Getting Results
-2016-09-06 11:21:59,505 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.559633027523
- - Score on test : 0.478260869565
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.559633027523
- - Score on test : 0.478260869565
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0852729302366
- - Score on test : -0.0555284586866
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.544642857143
- - Score on test : 0.43137254902
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.575471698113
- - Score on test : 0.536585365854
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.542543541364
- - Score on test : 0.47237431558
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:21:59,505 INFO: Done: Result Analysis
-2016-09-06 11:21:59,523 DEBUG: Done: Getting Results
-2016-09-06 11:21:59,523 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.566666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.610878661088
- - Score on test : 0.571428571429
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.610878661088
- - Score on test : 0.571428571429
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.442857142857
- - Score on test : 0.433333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.566666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.11594977827
- - Score on test : 0.144674846981
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.548872180451
- - Score on test : 0.52
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.688679245283
- - Score on test : 0.634146341463
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.55587808418
- - Score on test : 0.572175211548
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.442857142857
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-06 11:21:59,523 INFO: Done: Result Analysis
-2016-09-06 11:21:59,818 INFO: ### Main Programm for Multiview Classification
-2016-09-06 11:21:59,818 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-06 11:21:59,819 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 11:21:59,819 INFO: Info: Shape of View1 :(300, 18)
-2016-09-06 11:21:59,820 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 11:21:59,820 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 11:21:59,820 INFO: Done: Read Database Files
-2016-09-06 11:21:59,820 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 11:21:59,823 INFO: ### Main Programm for Multiview Classification
-2016-09-06 11:21:59,823 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 11:21:59,823 INFO: Info: Shape of View0 :(300, 13)
-2016-09-06 11:21:59,824 INFO: Info: Shape of View1 :(300, 18)
-2016-09-06 11:21:59,824 INFO: Done: Determine validation split
-2016-09-06 11:21:59,824 INFO: Start: Determine 5 folds
-2016-09-06 11:21:59,824 INFO: Info: Shape of View2 :(300, 8)
-2016-09-06 11:21:59,825 INFO: Info: Shape of View3 :(300, 6)
-2016-09-06 11:21:59,825 INFO: Done: Read Database Files
-2016-09-06 11:21:59,825 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 11:21:59,829 INFO: Done: Determine validation split
-2016-09-06 11:21:59,829 INFO: Start: Determine 5 folds
-2016-09-06 11:21:59,830 INFO: Info: Length of Learning Sets: 170
-2016-09-06 11:21:59,831 INFO: Info: Length of Testing Sets: 41
-2016-09-06 11:21:59,831 INFO: Info: Length of Validation Set: 89
-2016-09-06 11:21:59,831 INFO: Done: Determine folds
-2016-09-06 11:21:59,831 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-06 11:21:59,831 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 11:21:59,831 DEBUG: Start: Gridsearch for DecisionTree on View0
-2016-09-06 11:21:59,834 INFO: Info: Length of Learning Sets: 170
-2016-09-06 11:21:59,834 INFO: Info: Length of Testing Sets: 41
-2016-09-06 11:21:59,834 INFO: Info: Length of Validation Set: 89
-2016-09-06 11:21:59,834 INFO: Done: Determine folds
-2016-09-06 11:21:59,834 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 11:21:59,834 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 11:21:59,834 DEBUG: Start: Random search for Adaboost with 30 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7310aa13413c0fe51d30595a0611fbf8868e4fab..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.494382022472
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.494382022472
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.5
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.00596274193664
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.458333333333
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.536585365854
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.502986560478
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 60733f7f4372d2fbe0d01db2bf0ef9f663fe2087..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.413793103448
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.413793103448
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.131912640639
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.391304347826
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.439024390244
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433797909408
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 98c9ed2a5aec9b146da2753a524452b44e1a9d01..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 47
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.550724637681
- - Score on test : 0.315789473684
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.550724637681
- - Score on test : 0.315789473684
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.442857142857
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.11473701202
- - Score on test : -0.180519041032
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.564356435644
- - Score on test : 0.342857142857
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.537735849057
- - Score on test : 0.292682926829
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.55732946299
- - Score on test : 0.411647585864
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.442857142857
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 11143f3f7b7d16b5e170c5ab4fd7f72ea6e130b2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112155Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.957142857143
- - Score on test : 0.4
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.956937799043
- - Score on test : 0.325
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.956937799043
- - Score on test : 0.325
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0428571428571
- - Score on test : 0.6
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.957142857143
- - Score on test : 0.4
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.914674537841
- - Score on test : -0.214609988978
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.970873786408
- - Score on test : 0.333333333333
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.943396226415
- - Score on test : 0.317073170732
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.957275036284
- - Score on test : 0.393230462917
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0428571428571
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 22da83be580cf08f7fe511bc720910ca2202c3f6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.404761904762
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.404761904762
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.115633266975
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.395348837209
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.414634146341
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.442010950722
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 97bcfb03eede2f30c7c09839dcbc52d8578c8b88..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.418604651163
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.418604651163
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.111552687063
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.439024390244
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.44400199104
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db1e7c90d39f84e92df0500245ea9b9e26d1dedb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 47
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.633204633205
- - Score on test : 0.558558558559
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.633204633205
- - Score on test : 0.558558558559
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.452380952381
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.102191547553
- - Score on test : -0.0477019354931
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.535947712418
- - Score on test : 0.442857142857
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.77358490566
- - Score on test : 0.756097560976
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.545446298984
- - Score on test : 0.480089596814
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.452380952381
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 52272501df553f770e104ca4715e1eb70fe7a93f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.604761904762
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.433333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.62443438914
- - Score on test : 0.43956043956
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.62443438914
- - Score on test : 0.43956043956
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.395238095238
- - Score on test : 0.566666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.604761904762
- - Score on test : 0.433333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.209578877963
- - Score on test : -0.124719695673
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.4
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.650943396226
- - Score on test : 0.487804878049
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.604317851959
- - Score on test : 0.437779990045
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.395238095238
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 023c955b3646997254d66b6f74a22072139d0cdc..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.504672897196
- - Score on test : 0.404761904762
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.504672897196
- - Score on test : 0.404761904762
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.00980036362201
- - Score on test : -0.115633266975
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.395348837209
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.509433962264
- - Score on test : 0.414634146341
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.495101596517
- - Score on test : 0.442010950722
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4a817800d802fe8d5147dc163ddbc8afb6cdbb72..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.150943396226
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.150943396226
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.5
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.096260040145
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0975609756098
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.467147834744
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9c326ee0a2e05dea68d6e24bbb431c6b26d3dc19..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112156Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 13)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.345679012346
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.345679012346
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.588888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.411111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.189573937423
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.35
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.341463414634
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.405425584868
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bc43f864d2ea828df712fbae60f20043f68cfb9c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.444444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.104031856645
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.552015928323
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c42bb400ae8a8fdedb841cb3c754293d696319cb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.995305164319
- - Score on test : 0.511627906977
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.995305164319
- - Score on test : 0.511627906977
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.990519401324
- - Score on test : 0.066931612238
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.990654205607
- - Score on test : 0.488888888889
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.536585365854
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.995192307692
- - Score on test : 0.533598805376
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7bf769f1bc0c2c156f175b1fe70658f88c9b6906..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.976525821596
- - Score on test : 0.516853932584
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.976525821596
- - Score on test : 0.516853932584
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0238095238095
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.952415522541
- - Score on test : 0.0506833064614
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.971962616822
- - Score on test : 0.479166666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.981132075472
- - Score on test : 0.560975609756
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.976142960813
- - Score on test : 0.525385764062
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0238095238095
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 45ae81a4f03834da811af554902b0ff07b317177..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.564593301435
- - Score on test : 0.516853932584
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.564593301435
- - Score on test : 0.516853932584
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.433333333333
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.133545022783
- - Score on test : 0.0506833064614
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.572815533981
- - Score on test : 0.479166666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.556603773585
- - Score on test : 0.560975609756
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.566763425254
- - Score on test : 0.525385764062
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.433333333333
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 395792e34ea36d0f71040c5bd654478caf5c099b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.528571428571
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.535211267606
- - Score on test : 0.475
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.535211267606
- - Score on test : 0.475
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.471428571429
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.528571428571
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0569743711331
- - Score on test : 0.0555284586866
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.532710280374
- - Score on test : 0.487179487179
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.537735849057
- - Score on test : 0.463414634146
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.528483309144
- - Score on test : 0.52762568442
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.471428571429
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 96ee48b4709eca185b46819a817e5c8f27db9696..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.561403508772
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.561403508772
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0715653145323
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.438356164384
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.780487804878
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4718765555
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75cb5217f476620552e022b850221b11be87393f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112157Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511627906977
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511627906977
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.066931612238
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.536585365854
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533598805376
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 92282ac5d982afa7b67e798392101b6eab92cabd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 7, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.404761904762
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.404761904762
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.115633266975
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.395348837209
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.414634146341
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.442010950722
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ed81b59a61d0887a2038565f79039b2a5ebb6c36..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.428571428571
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.428571428571
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0709680565554
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.418604651163
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.439024390244
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.464410154306
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bddd0960441ccf4c67f24b028906a144474cd1c2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 47
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.477777777778
- - Score on test : 0.342105263158
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.477777777778
- - Score on test : 0.342105263158
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.447619047619
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.112597765671
- - Score on test : -0.134753650348
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.581081081081
- - Score on test : 0.371428571429
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.405660377358
- - Score on test : 0.317073170732
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.553791727141
- - Score on test : 0.434046789447
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.447619047619
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9d35d1a6da182138e6ae1ad331a4e2d7316dbb03..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.966666666667
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.96682464455
- - Score on test : 0.53164556962
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.96682464455
- - Score on test : 0.53164556962
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0333333333333
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.966666666667
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.933375664255
- - Score on test : 0.166630556676
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.552631578947
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.962264150943
- - Score on test : 0.512195121951
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.966708998549
- - Score on test : 0.582628173221
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0333333333333
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 98fcb2e6715737097b534f2d5a4e9c1553160301..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.5
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.544554455446
- - Score on test : 0.470588235294
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.544554455446
- - Score on test : 0.470588235294
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.438095238095
- - Score on test : 0.5
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.5
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.125091870983
- - Score on test : -0.00198364873142
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.572916666667
- - Score on test : 0.454545454545
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.518867924528
- - Score on test : 0.487804878049
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.562318577649
- - Score on test : 0.499004479841
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.438095238095
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4471f09ed96536e0167cd416edb89fe3dff7ef2b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.633333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.633333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.51376146789
- - Score on test : 0.637362637363
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.51376146789
- - Score on test : 0.637362637363
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.366666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.633333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.0101818424163
- - Score on test : 0.279372118308
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.58
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.528301886792
- - Score on test : 0.707317073171
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.494920174165
- - Score on test : 0.6393728223
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.366666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 25f841d0b5e3d23af058cf951b334597f5e1f51a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.995260663507
- - Score on test : 0.479166666667
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.995260663507
- - Score on test : 0.479166666667
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.990521113087
- - Score on test : -0.0940733030728
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.418181818182
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.990566037736
- - Score on test : 0.560975609756
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.995283018868
- - Score on test : 0.453957192633
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 70e52533fb7bce3e44528ba58ac0135e2e7730c8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112158Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0592334494774
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.428571428571
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.470383275261
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e57793d4a36e3f6ef7af1095b20a8028d4f664c8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.519047619048
-accuracy_score on test : 0.411111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 47
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.411111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.562770562771
- - Score on test : 0.453608247423
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.562770562771
- - Score on test : 0.453608247423
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.480952380952
- - Score on test : 0.588888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.519047619048
- - Score on test : 0.411111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0369594857345
- - Score on test : -0.161571085301
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.52
- - Score on test : 0.392857142857
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.61320754717
- - Score on test : 0.536585365854
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.518142235123
- - Score on test : 0.421353907417
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.480952380952
- - Score on test : 0.588888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e01149648085dff1c6ff500035e0a1f24eec1e2d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.97619047619
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 7, max_depth : 15
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.488888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.976303317536
- - Score on test : 0.410256410256
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.976303317536
- - Score on test : 0.410256410256
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0238095238095
- - Score on test : 0.511111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.97619047619
- - Score on test : 0.488888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.952424147199
- - Score on test : -0.0387937676182
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.980952380952
- - Score on test : 0.432432432432
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.971698113208
- - Score on test : 0.390243902439
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.976233671988
- - Score on test : 0.480836236934
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0238095238095
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 08b2b5e9b92fa2691c9cf68a26275399640d2306..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.559633027523
- - Score on test : 0.478260869565
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.559633027523
- - Score on test : 0.478260869565
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0852729302366
- - Score on test : -0.0555284586866
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.544642857143
- - Score on test : 0.43137254902
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.575471698113
- - Score on test : 0.536585365854
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.542543541364
- - Score on test : 0.47237431558
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 75c3ebcffd6e56bb4cde5a45b8b7327951ad3ad2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-112159Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.557142857143
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 2991
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.566666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.610878661088
- - Score on test : 0.571428571429
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.610878661088
- - Score on test : 0.571428571429
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.442857142857
- - Score on test : 0.433333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.566666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.11594977827
- - Score on test : 0.144674846981
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.548872180451
- - Score on test : 0.52
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.688679245283
- - Score on test : 0.634146341463
- For ROS AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.55587808418
- - Score on test : 0.572175211548
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.442857142857
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161431-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-161431-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 5d2fd1b2aa1e875c07cdef76f8f21a644659e684..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161431-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,50 +0,0 @@
-2016-09-06 16:14:31,063 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 16:14:31,063 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00012634375 Gbytes /!\
-2016-09-06 16:14:36,078 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 16:14:36,080 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 16:14:36,142 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:14:36,142 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:14:36,143 INFO: Info: Shape of View0 :(300, 9)
-2016-09-06 16:14:36,143 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:14:36,143 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:14:36,143 INFO: Info: Shape of View1 :(300, 13)
-2016-09-06 16:14:36,144 INFO: Info: Shape of View0 :(300, 9)
-2016-09-06 16:14:36,144 INFO: Info: Shape of View2 :(300, 12)
-2016-09-06 16:14:36,144 INFO: Info: Shape of View1 :(300, 13)
-2016-09-06 16:14:36,144 INFO: Info: Shape of View3 :(300, 12)
-2016-09-06 16:14:36,145 INFO: Done: Read Database Files
-2016-09-06 16:14:36,145 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 16:14:36,145 INFO: Info: Shape of View2 :(300, 12)
-2016-09-06 16:14:36,145 INFO: Info: Shape of View3 :(300, 12)
-2016-09-06 16:14:36,145 INFO: Done: Read Database Files
-2016-09-06 16:14:36,145 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 16:14:36,150 INFO: Done: Determine validation split
-2016-09-06 16:14:36,150 INFO: Start: Determine 5 folds
-2016-09-06 16:14:36,151 INFO: Done: Determine validation split
-2016-09-06 16:14:36,151 INFO: Start: Determine 5 folds
-2016-09-06 16:14:36,159 INFO: Info: Length of Learning Sets: 169
-2016-09-06 16:14:36,159 INFO: Info: Length of Testing Sets: 42
-2016-09-06 16:14:36,159 INFO: Info: Length of Validation Set: 89
-2016-09-06 16:14:36,159 INFO: Done: Determine folds
-2016-09-06 16:14:36,159 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 16:14:36,159 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 16:14:36,160 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:36,162 INFO: Info: Length of Learning Sets: 169
-2016-09-06 16:14:36,162 INFO: Info: Length of Testing Sets: 42
-2016-09-06 16:14:36,162 INFO: Info: Length of Validation Set: 89
-2016-09-06 16:14:36,162 INFO: Done: Determine folds
-2016-09-06 16:14:36,162 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 16:14:36,162 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 16:14:36,162 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:36,589 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:14:36,589 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:36,594 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:14:36,594 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:37,006 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:14:37,006 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:37,012 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:14:37,012 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:37,423 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:14:37,423 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:14:37,429 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:14:37,429 DEBUG: Start: Random search for DecisionTree with 30 iterations
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161457-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160906-161457-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index cd3713da6bdd48e685e1a2a5d11509f7380a4cf2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161457-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,352 +0,0 @@
-2016-09-06 16:14:57,297 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-06 16:14:57,298 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00015446875 Gbytes /!\
-2016-09-06 16:15:02,311 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-06 16:15:02,313 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-06 16:15:02,362 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:02,362 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:02,362 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:02,363 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:02,363 INFO: Info: Shape of View0 :(300, 15)
-2016-09-06 16:15:02,363 INFO: Info: Shape of View0 :(300, 15)
-2016-09-06 16:15:02,363 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 16:15:02,364 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 16:15:02,364 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 16:15:02,364 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 16:15:02,364 INFO: Info: Shape of View3 :(300, 13)
-2016-09-06 16:15:02,364 INFO: Done: Read Database Files
-2016-09-06 16:15:02,365 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 16:15:02,365 INFO: Info: Shape of View3 :(300, 13)
-2016-09-06 16:15:02,365 INFO: Done: Read Database Files
-2016-09-06 16:15:02,365 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 16:15:02,369 INFO: Done: Determine validation split
-2016-09-06 16:15:02,369 INFO: Start: Determine 5 folds
-2016-09-06 16:15:02,369 INFO: Done: Determine validation split
-2016-09-06 16:15:02,369 INFO: Start: Determine 5 folds
-2016-09-06 16:15:02,375 INFO: Info: Length of Learning Sets: 169
-2016-09-06 16:15:02,375 INFO: Info: Length of Testing Sets: 41
-2016-09-06 16:15:02,375 INFO: Info: Length of Validation Set: 90
-2016-09-06 16:15:02,375 INFO: Done: Determine folds
-2016-09-06 16:15:02,375 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 16:15:02,375 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:02,375 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:02,376 INFO: Info: Length of Learning Sets: 169
-2016-09-06 16:15:02,376 INFO: Info: Length of Testing Sets: 41
-2016-09-06 16:15:02,376 INFO: Info: Length of Validation Set: 90
-2016-09-06 16:15:02,376 INFO: Done: Determine folds
-2016-09-06 16:15:02,376 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 16:15:02,376 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:02,377 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:02,805 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:02,805 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:02,809 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:02,809 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:03,243 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:03,243 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:03,311 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:03,311 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:03,666 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:03,666 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:03,737 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:03,737 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:04,086 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:04,161 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:05,703 INFO: Done: Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:05,703 INFO: Start: Classification
-2016-09-06 16:15:05,703 INFO: Start: Fold number 1
-2016-09-06 16:15:05,733 INFO: Start: Classification
-2016-09-06 16:15:05,759 INFO: Done: Fold number 1
-2016-09-06 16:15:05,759 INFO: Start: Fold number 2
-2016-09-06 16:15:05,790 INFO: Start: Classification
-2016-09-06 16:15:05,817 INFO: Done: Fold number 2
-2016-09-06 16:15:05,817 INFO: Start: Fold number 3
-2016-09-06 16:15:05,848 INFO: Start: Classification
-2016-09-06 16:15:05,875 INFO: Done: Fold number 3
-2016-09-06 16:15:05,876 INFO: Start: Fold number 4
-2016-09-06 16:15:05,906 INFO: Start: Classification
-2016-09-06 16:15:05,933 INFO: Done: Fold number 4
-2016-09-06 16:15:05,933 INFO: Start: Fold number 5
-2016-09-06 16:15:05,964 INFO: Start: Classification
-2016-09-06 16:15:05,991 INFO: Done: Fold number 5
-2016-09-06 16:15:05,991 INFO: Done: Classification
-2016-09-06 16:15:05,991 INFO: Info: Time for Classification: 3[s]
-2016-09-06 16:15:05,992 INFO: Start: Result Analysis for Fusion
-2016-09-06 16:15:06,172 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 48.2926829268
- -On Validation : 49.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.4761253742, 0.00270164468431, 0.0406662390422, 0.480506742074
- -With monoview classifiers :
- - Decision Tree with max_depth : 7
- - Decision Tree with max_depth : 3
- - Decision Tree with max_depth : 24
- - Decision Tree with max_depth : 4
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:03 0:00:00
- Fold 2 0:00:03 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:03 0:00:00
- Fold 5 0:00:03 0:00:00
- Total 0:00:17 0:00:00
- So a total classification time of 0:00:03.
-
-
-2016-09-06 16:15:06,172 INFO: Done: Result Analysis
-2016-09-06 16:15:06,570 INFO: Done: Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:06,570 INFO: Start: Classification
-2016-09-06 16:15:06,570 INFO: Start: Fold number 1
-2016-09-06 16:15:06,599 INFO: Start: Classification
-2016-09-06 16:15:06,667 INFO: Done: Fold number 1
-2016-09-06 16:15:06,668 INFO: Start: Fold number 2
-2016-09-06 16:15:06,697 INFO: Start: Classification
-2016-09-06 16:15:06,765 INFO: Done: Fold number 2
-2016-09-06 16:15:06,765 INFO: Start: Fold number 3
-2016-09-06 16:15:06,794 INFO: Start: Classification
-2016-09-06 16:15:06,862 INFO: Done: Fold number 3
-2016-09-06 16:15:06,862 INFO: Start: Fold number 4
-2016-09-06 16:15:06,896 INFO: Start: Classification
-2016-09-06 16:15:06,964 INFO: Done: Fold number 4
-2016-09-06 16:15:06,964 INFO: Start: Fold number 5
-2016-09-06 16:15:06,993 INFO: Start: Classification
-2016-09-06 16:15:07,061 INFO: Done: Fold number 5
-2016-09-06 16:15:07,061 INFO: Done: Classification
-2016-09-06 16:15:07,061 INFO: Info: Time for Classification: 4[s]
-2016-09-06 16:15:07,061 INFO: Start: Result Analysis for Fusion
-2016-09-06 16:15:07,194 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 46.8292682927
- -On Validation : 44.0
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - Decision Tree with max_depth : 7
- - Decision Tree with max_depth : 3
- - Decision Tree with max_depth : 24
- - Decision Tree with max_depth : 4
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:04 0:00:00
- Fold 2 0:00:04 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:04 0:00:00
- Fold 5 0:00:04 0:00:00
- Total 0:00:22 0:00:00
- So a total classification time of 0:00:04.
-
-
-2016-09-06 16:15:07,195 INFO: Done: Result Analysis
-2016-09-06 16:15:07,318 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:07,318 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:07,319 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:07,319 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:07,319 INFO: Info: Shape of View0 :(300, 15)
-2016-09-06 16:15:07,319 INFO: Info: Shape of View0 :(300, 15)
-2016-09-06 16:15:07,320 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 16:15:07,320 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 16:15:07,321 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 16:15:07,321 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 16:15:07,321 INFO: Info: Shape of View3 :(300, 13)
-2016-09-06 16:15:07,321 INFO: Info: Shape of View3 :(300, 13)
-2016-09-06 16:15:07,321 INFO: Done: Read Database Files
-2016-09-06 16:15:07,321 INFO: Done: Read Database Files
-2016-09-06 16:15:07,322 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 16:15:07,322 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 16:15:07,327 INFO: Done: Determine validation split
-2016-09-06 16:15:07,328 INFO: Done: Determine validation split
-2016-09-06 16:15:07,328 INFO: Start: Determine 5 folds
-2016-09-06 16:15:07,328 INFO: Start: Determine 5 folds
-2016-09-06 16:15:07,339 INFO: Info: Length of Learning Sets: 169
-2016-09-06 16:15:07,339 INFO: Info: Length of Testing Sets: 41
-2016-09-06 16:15:07,339 INFO: Info: Length of Validation Set: 90
-2016-09-06 16:15:07,339 INFO: Done: Determine folds
-2016-09-06 16:15:07,339 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 16:15:07,339 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:07,339 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:07,344 INFO: Info: Length of Learning Sets: 169
-2016-09-06 16:15:07,345 INFO: Info: Length of Testing Sets: 41
-2016-09-06 16:15:07,345 INFO: Info: Length of Validation Set: 90
-2016-09-06 16:15:07,345 INFO: Done: Determine folds
-2016-09-06 16:15:07,345 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 16:15:07,345 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:07,345 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:08,051 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:08,051 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:08,056 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:08,057 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:08,762 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:08,762 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:08,764 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:08,765 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:09,471 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:09,471 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:09,474 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:09,474 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:10,189 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:10,190 INFO: Done: Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:10,190 INFO: Start: Classification
-2016-09-06 16:15:10,190 INFO: Start: Fold number 1
-2016-09-06 16:15:10,192 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:10,252 INFO: Start: Classification
-2016-09-06 16:15:10,280 INFO: Done: Fold number 1
-2016-09-06 16:15:10,280 INFO: Start: Fold number 2
-2016-09-06 16:15:10,327 INFO: Start: Classification
-2016-09-06 16:15:10,355 INFO: Done: Fold number 2
-2016-09-06 16:15:10,355 INFO: Start: Fold number 3
-2016-09-06 16:15:10,402 INFO: Start: Classification
-2016-09-06 16:15:10,430 INFO: Done: Fold number 3
-2016-09-06 16:15:10,430 INFO: Start: Fold number 4
-2016-09-06 16:15:10,477 INFO: Start: Classification
-2016-09-06 16:15:10,505 INFO: Done: Fold number 4
-2016-09-06 16:15:10,505 INFO: Start: Fold number 5
-2016-09-06 16:15:10,553 INFO: Start: Classification
-2016-09-06 16:15:10,581 INFO: Done: Fold number 5
-2016-09-06 16:15:10,581 INFO: Done: Classification
-2016-09-06 16:15:10,581 INFO: Info: Time for Classification: 3[s]
-2016-09-06 16:15:10,581 INFO: Start: Result Analysis for Fusion
-2016-09-06 16:15:10,751 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 44.8780487805
- -On Validation : 50.6666666667
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - Decision Tree with max_depth : 7
- - Decision Tree with max_depth : 3
- - Decision Tree with max_depth : 24
- - Decision Tree with max_depth : 4
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:02 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:03 0:00:00
- Fold 5 0:00:03 0:00:00
- Total 0:00:15 0:00:00
- So a total classification time of 0:00:03.
-
-
-2016-09-06 16:15:10,765 INFO: Done: Result Analysis
-2016-09-06 16:15:11,829 INFO: Done: Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:11,830 INFO: Start: Classification
-2016-09-06 16:15:11,830 INFO: Start: Fold number 1
-2016-09-06 16:15:11,858 INFO: Start: Classification
-2016-09-06 16:15:11,888 INFO: Done: Fold number 1
-2016-09-06 16:15:11,888 INFO: Start: Fold number 2
-2016-09-06 16:15:11,917 INFO: Start: Classification
-2016-09-06 16:15:11,947 INFO: Done: Fold number 2
-2016-09-06 16:15:11,947 INFO: Start: Fold number 3
-2016-09-06 16:15:11,975 INFO: Start: Classification
-2016-09-06 16:15:12,005 INFO: Done: Fold number 3
-2016-09-06 16:15:12,005 INFO: Start: Fold number 4
-2016-09-06 16:15:12,033 INFO: Start: Classification
-2016-09-06 16:15:12,063 INFO: Done: Fold number 4
-2016-09-06 16:15:12,064 INFO: Start: Fold number 5
-2016-09-06 16:15:12,092 INFO: Start: Classification
-2016-09-06 16:15:12,122 INFO: Done: Fold number 5
-2016-09-06 16:15:12,122 INFO: Done: Classification
-2016-09-06 16:15:12,122 INFO: Info: Time for Classification: 4[s]
-2016-09-06 16:15:12,122 INFO: Start: Result Analysis for Fusion
-2016-09-06 16:15:12,264 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 53.2544378698
- -On Test : 53.6585365854
- -On Validation : 53.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 0.111861495999, 0.371763764635, 0.0125470094031
- -With monoview classifiers :
- - Decision Tree with max_depth : 7
- - Decision Tree with max_depth : 3
- - Decision Tree with max_depth : 24
- - Decision Tree with max_depth : 4
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:04 0:00:00
- Fold 2 0:00:04 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:04 0:00:00
- Fold 5 0:00:04 0:00:00
- Total 0:00:23 0:00:00
- So a total classification time of 0:00:04.
-
-
-2016-09-06 16:15:12,264 INFO: Done: Result Analysis
-2016-09-06 16:15:12,376 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:12,376 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:12,377 INFO: ### Main Programm for Multiview Classification
-2016-09-06 16:15:12,377 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-06 16:15:12,377 INFO: Info: Shape of View0 :(300, 15)
-2016-09-06 16:15:12,378 INFO: Info: Shape of View0 :(300, 15)
-2016-09-06 16:15:12,378 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 16:15:12,379 INFO: Info: Shape of View1 :(300, 16)
-2016-09-06 16:15:12,379 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 16:15:12,380 INFO: Info: Shape of View2 :(300, 14)
-2016-09-06 16:15:12,380 INFO: Info: Shape of View3 :(300, 13)
-2016-09-06 16:15:12,380 INFO: Done: Read Database Files
-2016-09-06 16:15:12,380 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 16:15:12,381 INFO: Info: Shape of View3 :(300, 13)
-2016-09-06 16:15:12,381 INFO: Done: Read Database Files
-2016-09-06 16:15:12,381 INFO: Start: Determine validation split for ratio 0.7
-2016-09-06 16:15:12,385 INFO: Done: Determine validation split
-2016-09-06 16:15:12,385 INFO: Start: Determine 5 folds
-2016-09-06 16:15:12,385 INFO: Done: Determine validation split
-2016-09-06 16:15:12,385 INFO: Start: Determine 5 folds
-2016-09-06 16:15:12,392 INFO: Info: Length of Learning Sets: 169
-2016-09-06 16:15:12,392 INFO: Info: Length of Testing Sets: 41
-2016-09-06 16:15:12,392 INFO: Info: Length of Validation Set: 90
-2016-09-06 16:15:12,392 INFO: Done: Determine folds
-2016-09-06 16:15:12,392 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 16:15:12,393 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:12,393 DEBUG: Start: Random search for DecisionTree with 30 iterations
-2016-09-06 16:15:12,393 INFO: Info: Length of Learning Sets: 169
-2016-09-06 16:15:12,393 INFO: Info: Length of Testing Sets: 41
-2016-09-06 16:15:12,393 INFO: Info: Length of Validation Set: 90
-2016-09-06 16:15:12,393 INFO: Done: Determine folds
-2016-09-06 16:15:12,393 INFO: Start: Learning with Fusion and 5 folds
-2016-09-06 16:15:12,393 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-06 16:15:12,393 DEBUG: Start: Random search for Adaboost with 30 iterations
-2016-09-06 16:15:12,822 DEBUG: Done: Random search for DecisionTree
-2016-09-06 16:15:15,451 DEBUG: Done: Random search for Adaboost
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161506Results-Fusion-LateFusion-BayesianInference-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161506Results-Fusion-LateFusion-BayesianInference-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 05f6d2404a2dcd983b37b1df7c9d17ab53b37cbf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161506Results-Fusion-LateFusion-BayesianInference-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 48.2926829268
- -On Validation : 49.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.4761253742, 0.00270164468431, 0.0406662390422, 0.480506742074
- -With monoview classifiers :
- - Decision Tree with max_depth : 7
- - Decision Tree with max_depth : 3
- - Decision Tree with max_depth : 24
- - Decision Tree with max_depth : 4
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:03 0:00:00
- Fold 2 0:00:03 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:03 0:00:00
- Fold 5 0:00:03 0:00:00
- Total 0:00:17 0:00:00
- So a total classification time of 0:00:03.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161507Results-Fusion-LateFusion-MajorityVoting-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161507Results-Fusion-LateFusion-MajorityVoting-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 83b87a9205a913e9cdd6483808f8d96f2c702d32..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161507Results-Fusion-LateFusion-MajorityVoting-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 46.8292682927
- -On Validation : 44.0
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - Decision Tree with max_depth : 7
- - Decision Tree with max_depth : 3
- - Decision Tree with max_depth : 24
- - Decision Tree with max_depth : 4
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:04 0:00:00
- Fold 2 0:00:04 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:04 0:00:00
- Fold 5 0:00:04 0:00:00
- Total 0:00:22 0:00:00
- So a total classification time of 0:00:04.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161510Results-Fusion-LateFusion-SVMForLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161510Results-Fusion-LateFusion-SVMForLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index c190e8d144c1431ffbef8cd728c7e9311e78d508..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161510Results-Fusion-LateFusion-SVMForLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 44.8780487805
- -On Validation : 50.6666666667
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - Decision Tree with max_depth : 7
- - Decision Tree with max_depth : 3
- - Decision Tree with max_depth : 24
- - Decision Tree with max_depth : 4
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:02 0:00:00
- Fold 2 0:00:02 0:00:00
- Fold 3 0:00:03 0:00:00
- Fold 4 0:00:03 0:00:00
- Fold 5 0:00:03 0:00:00
- Total 0:00:15 0:00:00
- So a total classification time of 0:00:03.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160906-161512Results-Fusion-LateFusion-WeightedLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160906-161512Results-Fusion-LateFusion-WeightedLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5e8e31ae0495caadf8b277decbca8b06dca480e3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160906-161512Results-Fusion-LateFusion-WeightedLinear-DecisionTree-DecisionTree-DecisionTree-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 53.2544378698
- -On Test : 53.6585365854
- -On Validation : 53.3333333333
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 0.111861495999, 0.371763764635, 0.0125470094031
- -With monoview classifiers :
- - Decision Tree with max_depth : 7
- - Decision Tree with max_depth : 3
- - Decision Tree with max_depth : 24
- - Decision Tree with max_depth : 4
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:04 0:00:00
- Fold 2 0:00:04 0:00:00
- Fold 3 0:00:04 0:00:00
- Fold 4 0:00:04 0:00:00
- Fold 5 0:00:04 0:00:00
- Total 0:00:23 0:00:00
- So a total classification time of 0:00:04.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095527-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160908-095527-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index 52d4e73fa353f4c5e4dd770a4fbf8026a0dfdd7b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095527-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2211 +0,0 @@
-2016-09-08 09:55:28,079 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-08 09:55:28,079 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00010290625 Gbytes /!\
-2016-09-08 09:55:33,093 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-08 09:55:33,096 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-08 09:55:33,343 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:33,343 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:33,343 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:55:33,343 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:55:33,343 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:33,343 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:33,383 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:33,383 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:33,383 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:33,384 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:33,384 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:33,384 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:33,384 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:33,384 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:33,415 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:33,415 DEBUG: Start: Training
-2016-09-08 09:55:33,417 DEBUG: Info: Time for Training: 0.0742340087891[s]
-2016-09-08 09:55:33,417 DEBUG: Done: Training
-2016-09-08 09:55:33,417 DEBUG: Start: Predicting
-2016-09-08 09:55:33,439 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:33,440 DEBUG: Start: Training
-2016-09-08 09:55:33,445 DEBUG: Info: Time for Training: 0.102918863297[s]
-2016-09-08 09:55:33,446 DEBUG: Done: Training
-2016-09-08 09:55:33,446 DEBUG: Start: Predicting
-2016-09-08 09:55:33,583 DEBUG: Done: Predicting
-2016-09-08 09:55:33,583 DEBUG: Done: Predicting
-2016-09-08 09:55:33,584 DEBUG: Start: Getting Results
-2016-09-08 09:55:33,584 DEBUG: Start: Getting Results
-2016-09-08 09:55:34,228 DEBUG: Done: Getting Results
-2016-09-08 09:55:34,228 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.505263157895
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.505263157895
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.522222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0418655345164
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.470588235294
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.545454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.479249011858
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,228 DEBUG: Done: Getting Results
-2016-09-08 09:55:34,228 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.484210526316
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.484210526316
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0867214643554
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.450980392157
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522727272727
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.457015810277
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,228 INFO: Done: Result Analysis
-2016-09-08 09:55:34,228 INFO: Done: Result Analysis
-2016-09-08 09:55:34,286 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,286 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,287 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:55:34,287 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:55:34,287 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:34,287 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:34,287 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,287 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,288 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,288 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,288 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:34,288 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:34,288 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,288 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,320 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:34,320 DEBUG: Start: Training
-2016-09-08 09:55:34,321 DEBUG: Info: Time for Training: 0.0349078178406[s]
-2016-09-08 09:55:34,321 DEBUG: Done: Training
-2016-09-08 09:55:34,321 DEBUG: Start: Predicting
-2016-09-08 09:55:34,326 DEBUG: Done: Predicting
-2016-09-08 09:55:34,326 DEBUG: Start: Getting Results
-2016-09-08 09:55:34,371 DEBUG: Done: Getting Results
-2016-09-08 09:55:34,371 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.661904761905
- - Score on test : 0.477777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.727969348659
- - Score on test : 0.534653465347
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.727969348659
- - Score on test : 0.534653465347
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.338095238095
- - Score on test : 0.522222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.661904761905
- - Score on test : 0.477777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.290672377783
- - Score on test : -0.0399755963154
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.68345323741
- - Score on test : 0.473684210526
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.77868852459
- - Score on test : 0.613636363636
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.639344262295
- - Score on test : 0.480731225296
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.338095238095
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,371 INFO: Done: Result Analysis
-2016-09-08 09:55:34,386 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:34,387 DEBUG: Start: Training
-2016-09-08 09:55:34,397 DEBUG: Info: Time for Training: 0.11102604866[s]
-2016-09-08 09:55:34,397 DEBUG: Done: Training
-2016-09-08 09:55:34,397 DEBUG: Start: Predicting
-2016-09-08 09:55:34,400 DEBUG: Done: Predicting
-2016-09-08 09:55:34,401 DEBUG: Start: Getting Results
-2016-09-08 09:55:34,429 DEBUG: Done: Getting Results
-2016-09-08 09:55:34,429 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.477777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.905982905983
- - Score on test : 0.515463917526
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.905982905983
- - Score on test : 0.515463917526
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.104761904762
- - Score on test : 0.522222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.477777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.791868570857
- - Score on test : -0.0411594726194
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.946428571429
- - Score on test : 0.471698113208
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.868852459016
- - Score on test : 0.568181818182
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.900335320417
- - Score on test : 0.479743083004
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.104761904762
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,430 INFO: Done: Result Analysis
-2016-09-08 09:55:34,537 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,537 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:55:34,537 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:34,537 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,538 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:55:34,538 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:34,538 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,538 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,538 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:34,538 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,538 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,538 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,538 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:34,539 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,668 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:34,668 DEBUG: Start: Training
-2016-09-08 09:55:34,686 DEBUG: Info: Time for Training: 0.148998022079[s]
-2016-09-08 09:55:34,686 DEBUG: Done: Training
-2016-09-08 09:55:34,686 DEBUG: Start: Predicting
-2016-09-08 09:55:34,689 DEBUG: Done: Predicting
-2016-09-08 09:55:34,689 DEBUG: Start: Getting Results
-2016-09-08 09:55:34,699 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:34,699 DEBUG: Start: Training
-2016-09-08 09:55:34,700 DEBUG: Info: Time for Training: 0.164316892624[s]
-2016-09-08 09:55:34,700 DEBUG: Done: Training
-2016-09-08 09:55:34,700 DEBUG: Start: Predicting
-2016-09-08 09:55:34,711 DEBUG: Done: Predicting
-2016-09-08 09:55:34,711 DEBUG: Start: Getting Results
-2016-09-08 09:55:34,727 DEBUG: Done: Getting Results
-2016-09-08 09:55:34,727 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.377777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.563265306122
- - Score on test : 0.416666666667
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.563265306122
- - Score on test : 0.416666666667
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.622222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.377777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.0481411286791
- - Score on test : -0.244017569898
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.560975609756
- - Score on test : 0.384615384615
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.565573770492
- - Score on test : 0.454545454545
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.475968703428
- - Score on test : 0.379446640316
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,727 INFO: Done: Result Analysis
-2016-09-08 09:55:34,737 DEBUG: Done: Getting Results
-2016-09-08 09:55:34,737 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.749226006192
- - Score on test : 0.630769230769
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.749226006192
- - Score on test : 0.630769230769
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.385714285714
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.201498784613
- - Score on test : -0.112653159931
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.601990049751
- - Score on test : 0.476744186047
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.991803278689
- - Score on test : 0.931818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.541356184799
- - Score on test : 0.476778656126
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.385714285714
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,738 INFO: Done: Result Analysis
-2016-09-08 09:55:34,880 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,881 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:34,881 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:55:34,881 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:55:34,881 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:34,881 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:34,881 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,881 DEBUG: Info: Shape X_train:(210, 8), Length of y_train:210
-2016-09-08 09:55:34,882 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,882 DEBUG: Info: Shape X_test:(90, 8), Length of y_test:90
-2016-09-08 09:55:34,882 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:34,882 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:34,882 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,882 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:34,928 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:34,928 DEBUG: Start: Training
-2016-09-08 09:55:34,928 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:34,929 DEBUG: Start: Training
-2016-09-08 09:55:34,944 DEBUG: Info: Time for Training: 0.0635361671448[s]
-2016-09-08 09:55:34,944 DEBUG: Done: Training
-2016-09-08 09:55:34,944 DEBUG: Start: Predicting
-2016-09-08 09:55:34,945 DEBUG: Info: Time for Training: 0.0647149085999[s]
-2016-09-08 09:55:34,945 DEBUG: Done: Training
-2016-09-08 09:55:34,945 DEBUG: Start: Predicting
-2016-09-08 09:55:34,947 DEBUG: Done: Predicting
-2016-09-08 09:55:34,947 DEBUG: Start: Getting Results
-2016-09-08 09:55:34,950 DEBUG: Done: Predicting
-2016-09-08 09:55:34,950 DEBUG: Start: Getting Results
-2016-09-08 09:55:34,979 DEBUG: Done: Getting Results
-2016-09-08 09:55:34,979 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.468085106383
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.468085106383
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.109345881217
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.44
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.445652173913
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,980 INFO: Done: Result Analysis
-2016-09-08 09:55:34,995 DEBUG: Done: Getting Results
-2016-09-08 09:55:34,995 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.616822429907
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.616822429907
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.106710653456
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.52380952381
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.75
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.548913043478
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:55:34,995 INFO: Done: Result Analysis
-2016-09-08 09:55:35,123 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,123 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,123 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:55:35,123 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:55:35,123 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:35,123 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:35,124 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,125 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,125 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,125 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:35,125 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,125 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,125 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:35,125 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,163 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:35,163 DEBUG: Start: Training
-2016-09-08 09:55:35,165 DEBUG: Info: Time for Training: 0.0432438850403[s]
-2016-09-08 09:55:35,165 DEBUG: Done: Training
-2016-09-08 09:55:35,165 DEBUG: Start: Predicting
-2016-09-08 09:55:35,168 DEBUG: Done: Predicting
-2016-09-08 09:55:35,168 DEBUG: Start: Getting Results
-2016-09-08 09:55:35,177 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:35,177 DEBUG: Start: Training
-2016-09-08 09:55:35,181 DEBUG: Info: Time for Training: 0.0592088699341[s]
-2016-09-08 09:55:35,181 DEBUG: Done: Training
-2016-09-08 09:55:35,181 DEBUG: Start: Predicting
-2016-09-08 09:55:35,184 DEBUG: Done: Predicting
-2016-09-08 09:55:35,184 DEBUG: Start: Getting Results
-2016-09-08 09:55:35,215 DEBUG: Done: Getting Results
-2016-09-08 09:55:35,215 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.48
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.48
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.154858431981
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.428571428571
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.545454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.424901185771
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,216 INFO: Done: Result Analysis
-2016-09-08 09:55:35,220 DEBUG: Done: Getting Results
-2016-09-08 09:55:35,220 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.48
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.48
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.154858431981
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.428571428571
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.545454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.424901185771
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,220 INFO: Done: Result Analysis
-2016-09-08 09:55:35,370 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,370 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:55:35,370 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,370 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:35,370 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:55:35,370 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:35,371 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,371 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,371 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,371 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:35,371 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,371 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,371 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:35,371 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,402 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:35,403 DEBUG: Start: Training
-2016-09-08 09:55:35,403 DEBUG: Info: Time for Training: 0.0339629650116[s]
-2016-09-08 09:55:35,403 DEBUG: Done: Training
-2016-09-08 09:55:35,403 DEBUG: Start: Predicting
-2016-09-08 09:55:35,409 DEBUG: Done: Predicting
-2016-09-08 09:55:35,409 DEBUG: Start: Getting Results
-2016-09-08 09:55:35,450 DEBUG: Done: Getting Results
-2016-09-08 09:55:35,450 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.6484375
- - Score on test : 0.541666666667
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.6484375
- - Score on test : 0.541666666667
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.428571428571
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.103477711187
- - Score on test : 0.0260018722022
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.619402985075
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.680327868852
- - Score on test : 0.590909090909
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.550391207154
- - Score on test : 0.512845849802
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.428571428571
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,450 INFO: Done: Result Analysis
-2016-09-08 09:55:35,480 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:35,481 DEBUG: Start: Training
-2016-09-08 09:55:35,491 DEBUG: Info: Time for Training: 0.121489048004[s]
-2016-09-08 09:55:35,491 DEBUG: Done: Training
-2016-09-08 09:55:35,491 DEBUG: Start: Predicting
-2016-09-08 09:55:35,495 DEBUG: Done: Predicting
-2016-09-08 09:55:35,495 DEBUG: Start: Getting Results
-2016-09-08 09:55:35,527 DEBUG: Done: Getting Results
-2016-09-08 09:55:35,527 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.477777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.909090909091
- - Score on test : 0.356164383562
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.909090909091
- - Score on test : 0.356164383562
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.1
- - Score on test : 0.522222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.477777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.805030105216
- - Score on test : -0.0560191732057
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.963302752294
- - Score on test : 0.448275862069
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.860655737705
- - Score on test : 0.295454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.907600596125
- - Score on test : 0.473814229249
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.1
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,528 INFO: Done: Result Analysis
-2016-09-08 09:55:35,610 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,610 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,610 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:55:35,610 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:55:35,610 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:35,610 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:35,611 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,611 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,611 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,611 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,611 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:35,611 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:35,611 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,611 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,656 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:35,656 DEBUG: Start: Training
-2016-09-08 09:55:35,657 DEBUG: Info: Time for Training: 0.0472548007965[s]
-2016-09-08 09:55:35,657 DEBUG: Done: Training
-2016-09-08 09:55:35,657 DEBUG: Start: Predicting
-2016-09-08 09:55:35,660 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:35,661 DEBUG: Start: Training
-2016-09-08 09:55:35,680 DEBUG: Done: Predicting
-2016-09-08 09:55:35,680 DEBUG: Start: Getting Results
-2016-09-08 09:55:35,681 DEBUG: Info: Time for Training: 0.0712029933929[s]
-2016-09-08 09:55:35,681 DEBUG: Done: Training
-2016-09-08 09:55:35,681 DEBUG: Start: Predicting
-2016-09-08 09:55:35,684 DEBUG: Done: Predicting
-2016-09-08 09:55:35,684 DEBUG: Start: Getting Results
-2016-09-08 09:55:35,704 DEBUG: Done: Getting Results
-2016-09-08 09:55:35,704 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.745341614907
- - Score on test : 0.661417322835
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.745341614907
- - Score on test : 0.661417322835
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.390476190476
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.172644893682
- - Score on test : 0.118036588599
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.506024096386
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.983606557377
- - Score on test : 0.954545454545
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.537257824143
- - Score on test : 0.53162055336
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.390476190476
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,704 INFO: Done: Result Analysis
-2016-09-08 09:55:35,714 DEBUG: Done: Getting Results
-2016-09-08 09:55:35,715 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.606557377049
- - Score on test : 0.584905660377
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.606557377049
- - Score on test : 0.584905660377
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0611028315946
- - Score on test : 0.0330758927464
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.606557377049
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.606557377049
- - Score on test : 0.704545454545
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.530551415797
- - Score on test : 0.515316205534
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,715 INFO: Done: Result Analysis
-2016-09-08 09:55:35,860 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,860 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:35,860 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:55:35,860 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:55:35,860 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:35,860 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:35,861 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,861 DEBUG: Info: Shape X_train:(210, 15), Length of y_train:210
-2016-09-08 09:55:35,861 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,861 DEBUG: Info: Shape X_test:(90, 15), Length of y_test:90
-2016-09-08 09:55:35,861 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:35,861 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:35,861 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,861 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:35,908 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:35,908 DEBUG: Start: Training
-2016-09-08 09:55:35,910 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:35,910 DEBUG: Start: Training
-2016-09-08 09:55:35,925 DEBUG: Info: Time for Training: 0.0658419132233[s]
-2016-09-08 09:55:35,925 DEBUG: Done: Training
-2016-09-08 09:55:35,925 DEBUG: Start: Predicting
-2016-09-08 09:55:35,926 DEBUG: Info: Time for Training: 0.06693816185[s]
-2016-09-08 09:55:35,926 DEBUG: Done: Training
-2016-09-08 09:55:35,926 DEBUG: Start: Predicting
-2016-09-08 09:55:35,929 DEBUG: Done: Predicting
-2016-09-08 09:55:35,929 DEBUG: Start: Getting Results
-2016-09-08 09:55:35,932 DEBUG: Done: Predicting
-2016-09-08 09:55:35,932 DEBUG: Start: Getting Results
-2016-09-08 09:55:35,962 DEBUG: Done: Getting Results
-2016-09-08 09:55:35,962 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.358208955224
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.358208955224
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0385036888617
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.521739130435
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.272727272727
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.516798418972
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,962 INFO: Done: Result Analysis
-2016-09-08 09:55:35,967 DEBUG: Done: Getting Results
-2016-09-08 09:55:35,967 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.645161290323
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.645161290323
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0628694613462
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.909090909091
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.51976284585
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:55:35,967 INFO: Done: Result Analysis
-2016-09-08 09:55:36,109 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,109 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:55:36,110 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:36,110 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,110 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:55:36,111 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:36,111 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,111 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,111 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:36,111 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,112 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,112 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,112 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:36,112 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,145 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:36,145 DEBUG: Start: Training
-2016-09-08 09:55:36,146 DEBUG: Info: Time for Training: 0.0371689796448[s]
-2016-09-08 09:55:36,146 DEBUG: Done: Training
-2016-09-08 09:55:36,146 DEBUG: Start: Predicting
-2016-09-08 09:55:36,149 DEBUG: Done: Predicting
-2016-09-08 09:55:36,149 DEBUG: Start: Getting Results
-2016-09-08 09:55:36,159 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:36,159 DEBUG: Start: Training
-2016-09-08 09:55:36,163 DEBUG: Info: Time for Training: 0.0549581050873[s]
-2016-09-08 09:55:36,163 DEBUG: Done: Training
-2016-09-08 09:55:36,163 DEBUG: Start: Predicting
-2016-09-08 09:55:36,166 DEBUG: Done: Predicting
-2016-09-08 09:55:36,166 DEBUG: Start: Getting Results
-2016-09-08 09:55:36,197 DEBUG: Done: Getting Results
-2016-09-08 09:55:36,197 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.441860465116
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.441860465116
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.068316965625
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.452380952381
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.431818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.465909090909
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,197 INFO: Done: Result Analysis
-2016-09-08 09:55:36,208 DEBUG: Done: Getting Results
-2016-09-08 09:55:36,208 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.436781609195
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.436781609195
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0899876638096
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.441860465116
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.431818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455039525692
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,209 INFO: Done: Result Analysis
-2016-09-08 09:55:36,356 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,356 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:55:36,356 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:36,356 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,357 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:55:36,357 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:36,357 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,357 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,357 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,358 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:36,358 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,358 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,358 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:36,358 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,387 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:36,388 DEBUG: Start: Training
-2016-09-08 09:55:36,388 DEBUG: Info: Time for Training: 0.0327939987183[s]
-2016-09-08 09:55:36,388 DEBUG: Done: Training
-2016-09-08 09:55:36,388 DEBUG: Start: Predicting
-2016-09-08 09:55:36,394 DEBUG: Done: Predicting
-2016-09-08 09:55:36,394 DEBUG: Start: Getting Results
-2016-09-08 09:55:36,437 DEBUG: Done: Getting Results
-2016-09-08 09:55:36,437 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.705882352941
- - Score on test : 0.619469026549
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.705882352941
- - Score on test : 0.619469026549
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.380952380952
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.189221481343
- - Score on test : 0.0665679839847
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.64
- - Score on test : 0.507246376812
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.786885245902
- - Score on test : 0.795454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.586624441133
- - Score on test : 0.528162055336
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.380952380952
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,437 INFO: Done: Result Analysis
-2016-09-08 09:55:36,463 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:36,463 DEBUG: Start: Training
-2016-09-08 09:55:36,473 DEBUG: Info: Time for Training: 0.11700296402[s]
-2016-09-08 09:55:36,473 DEBUG: Done: Training
-2016-09-08 09:55:36,473 DEBUG: Start: Predicting
-2016-09-08 09:55:36,477 DEBUG: Done: Predicting
-2016-09-08 09:55:36,477 DEBUG: Start: Getting Results
-2016-09-08 09:55:36,509 DEBUG: Done: Getting Results
-2016-09-08 09:55:36,509 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.4
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.904564315353
- - Score on test : 0.357142857143
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.904564315353
- - Score on test : 0.357142857143
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.109523809524
- - Score on test : 0.6
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.4
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.77645053118
- - Score on test : -0.20378096045
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.915966386555
- - Score on test : 0.375
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.893442622951
- - Score on test : 0.340909090909
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.889903129657
- - Score on test : 0.39871541502
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.109523809524
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,509 INFO: Done: Result Analysis
-2016-09-08 09:55:36,597 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,597 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:55:36,597 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,597 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:36,598 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:55:36,598 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:36,598 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,598 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,598 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:36,598 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,598 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,598 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,599 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:36,599 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,645 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:36,645 DEBUG: Start: Training
-2016-09-08 09:55:36,646 DEBUG: Info: Time for Training: 0.0489931106567[s]
-2016-09-08 09:55:36,646 DEBUG: Done: Training
-2016-09-08 09:55:36,646 DEBUG: Start: Predicting
-2016-09-08 09:55:36,651 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:36,651 DEBUG: Start: Training
-2016-09-08 09:55:36,658 DEBUG: Done: Predicting
-2016-09-08 09:55:36,658 DEBUG: Start: Getting Results
-2016-09-08 09:55:36,670 DEBUG: Info: Time for Training: 0.0731010437012[s]
-2016-09-08 09:55:36,670 DEBUG: Done: Training
-2016-09-08 09:55:36,670 DEBUG: Start: Predicting
-2016-09-08 09:55:36,674 DEBUG: Done: Predicting
-2016-09-08 09:55:36,674 DEBUG: Start: Getting Results
-2016-09-08 09:55:36,682 DEBUG: Done: Getting Results
-2016-09-08 09:55:36,683 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.715231788079
- - Score on test : 0.676923076923
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.715231788079
- - Score on test : 0.676923076923
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0945615027077
- - Score on test : 0.210925065403
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.511627906977
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.885245901639
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.533532041729
- - Score on test : 0.54347826087
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,683 INFO: Done: Result Analysis
-2016-09-08 09:55:36,702 DEBUG: Done: Getting Results
-2016-09-08 09:55:36,703 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.433333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.604838709677
- - Score on test : 0.484848484848
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.604838709677
- - Score on test : 0.484848484848
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.566666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.433333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0354605635154
- - Score on test : -0.131720304791
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.436363636364
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.614754098361
- - Score on test : 0.545454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.517604321908
- - Score on test : 0.435770750988
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,703 INFO: Done: Result Analysis
-2016-09-08 09:55:36,843 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,843 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:55:36,843 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:36,843 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:36,843 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:55:36,843 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:36,843 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,844 DEBUG: Info: Shape X_train:(210, 7), Length of y_train:210
-2016-09-08 09:55:36,844 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,844 DEBUG: Info: Shape X_test:(90, 7), Length of y_test:90
-2016-09-08 09:55:36,844 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:36,844 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:36,844 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,844 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:36,891 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:36,891 DEBUG: Start: Training
-2016-09-08 09:55:36,892 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:36,892 DEBUG: Start: Training
-2016-09-08 09:55:36,907 DEBUG: Info: Time for Training: 0.0647799968719[s]
-2016-09-08 09:55:36,907 DEBUG: Done: Training
-2016-09-08 09:55:36,907 DEBUG: Start: Predicting
-2016-09-08 09:55:36,908 DEBUG: Info: Time for Training: 0.0657360553741[s]
-2016-09-08 09:55:36,908 DEBUG: Done: Training
-2016-09-08 09:55:36,908 DEBUG: Start: Predicting
-2016-09-08 09:55:36,910 DEBUG: Done: Predicting
-2016-09-08 09:55:36,910 DEBUG: Start: Getting Results
-2016-09-08 09:55:36,913 DEBUG: Done: Predicting
-2016-09-08 09:55:36,913 DEBUG: Start: Getting Results
-2016-09-08 09:55:36,946 DEBUG: Done: Getting Results
-2016-09-08 09:55:36,946 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.357142857143
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.357142857143
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.6
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.20378096045
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.375
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.340909090909
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.39871541502
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.6
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,946 INFO: Done: Result Analysis
-2016-09-08 09:55:36,952 DEBUG: Done: Getting Results
-2016-09-08 09:55:36,952 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.538461538462
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.538461538462
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0628694613462
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.636363636364
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.470355731225
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
-2016-09-08 09:55:36,952 INFO: Done: Result Analysis
-2016-09-08 09:55:37,093 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,093 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,093 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:55:37,093 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:37,093 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:55:37,093 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:37,093 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,093 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,094 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,094 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,094 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:37,094 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:37,094 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,094 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,124 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:37,124 DEBUG: Start: Training
-2016-09-08 09:55:37,125 DEBUG: Info: Time for Training: 0.0331890583038[s]
-2016-09-08 09:55:37,126 DEBUG: Done: Training
-2016-09-08 09:55:37,126 DEBUG: Start: Predicting
-2016-09-08 09:55:37,128 DEBUG: Done: Predicting
-2016-09-08 09:55:37,128 DEBUG: Start: Getting Results
-2016-09-08 09:55:37,139 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:37,139 DEBUG: Start: Training
-2016-09-08 09:55:37,142 DEBUG: Info: Time for Training: 0.0498540401459[s]
-2016-09-08 09:55:37,142 DEBUG: Done: Training
-2016-09-08 09:55:37,142 DEBUG: Start: Predicting
-2016-09-08 09:55:37,145 DEBUG: Done: Predicting
-2016-09-08 09:55:37,145 DEBUG: Start: Getting Results
-2016-09-08 09:55:37,172 DEBUG: Done: Getting Results
-2016-09-08 09:55:37,172 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588235294118
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588235294118
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0763602735229
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.51724137931
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.681818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.536561264822
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,173 INFO: Done: Result Analysis
-2016-09-08 09:55:37,183 DEBUG: Done: Getting Results
-2016-09-08 09:55:37,184 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.576923076923
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.576923076923
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0314347306731
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.681818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.514822134387
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,184 INFO: Done: Result Analysis
-2016-09-08 09:55:37,335 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,335 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,335 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:55:37,335 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:55:37,335 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:37,335 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:37,336 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,336 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,336 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,336 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,336 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:37,336 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:37,336 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,336 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,365 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:37,365 DEBUG: Start: Training
-2016-09-08 09:55:37,365 DEBUG: Info: Time for Training: 0.0312879085541[s]
-2016-09-08 09:55:37,365 DEBUG: Done: Training
-2016-09-08 09:55:37,366 DEBUG: Start: Predicting
-2016-09-08 09:55:37,370 DEBUG: Done: Predicting
-2016-09-08 09:55:37,371 DEBUG: Start: Getting Results
-2016-09-08 09:55:37,421 DEBUG: Done: Getting Results
-2016-09-08 09:55:37,422 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.681481481481
- - Score on test : 0.601941747573
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.681481481481
- - Score on test : 0.601941747573
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.127350050081
- - Score on test : 0.100829966549
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.621621621622
- - Score on test : 0.525423728814
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.754098360656
- - Score on test : 0.704545454545
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.558867362146
- - Score on test : 0.547924901186
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,422 INFO: Done: Result Analysis
-2016-09-08 09:55:37,439 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:37,439 DEBUG: Start: Training
-2016-09-08 09:55:37,449 DEBUG: Info: Time for Training: 0.114704847336[s]
-2016-09-08 09:55:37,449 DEBUG: Done: Training
-2016-09-08 09:55:37,449 DEBUG: Start: Predicting
-2016-09-08 09:55:37,452 DEBUG: Done: Predicting
-2016-09-08 09:55:37,453 DEBUG: Start: Getting Results
-2016-09-08 09:55:37,485 DEBUG: Done: Getting Results
-2016-09-08 09:55:37,485 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.577777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.910638297872
- - Score on test : 0.586956521739
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.910638297872
- - Score on test : 0.586956521739
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.1
- - Score on test : 0.422222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.577777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.800522373751
- - Score on test : 0.157426051223
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.946902654867
- - Score on test : 0.5625
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.877049180328
- - Score on test : 0.613636363636
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.904433681073
- - Score on test : 0.578557312253
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.1
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,486 INFO: Done: Result Analysis
-2016-09-08 09:55:37,579 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,579 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:55:37,579 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:55:37,579 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:37,579 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:55:37,579 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:55:37,579 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,580 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,580 DEBUG: Info: Shape X_train:(210, 6), Length of y_train:210
-2016-09-08 09:55:37,580 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:37,580 DEBUG: Info: Shape X_test:(90, 6), Length of y_test:90
-2016-09-08 09:55:37,580 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:55:37,580 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,580 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:55:37,625 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:37,625 DEBUG: Start: Training
-2016-09-08 09:55:37,626 DEBUG: Info: Time for Training: 0.0474660396576[s]
-2016-09-08 09:55:37,626 DEBUG: Done: Training
-2016-09-08 09:55:37,626 DEBUG: Start: Predicting
-2016-09-08 09:55:37,627 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:55:37,627 DEBUG: Start: Training
-2016-09-08 09:55:37,643 DEBUG: Info: Time for Training: 0.0650768280029[s]
-2016-09-08 09:55:37,644 DEBUG: Done: Training
-2016-09-08 09:55:37,644 DEBUG: Start: Predicting
-2016-09-08 09:55:37,647 DEBUG: Done: Predicting
-2016-09-08 09:55:37,647 DEBUG: Start: Getting Results
-2016-09-08 09:55:37,652 DEBUG: Done: Predicting
-2016-09-08 09:55:37,652 DEBUG: Start: Getting Results
-2016-09-08 09:55:37,675 DEBUG: Done: Getting Results
-2016-09-08 09:55:37,675 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.488888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.739393939394
- - Score on test : 0.65671641791
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.739393939394
- - Score on test : 0.65671641791
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.511111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.488888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.115457436228
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.586538461538
- - Score on test : 0.488888888889
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.511363636364
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,675 INFO: Done: Result Analysis
-2016-09-08 09:55:37,686 DEBUG: Done: Getting Results
-2016-09-08 09:55:37,686 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.595419847328
- - Score on test : 0.550458715596
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.595419847328
- - Score on test : 0.550458715596
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.0682438863041
- - Score on test : -0.0882242643891
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.461538461538
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.639344262295
- - Score on test : 0.681818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.467399403875
- - Score on test : 0.4604743083
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:55:37,686 INFO: Done: Result Analysis
-2016-09-08 09:55:37,978 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:55:37,979 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
-2016-09-08 09:55:37,980 INFO: Info: Shape of View0 :(300, 8)
-2016-09-08 09:55:37,981 INFO: Info: Shape of View1 :(300, 15)
-2016-09-08 09:55:37,982 INFO: Info: Shape of View2 :(300, 7)
-2016-09-08 09:55:37,983 INFO: Info: Shape of View3 :(300, 6)
-2016-09-08 09:55:37,983 INFO: Done: Read Database Files
-2016-09-08 09:55:37,983 INFO: Start: Determine validation split for ratio 0.7
-2016-09-08 09:55:37,987 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:55:37,987 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-08 09:55:37,987 INFO: Done: Determine validation split
-2016-09-08 09:55:37,987 INFO: Start: Determine 5 folds
-2016-09-08 09:55:37,988 INFO: Info: Shape of View0 :(300, 8)
-2016-09-08 09:55:37,988 INFO: Info: Shape of View1 :(300, 15)
-2016-09-08 09:55:37,989 INFO: Info: Shape of View2 :(300, 7)
-2016-09-08 09:55:37,989 INFO: Info: Shape of View3 :(300, 6)
-2016-09-08 09:55:37,989 INFO: Done: Read Database Files
-2016-09-08 09:55:37,989 INFO: Start: Determine validation split for ratio 0.7
-2016-09-08 09:55:37,993 INFO: Done: Determine validation split
-2016-09-08 09:55:37,993 INFO: Start: Determine 5 folds
-2016-09-08 09:55:37,995 INFO: Info: Length of Learning Sets: 170
-2016-09-08 09:55:37,995 INFO: Info: Length of Testing Sets: 41
-2016-09-08 09:55:37,995 INFO: Info: Length of Validation Set: 89
-2016-09-08 09:55:37,995 INFO: Done: Determine folds
-2016-09-08 09:55:37,995 INFO: Start: Learning with Mumbo and 5 folds
-2016-09-08 09:55:37,996 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-08 09:55:37,996 DEBUG: Start: Random search for DecisionTree on View0
-2016-09-08 09:55:37,999 INFO: Info: Length of Learning Sets: 170
-2016-09-08 09:55:38,000 INFO: Info: Length of Testing Sets: 41
-2016-09-08 09:55:38,000 INFO: Info: Length of Validation Set: 89
-2016-09-08 09:55:38,000 INFO: Done: Determine folds
-2016-09-08 09:55:38,000 INFO: Start: Learning with Fusion and 5 folds
-2016-09-08 09:55:38,000 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-08 09:55:38,000 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:55:38,054 DEBUG: Done: Random search for SGD
-2016-09-08 09:55:38,054 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:55:38,107 DEBUG: Done: Random search for SGD
-2016-09-08 09:55:38,107 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:55:38,157 DEBUG: Done: Random search for SGD
-2016-09-08 09:55:38,157 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:55:38,212 DEBUG: Done: Random search for SGD
-2016-09-08 09:55:38,284 INFO: Done: Randomsearching best settings for monoview classifiers
-2016-09-08 09:55:38,284 INFO: Start: Classification
-2016-09-08 09:55:38,284 INFO: Start: Fold number 1
-2016-09-08 09:55:38,311 INFO: Start: Classification
-2016-09-08 09:55:38,336 INFO: Done: Fold number 1
-2016-09-08 09:55:38,337 INFO: Start: Fold number 2
-2016-09-08 09:55:38,363 INFO: Start: Classification
-2016-09-08 09:55:38,389 INFO: Done: Fold number 2
-2016-09-08 09:55:38,389 INFO: Start: Fold number 3
-2016-09-08 09:55:38,416 INFO: Start: Classification
-2016-09-08 09:55:38,443 INFO: Done: Fold number 3
-2016-09-08 09:55:38,443 INFO: Start: Fold number 4
-2016-09-08 09:55:38,470 INFO: Start: Classification
-2016-09-08 09:55:38,496 INFO: Done: Fold number 4
-2016-09-08 09:55:38,496 INFO: Start: Fold number 5
-2016-09-08 09:55:38,523 INFO: Start: Classification
-2016-09-08 09:55:38,549 INFO: Done: Fold number 5
-2016-09-08 09:55:38,549 INFO: Done: Classification
-2016-09-08 09:55:38,549 INFO: Info: Time for Classification: 0[s]
-2016-09-08 09:55:38,550 INFO: Start: Result Analysis for Fusion
-2016-09-08 09:55:38,681 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 57.1764705882
- -On Test : 52.6829268293
- -On Validation : 52.1348314607
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.322967175445, 0.0184701333132, 0.322597810111, 0.335964881131
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-08 09:55:38,682 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 0d0cf3ff0c83bca73e37fc715e290390079bbb69..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.505263157895
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.505263157895
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.522222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.477777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0418655345164
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.470588235294
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.545454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.479249011858
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9f126a9ea93eea83d8098254d24706eaafb2b497..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.484210526316
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.484210526316
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0867214643554
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.450980392157
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522727272727
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.457015810277
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 79be41beb0284d7bf6eb3848621838655d406c4c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.661904761905
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.661904761905
- - Score on test : 0.477777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.727969348659
- - Score on test : 0.534653465347
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.727969348659
- - Score on test : 0.534653465347
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.338095238095
- - Score on test : 0.522222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.661904761905
- - Score on test : 0.477777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.290672377783
- - Score on test : -0.0399755963154
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.68345323741
- - Score on test : 0.473684210526
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.77868852459
- - Score on test : 0.613636363636
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.639344262295
- - Score on test : 0.480731225296
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.338095238095
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bbd624877d4bfc10a8b89482d2ec1a7829577c75..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 0.895238095238
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.477777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.905982905983
- - Score on test : 0.515463917526
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.905982905983
- - Score on test : 0.515463917526
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.104761904762
- - Score on test : 0.522222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.895238095238
- - Score on test : 0.477777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.791868570857
- - Score on test : -0.0411594726194
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.946428571429
- - Score on test : 0.471698113208
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.868852459016
- - Score on test : 0.568181818182
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.900335320417
- - Score on test : 0.479743083004
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.104761904762
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fd413a5d803bf18dd794b651f84d3eeb305d6c94..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.614285714286
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.749226006192
- - Score on test : 0.630769230769
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.749226006192
- - Score on test : 0.630769230769
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.385714285714
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.614285714286
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.201498784613
- - Score on test : -0.112653159931
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.601990049751
- - Score on test : 0.476744186047
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.991803278689
- - Score on test : 0.931818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.541356184799
- - Score on test : 0.476778656126
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.385714285714
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7b2f009cb9959d5bd993cf1b27c37272d3ffaf39..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.377777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.563265306122
- - Score on test : 0.416666666667
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.563265306122
- - Score on test : 0.416666666667
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.622222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.377777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.0481411286791
- - Score on test : -0.244017569898
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.560975609756
- - Score on test : 0.384615384615
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.565573770492
- - Score on test : 0.454545454545
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.475968703428
- - Score on test : 0.379446640316
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db536ff68f9249c6cab5a9be1ebf6c21dbb9de58..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.468085106383
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.468085106383
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.109345881217
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.44
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.445652173913
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4251899bb7a8bf556dee03c2c785c4ac0e48d076..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095534Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 8)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.616822429907
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.616822429907
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.106710653456
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.52380952381
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.75
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.548913043478
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a32b018a2505e28f037bf72ff427fc03005ce552..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.48
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.48
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.154858431981
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.428571428571
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.545454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.424901185771
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b096d574bd224677355e3b214b9cfe60865aabed..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.48
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.48
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.154858431981
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.428571428571
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.545454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.424901185771
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 376343e68cd0ba94ded748ff13a5313cc2f34cb9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.571428571429
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.6484375
- - Score on test : 0.541666666667
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.6484375
- - Score on test : 0.541666666667
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.428571428571
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.103477711187
- - Score on test : 0.0260018722022
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.619402985075
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.680327868852
- - Score on test : 0.590909090909
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.550391207154
- - Score on test : 0.512845849802
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.428571428571
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c3e2e2995350f4e7c2570de375e0fe6d36fb5526..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.477777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.909090909091
- - Score on test : 0.356164383562
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.909090909091
- - Score on test : 0.356164383562
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.1
- - Score on test : 0.522222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.477777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.805030105216
- - Score on test : -0.0560191732057
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.963302752294
- - Score on test : 0.448275862069
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.860655737705
- - Score on test : 0.295454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.907600596125
- - Score on test : 0.473814229249
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.1
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1bd711b474351f5d09ddf4a343c4f7ee989d2036..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.745341614907
- - Score on test : 0.661417322835
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.745341614907
- - Score on test : 0.661417322835
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.390476190476
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.172644893682
- - Score on test : 0.118036588599
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.506024096386
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.983606557377
- - Score on test : 0.954545454545
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.537257824143
- - Score on test : 0.53162055336
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.390476190476
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 6b6c9c97508ff479293091869f19742e26369f0a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.606557377049
- - Score on test : 0.584905660377
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.606557377049
- - Score on test : 0.584905660377
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0611028315946
- - Score on test : 0.0330758927464
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.606557377049
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.606557377049
- - Score on test : 0.704545454545
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.530551415797
- - Score on test : 0.515316205534
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5bc5811d3a8eecb74b0763c70685c8d806b52e8b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.358208955224
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.358208955224
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0385036888617
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.521739130435
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.272727272727
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.516798418972
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ccd0f1ad914c25ad47b37f5f969f4202f2a8de66..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095535Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.645161290323
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.645161290323
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0628694613462
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.909090909091
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.51976284585
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9deb12f3b14d4f8565c6168fa4a139bddf91fb16..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.436781609195
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.436781609195
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0899876638096
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.441860465116
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.431818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455039525692
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 183d09191497b8ab3804d324b510249818998073..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.441860465116
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.441860465116
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.068316965625
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.452380952381
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.431818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.465909090909
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8cf553cc1128b3958539e214301a382219781461..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.705882352941
- - Score on test : 0.619469026549
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.705882352941
- - Score on test : 0.619469026549
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.380952380952
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.189221481343
- - Score on test : 0.0665679839847
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.64
- - Score on test : 0.507246376812
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.786885245902
- - Score on test : 0.795454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.586624441133
- - Score on test : 0.528162055336
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.380952380952
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index f74150b10001487507af804c177f45b9af53b193..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.890476190476
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.4
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.904564315353
- - Score on test : 0.357142857143
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.904564315353
- - Score on test : 0.357142857143
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.109523809524
- - Score on test : 0.6
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.890476190476
- - Score on test : 0.4
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.77645053118
- - Score on test : -0.20378096045
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.915966386555
- - Score on test : 0.375
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.893442622951
- - Score on test : 0.340909090909
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.889903129657
- - Score on test : 0.39871541502
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.109523809524
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 608729cd17cff989b5994f699bcc2c9f7d249468..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.715231788079
- - Score on test : 0.676923076923
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.715231788079
- - Score on test : 0.676923076923
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0945615027077
- - Score on test : 0.210925065403
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.511627906977
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.885245901639
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.533532041729
- - Score on test : 0.54347826087
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 59671aeef690b38b65227b5dd243edfa5b9b1e5b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.433333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.604838709677
- - Score on test : 0.484848484848
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.604838709677
- - Score on test : 0.484848484848
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.566666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.433333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0354605635154
- - Score on test : -0.131720304791
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.595238095238
- - Score on test : 0.436363636364
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.614754098361
- - Score on test : 0.545454545455
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.517604321908
- - Score on test : 0.435770750988
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a085f7609062a0cf55e082cfc7420a115dad6758..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.4
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.357142857143
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.357142857143
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.6
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.20378096045
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.375
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.340909090909
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.39871541502
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.6
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cf24b718b7d42ee523ad9050390a3c2e19696090..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095536Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 7)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.538461538462
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.538461538462
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0628694613462
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.636363636364
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.470355731225
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b93b2e14fa71d3dfb0ae9bead5ea8c59f93ea5b9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 4, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.576923076923
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.576923076923
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0314347306731
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.681818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.514822134387
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ed86c3be7a62566d0a9aeb7b157f834ad4429c2d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588235294118
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588235294118
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0763602735229
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.51724137931
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.681818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.536561264822
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 81a8c71b0206a996bae52dbc3db9c74f8f8ceb77..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.681481481481
- - Score on test : 0.601941747573
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.681481481481
- - Score on test : 0.601941747573
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.127350050081
- - Score on test : 0.100829966549
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.621621621622
- - Score on test : 0.525423728814
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.754098360656
- - Score on test : 0.704545454545
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.558867362146
- - Score on test : 0.547924901186
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 5a7ef634e77ff082d71a4580ab038c25426e46c2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.9
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 4, max_depth : 20
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.577777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.910638297872
- - Score on test : 0.586956521739
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.910638297872
- - Score on test : 0.586956521739
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.1
- - Score on test : 0.422222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.9
- - Score on test : 0.577777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.800522373751
- - Score on test : 0.157426051223
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.946902654867
- - Score on test : 0.5625
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.877049180328
- - Score on test : 0.613636363636
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.904433681073
- - Score on test : 0.578557312253
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.1
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 616ff06f5c3552206bb0e28675b677ea6d137aac..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.590476190476
-accuracy_score on test : 0.488888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : elasticnet
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.488888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.739393939394
- - Score on test : 0.65671641791
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.739393939394
- - Score on test : 0.65671641791
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.511111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.590476190476
- - Score on test : 0.488888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.115457436228
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.586538461538
- - Score on test : 0.488888888889
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.511363636364
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.409523809524
- - Score on test : 0.511111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 226afbe88c8d26e49858a82280f274251102f2e3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095537Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.495238095238
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 6)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 6107
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.595419847328
- - Score on test : 0.550458715596
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.595419847328
- - Score on test : 0.550458715596
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.495238095238
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.0682438863041
- - Score on test : -0.0882242643891
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.557142857143
- - Score on test : 0.461538461538
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.639344262295
- - Score on test : 0.681818181818
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.467399403875
- - Score on test : 0.4604743083
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.504761904762
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095538Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095538Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index d2af511daa99659d08543c83537f0fa618a2ebef..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095538Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 57.1764705882
- -On Test : 52.6829268293
- -On Validation : 52.1348314607
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.322967175445, 0.0184701333132, 0.322597810111, 0.335964881131
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160908-095622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index e8dd4634725d66617aab8ac7917d7f46df47a448..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095622-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,2394 +0,0 @@
-2016-09-08 09:56:22,282 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-08 09:56:22,282 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.0001661875 Gbytes /!\
-2016-09-08 09:56:27,294 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-08 09:56:27,298 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-08 09:56:27,351 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:27,351 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:56:27,351 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:27,352 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:27,352 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:56:27,352 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:27,352 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:27,352 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:27,352 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:27,353 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:27,353 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:27,353 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:27,353 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:27,354 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:27,389 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:27,389 DEBUG: Start: Training
-2016-09-08 09:56:27,391 DEBUG: Info: Time for Training: 0.0395510196686[s]
-2016-09-08 09:56:27,391 DEBUG: Done: Training
-2016-09-08 09:56:27,391 DEBUG: Start: Predicting
-2016-09-08 09:56:27,393 DEBUG: Done: Predicting
-2016-09-08 09:56:27,393 DEBUG: Start: Getting Results
-2016-09-08 09:56:27,403 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:27,403 DEBUG: Start: Training
-2016-09-08 09:56:27,407 DEBUG: Info: Time for Training: 0.0563409328461[s]
-2016-09-08 09:56:27,407 DEBUG: Done: Training
-2016-09-08 09:56:27,407 DEBUG: Start: Predicting
-2016-09-08 09:56:27,410 DEBUG: Done: Predicting
-2016-09-08 09:56:27,410 DEBUG: Start: Getting Results
-2016-09-08 09:56:27,440 DEBUG: Done: Getting Results
-2016-09-08 09:56:27,440 INFO: Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.377777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.348837209302
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.348837209302
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.622222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.377777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.245415911539
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.365853658537
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.376804380289
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:56:27,441 INFO: Done: Result Analysis
-2016-09-08 09:56:27,452 DEBUG: Done: Getting Results
-2016-09-08 09:56:27,452 INFO: Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.377777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.622222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.377777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.249628898234
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.325581395349
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.341463414634
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.37481333997
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:56:27,452 INFO: Done: Result Analysis
-2016-09-08 09:56:27,595 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:27,595 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:27,595 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:56:27,595 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:56:27,595 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:27,595 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:27,596 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:27,596 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:27,596 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:27,596 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:27,596 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:27,596 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:27,596 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:27,596 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:27,627 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:27,627 DEBUG: Start: Training
-2016-09-08 09:56:27,628 DEBUG: Info: Time for Training: 0.0337619781494[s]
-2016-09-08 09:56:27,628 DEBUG: Done: Training
-2016-09-08 09:56:27,628 DEBUG: Start: Predicting
-2016-09-08 09:56:27,635 DEBUG: Done: Predicting
-2016-09-08 09:56:27,635 DEBUG: Start: Getting Results
-2016-09-08 09:56:27,676 DEBUG: Done: Getting Results
-2016-09-08 09:56:27,676 INFO: Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.467532467532
- - Score on test : 0.212121212121
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.467532467532
- - Score on test : 0.212121212121
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.390476190476
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.237135686833
- - Score on test : -0.218615245335
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.692307692308
- - Score on test : 0.28
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.352941176471
- - Score on test : 0.170731707317
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.602396514161
- - Score on test : 0.401692384271
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.390476190476
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:56:27,676 INFO: Done: Result Analysis
-2016-09-08 09:56:27,921 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:27,921 DEBUG: Start: Training
-2016-09-08 09:56:27,970 DEBUG: Info: Time for Training: 0.375941991806[s]
-2016-09-08 09:56:27,970 DEBUG: Done: Training
-2016-09-08 09:56:27,970 DEBUG: Start: Predicting
-2016-09-08 09:56:27,976 DEBUG: Done: Predicting
-2016-09-08 09:56:27,977 DEBUG: Start: Getting Results
-2016-09-08 09:56:28,010 DEBUG: Done: Getting Results
-2016-09-08 09:56:28,011 INFO: Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 19, max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.423529411765
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.423529411765
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0912478416452
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.409090909091
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.439024390244
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.454206072673
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,011 INFO: Done: Result Analysis
-2016-09-08 09:56:28,143 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,143 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:56:28,143 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:28,143 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,143 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:56:28,144 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:28,144 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:28,144 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:28,144 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:28,144 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:28,144 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,144 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:28,144 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:28,145 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,189 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:28,189 DEBUG: Start: Training
-2016-09-08 09:56:28,190 DEBUG: Info: Time for Training: 0.0481970310211[s]
-2016-09-08 09:56:28,190 DEBUG: Done: Training
-2016-09-08 09:56:28,190 DEBUG: Start: Predicting
-2016-09-08 09:56:28,195 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:28,195 DEBUG: Start: Training
-2016-09-08 09:56:28,214 DEBUG: Info: Time for Training: 0.0711450576782[s]
-2016-09-08 09:56:28,214 DEBUG: Done: Training
-2016-09-08 09:56:28,214 DEBUG: Start: Predicting
-2016-09-08 09:56:28,216 DEBUG: Done: Predicting
-2016-09-08 09:56:28,216 DEBUG: Start: Getting Results
-2016-09-08 09:56:28,217 DEBUG: Done: Predicting
-2016-09-08 09:56:28,217 DEBUG: Start: Getting Results
-2016-09-08 09:56:28,239 DEBUG: Done: Getting Results
-2016-09-08 09:56:28,239 INFO: Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,239 INFO: Done: Result Analysis
-2016-09-08 09:56:28,250 DEBUG: Done: Getting Results
-2016-09-08 09:56:28,250 INFO: Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.477777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.532710280374
- - Score on test : 0.459770114943
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.532710280374
- - Score on test : 0.459770114943
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.522222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.477777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0496545019224
- - Score on test : -0.0426484477255
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.508928571429
- - Score on test : 0.434782608696
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.558823529412
- - Score on test : 0.487804878049
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.524782135076
- - Score on test : 0.478596316575
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,250 INFO: Done: Result Analysis
-2016-09-08 09:56:28,387 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,387 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,387 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:56:28,387 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:56:28,387 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:28,387 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:28,388 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:28,388 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:28,388 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:28,388 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:28,388 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:28,388 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:28,388 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,388 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,433 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:28,433 DEBUG: Start: Training
-2016-09-08 09:56:28,438 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:28,438 DEBUG: Start: Training
-2016-09-08 09:56:28,450 DEBUG: Info: Time for Training: 0.0634729862213[s]
-2016-09-08 09:56:28,450 DEBUG: Done: Training
-2016-09-08 09:56:28,450 DEBUG: Start: Predicting
-2016-09-08 09:56:28,455 DEBUG: Info: Time for Training: 0.0687439441681[s]
-2016-09-08 09:56:28,455 DEBUG: Done: Training
-2016-09-08 09:56:28,455 DEBUG: Start: Predicting
-2016-09-08 09:56:28,455 DEBUG: Done: Predicting
-2016-09-08 09:56:28,455 DEBUG: Start: Getting Results
-2016-09-08 09:56:28,459 DEBUG: Done: Predicting
-2016-09-08 09:56:28,459 DEBUG: Start: Getting Results
-2016-09-08 09:56:28,491 DEBUG: Done: Getting Results
-2016-09-08 09:56:28,491 INFO: Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.409090909091
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.409090909091
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.152357995542
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.382978723404
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.439024390244
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.423593827775
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,492 INFO: Done: Result Analysis
-2016-09-08 09:56:28,505 DEBUG: Done: Getting Results
-2016-09-08 09:56:28,505 INFO: Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.475
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.475
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0555284586866
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.487179487179
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.463414634146
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.52762568442
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,505 INFO: Done: Result Analysis
-2016-09-08 09:56:28,637 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,638 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:56:28,638 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:28,638 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,638 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:56:28,638 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:28,638 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:28,638 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:28,638 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:28,639 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:28,639 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:28,639 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:28,639 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,639 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,677 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:28,677 DEBUG: Start: Training
-2016-09-08 09:56:28,680 DEBUG: Info: Time for Training: 0.0427668094635[s]
-2016-09-08 09:56:28,680 DEBUG: Done: Training
-2016-09-08 09:56:28,680 DEBUG: Start: Predicting
-2016-09-08 09:56:28,683 DEBUG: Done: Predicting
-2016-09-08 09:56:28,683 DEBUG: Start: Getting Results
-2016-09-08 09:56:28,691 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:28,691 DEBUG: Start: Training
-2016-09-08 09:56:28,696 DEBUG: Info: Time for Training: 0.0597720146179[s]
-2016-09-08 09:56:28,696 DEBUG: Done: Training
-2016-09-08 09:56:28,696 DEBUG: Start: Predicting
-2016-09-08 09:56:28,699 DEBUG: Done: Predicting
-2016-09-08 09:56:28,699 DEBUG: Start: Getting Results
-2016-09-08 09:56:28,734 DEBUG: Done: Getting Results
-2016-09-08 09:56:28,734 INFO: Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.957142857143
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.957746478873
- - Score on test : 0.444444444444
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.957746478873
- - Score on test : 0.444444444444
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0428571428571
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.957142857143
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.917792101918
- - Score on test : -0.104031856645
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.918918918919
- - Score on test : 0.408163265306
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.487804878049
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.958333333333
- - Score on test : 0.447984071677
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0428571428571
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,734 INFO: Done: Result Analysis
-2016-09-08 09:56:28,741 DEBUG: Done: Getting Results
-2016-09-08 09:56:28,741 INFO: Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.390243902439
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.390243902439
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.119960179194
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.390243902439
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.390243902439
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.440019910403
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,741 INFO: Done: Result Analysis
-2016-09-08 09:56:28,882 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,883 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:56:28,883 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:28,883 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:28,883 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:56:28,884 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:28,884 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:28,884 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:28,884 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:28,884 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:28,885 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:28,885 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:28,885 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,885 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:28,919 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:28,919 DEBUG: Start: Training
-2016-09-08 09:56:28,920 DEBUG: Info: Time for Training: 0.0378148555756[s]
-2016-09-08 09:56:28,920 DEBUG: Done: Training
-2016-09-08 09:56:28,920 DEBUG: Start: Predicting
-2016-09-08 09:56:28,927 DEBUG: Done: Predicting
-2016-09-08 09:56:28,927 DEBUG: Start: Getting Results
-2016-09-08 09:56:28,967 DEBUG: Done: Getting Results
-2016-09-08 09:56:28,967 INFO: Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.559139784946
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.559139784946
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.438095238095
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.133359904768
- - Score on test : 0.104395047556
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.5390625
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.676470588235
- - Score on test : 0.634146341463
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.565087145969
- - Score on test : 0.551767048283
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.438095238095
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:56:28,967 INFO: Done: Result Analysis
-2016-09-08 09:56:29,211 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:29,212 DEBUG: Start: Training
-2016-09-08 09:56:29,260 DEBUG: Info: Time for Training: 0.377947092056[s]
-2016-09-08 09:56:29,261 DEBUG: Done: Training
-2016-09-08 09:56:29,261 DEBUG: Start: Predicting
-2016-09-08 09:56:29,267 DEBUG: Done: Predicting
-2016-09-08 09:56:29,267 DEBUG: Start: Getting Results
-2016-09-08 09:56:29,300 DEBUG: Done: Getting Results
-2016-09-08 09:56:29,300 INFO: Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 19, max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.995073891626
- - Score on test : 0.516853932584
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.995073891626
- - Score on test : 0.516853932584
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.990510833227
- - Score on test : 0.0506833064614
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.479166666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.990196078431
- - Score on test : 0.560975609756
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.995098039216
- - Score on test : 0.525385764062
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:56:29,300 INFO: Done: Result Analysis
-2016-09-08 09:56:29,434 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,435 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:56:29,435 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:29,435 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,435 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:56:29,435 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:29,436 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:29,436 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:29,436 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:29,436 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:29,436 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,436 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:29,437 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:29,437 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,483 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:29,483 DEBUG: Start: Training
-2016-09-08 09:56:29,484 DEBUG: Info: Time for Training: 0.0506761074066[s]
-2016-09-08 09:56:29,484 DEBUG: Done: Training
-2016-09-08 09:56:29,484 DEBUG: Start: Predicting
-2016-09-08 09:56:29,491 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:29,491 DEBUG: Start: Training
-2016-09-08 09:56:29,511 DEBUG: Done: Predicting
-2016-09-08 09:56:29,511 DEBUG: Start: Getting Results
-2016-09-08 09:56:29,520 DEBUG: Info: Time for Training: 0.0860919952393[s]
-2016-09-08 09:56:29,520 DEBUG: Done: Training
-2016-09-08 09:56:29,521 DEBUG: Start: Predicting
-2016-09-08 09:56:29,526 DEBUG: Done: Predicting
-2016-09-08 09:56:29,526 DEBUG: Start: Getting Results
-2016-09-08 09:56:29,539 DEBUG: Done: Getting Results
-2016-09-08 09:56:29,539 INFO: Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:29,540 INFO: Done: Result Analysis
-2016-09-08 09:56:29,553 DEBUG: Done: Getting Results
-2016-09-08 09:56:29,554 INFO: Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.577777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.547619047619
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.547619047619
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.422222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.577777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0294117647059
- - Score on test : 0.152357995542
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.53488372093
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.529411764706
- - Score on test : 0.560975609756
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.514705882353
- - Score on test : 0.576406172225
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
-2016-09-08 09:56:29,554 INFO: Done: Result Analysis
-2016-09-08 09:56:29,683 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,684 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,684 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:56:29,684 DEBUG: ### Classification - Database:Fake Feature:View1 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:56:29,684 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:29,684 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:29,685 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:29,685 DEBUG: Info: Shape X_train:(210, 19), Length of y_train:210
-2016-09-08 09:56:29,685 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:29,685 DEBUG: Info: Shape X_test:(90, 19), Length of y_test:90
-2016-09-08 09:56:29,686 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:29,686 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:29,686 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,686 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,756 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:29,756 DEBUG: Start: Training
-2016-09-08 09:56:29,762 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:29,762 DEBUG: Start: Training
-2016-09-08 09:56:29,781 DEBUG: Info: Time for Training: 0.0979940891266[s]
-2016-09-08 09:56:29,781 DEBUG: Done: Training
-2016-09-08 09:56:29,781 DEBUG: Start: Predicting
-2016-09-08 09:56:29,788 DEBUG: Info: Time for Training: 0.105060100555[s]
-2016-09-08 09:56:29,788 DEBUG: Done: Training
-2016-09-08 09:56:29,788 DEBUG: Start: Predicting
-2016-09-08 09:56:29,790 DEBUG: Done: Predicting
-2016-09-08 09:56:29,790 DEBUG: Start: Getting Results
-2016-09-08 09:56:29,793 DEBUG: Done: Predicting
-2016-09-08 09:56:29,794 DEBUG: Start: Getting Results
-2016-09-08 09:56:29,827 DEBUG: Done: Getting Results
-2016-09-08 09:56:29,827 DEBUG: Done: Getting Results
-2016-09-08 09:56:29,827 INFO: Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.394366197183
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.394366197183
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0157759322964
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.341463414634
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.507466401195
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:56:29,827 INFO: Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0193709711057
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.44
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.268292682927
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.491289198606
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:56:29,827 INFO: Done: Result Analysis
-2016-09-08 09:56:29,828 INFO: Done: Result Analysis
-2016-09-08 09:56:29,934 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,934 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:56:29,934 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:29,934 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:29,935 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:56:29,935 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:29,935 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:29,935 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:29,936 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:29,936 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:29,936 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:29,936 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,936 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:29,936 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:29,974 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:29,974 DEBUG: Start: Training
-2016-09-08 09:56:29,976 DEBUG: Info: Time for Training: 0.0422959327698[s]
-2016-09-08 09:56:29,976 DEBUG: Done: Training
-2016-09-08 09:56:29,976 DEBUG: Start: Predicting
-2016-09-08 09:56:29,979 DEBUG: Done: Predicting
-2016-09-08 09:56:29,979 DEBUG: Start: Getting Results
-2016-09-08 09:56:29,988 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:29,988 DEBUG: Start: Training
-2016-09-08 09:56:29,994 DEBUG: Info: Time for Training: 0.0606279373169[s]
-2016-09-08 09:56:29,994 DEBUG: Done: Training
-2016-09-08 09:56:29,994 DEBUG: Start: Predicting
-2016-09-08 09:56:29,997 DEBUG: Done: Predicting
-2016-09-08 09:56:29,997 DEBUG: Start: Getting Results
-2016-09-08 09:56:30,029 DEBUG: Done: Getting Results
-2016-09-08 09:56:30,029 INFO: Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.385542168675
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.385542168675
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.140124435511
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.380952380952
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.390243902439
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.429815828771
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,029 INFO: Done: Result Analysis
-2016-09-08 09:56:30,043 DEBUG: Done: Getting Results
-2016-09-08 09:56:30,043 INFO: Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.375
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.375
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.124563839757
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.384615384615
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.365853658537
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.438028870085
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,044 INFO: Done: Result Analysis
-2016-09-08 09:56:30,185 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,186 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:56:30,186 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,186 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:30,186 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:56:30,186 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:30,187 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,187 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,187 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,187 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,187 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:30,187 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:30,187 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:30,188 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:30,236 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:30,237 DEBUG: Start: Training
-2016-09-08 09:56:30,238 DEBUG: Info: Time for Training: 0.0538048744202[s]
-2016-09-08 09:56:30,238 DEBUG: Done: Training
-2016-09-08 09:56:30,238 DEBUG: Start: Predicting
-2016-09-08 09:56:30,249 DEBUG: Done: Predicting
-2016-09-08 09:56:30,249 DEBUG: Start: Getting Results
-2016-09-08 09:56:30,288 DEBUG: Done: Getting Results
-2016-09-08 09:56:30,288 INFO: Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.323943661972
- - Score on test : 0.327272727273
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.323943661972
- - Score on test : 0.327272727273
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0866552427925
- - Score on test : 0.161417724438
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.575
- - Score on test : 0.642857142857
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.225490196078
- - Score on test : 0.219512195122
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.534041394336
- - Score on test : 0.558735689398
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,289 INFO: Done: Result Analysis
-2016-09-08 09:56:30,545 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:30,545 DEBUG: Start: Training
-2016-09-08 09:56:30,594 DEBUG: Info: Time for Training: 0.40927195549[s]
-2016-09-08 09:56:30,594 DEBUG: Done: Training
-2016-09-08 09:56:30,594 DEBUG: Start: Predicting
-2016-09-08 09:56:30,600 DEBUG: Done: Predicting
-2016-09-08 09:56:30,600 DEBUG: Start: Getting Results
-2016-09-08 09:56:30,633 DEBUG: Done: Getting Results
-2016-09-08 09:56:30,633 INFO: Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 19, max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.55421686747
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.55421686747
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.172919516163
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.547619047619
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.560975609756
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.586610253858
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,633 INFO: Done: Result Analysis
-2016-09-08 09:56:30,727 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,727 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:56:30,727 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:30,727 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,728 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:56:30,728 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:30,728 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,728 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,728 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:30,728 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:30,728 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,728 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,729 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:30,729 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:30,773 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:30,773 DEBUG: Start: Training
-2016-09-08 09:56:30,774 DEBUG: Info: Time for Training: 0.0476858615875[s]
-2016-09-08 09:56:30,774 DEBUG: Done: Training
-2016-09-08 09:56:30,774 DEBUG: Start: Predicting
-2016-09-08 09:56:30,779 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:30,779 DEBUG: Start: Training
-2016-09-08 09:56:30,796 DEBUG: Done: Predicting
-2016-09-08 09:56:30,796 DEBUG: Start: Getting Results
-2016-09-08 09:56:30,800 DEBUG: Info: Time for Training: 0.0737199783325[s]
-2016-09-08 09:56:30,800 DEBUG: Done: Training
-2016-09-08 09:56:30,801 DEBUG: Start: Predicting
-2016-09-08 09:56:30,804 DEBUG: Done: Predicting
-2016-09-08 09:56:30,804 DEBUG: Start: Getting Results
-2016-09-08 09:56:30,819 DEBUG: Done: Getting Results
-2016-09-08 09:56:30,819 INFO: Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,819 INFO: Done: Result Analysis
-2016-09-08 09:56:30,833 DEBUG: Done: Getting Results
-2016-09-08 09:56:30,833 INFO: Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.514851485149
- - Score on test : 0.543209876543
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.514851485149
- - Score on test : 0.543209876543
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0654069940168
- - Score on test : 0.169618786115
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.52
- - Score on test : 0.55
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.509803921569
- - Score on test : 0.536585365854
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.532679738562
- - Score on test : 0.584619213539
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-08 09:56:30,833 INFO: Done: Result Analysis
-2016-09-08 09:56:30,973 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,973 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:30,974 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
-2016-09-08 09:56:30,974 DEBUG: ### Classification - Database:Fake Feature:View2 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
-2016-09-08 09:56:30,974 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:30,974 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:30,974 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,974 DEBUG: Info: Shape X_train:(210, 20), Length of y_train:210
-2016-09-08 09:56:30,974 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,974 DEBUG: Info: Shape X_test:(90, 20), Length of y_test:90
-2016-09-08 09:56:30,975 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:30,975 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:30,975 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:30,975 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:31,020 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:31,020 DEBUG: Start: Training
-2016-09-08 09:56:31,024 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:31,024 DEBUG: Start: Training
-2016-09-08 09:56:31,039 DEBUG: Info: Time for Training: 0.0657398700714[s]
-2016-09-08 09:56:31,039 DEBUG: Done: Training
-2016-09-08 09:56:31,039 DEBUG: Start: Predicting
-2016-09-08 09:56:31,043 DEBUG: Info: Time for Training: 0.0697932243347[s]
-2016-09-08 09:56:31,043 DEBUG: Done: Training
-2016-09-08 09:56:31,043 DEBUG: Start: Predicting
-2016-09-08 09:56:31,045 DEBUG: Done: Predicting
-2016-09-08 09:56:31,045 DEBUG: Start: Getting Results
-2016-09-08 09:56:31,047 DEBUG: Done: Predicting
-2016-09-08 09:56:31,047 DEBUG: Start: Getting Results
-2016-09-08 09:56:31,077 DEBUG: Done: Getting Results
-2016-09-08 09:56:31,077 INFO: Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588235294118
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588235294118
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0350036509618
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.448717948718
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.853658536585
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488053758089
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,077 INFO: Done: Result Analysis
-2016-09-08 09:56:31,077 DEBUG: Done: Getting Results
-2016-09-08 09:56:31,077 INFO: Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.453333333333
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.453333333333
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0695369227879
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.414634146341
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533847685416
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,078 INFO: Done: Result Analysis
-2016-09-08 09:56:31,220 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:31,220 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:31,220 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:56:31,220 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:56:31,221 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:31,221 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:31,221 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:31,221 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:31,221 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:31,221 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:31,221 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:31,222 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:31,222 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:31,222 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:31,257 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:31,257 DEBUG: Start: Training
-2016-09-08 09:56:31,259 DEBUG: Info: Time for Training: 0.0391139984131[s]
-2016-09-08 09:56:31,259 DEBUG: Done: Training
-2016-09-08 09:56:31,259 DEBUG: Start: Predicting
-2016-09-08 09:56:31,262 DEBUG: Done: Predicting
-2016-09-08 09:56:31,262 DEBUG: Start: Getting Results
-2016-09-08 09:56:31,270 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:31,270 DEBUG: Start: Training
-2016-09-08 09:56:31,274 DEBUG: Info: Time for Training: 0.0547120571136[s]
-2016-09-08 09:56:31,275 DEBUG: Done: Training
-2016-09-08 09:56:31,275 DEBUG: Start: Predicting
-2016-09-08 09:56:31,277 DEBUG: Done: Predicting
-2016-09-08 09:56:31,277 DEBUG: Start: Getting Results
-2016-09-08 09:56:31,302 DEBUG: Done: Getting Results
-2016-09-08 09:56:31,302 INFO: Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.985507246377
- - Score on test : 0.516853932584
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.985507246377
- - Score on test : 0.516853932584
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0142857142857
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.971825315808
- - Score on test : 0.0506833064614
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.479166666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.560975609756
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.986111111111
- - Score on test : 0.525385764062
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0142857142857
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,303 INFO: Done: Result Analysis
-2016-09-08 09:56:31,313 DEBUG: Done: Getting Results
-2016-09-08 09:56:31,314 INFO: Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511627906977
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511627906977
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.066931612238
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.536585365854
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533598805376
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,314 INFO: Done: Result Analysis
-2016-09-08 09:56:31,468 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:31,468 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
-2016-09-08 09:56:31,468 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:31,468 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:31,469 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
-2016-09-08 09:56:31,469 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:31,469 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:31,469 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:31,469 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:31,469 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:31,469 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:31,469 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:31,470 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:31,470 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:31,503 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:31,503 DEBUG: Start: Training
-2016-09-08 09:56:31,504 DEBUG: Info: Time for Training: 0.0364670753479[s]
-2016-09-08 09:56:31,504 DEBUG: Done: Training
-2016-09-08 09:56:31,504 DEBUG: Start: Predicting
-2016-09-08 09:56:31,511 DEBUG: Done: Predicting
-2016-09-08 09:56:31,511 DEBUG: Start: Getting Results
-2016-09-08 09:56:31,565 DEBUG: Done: Getting Results
-2016-09-08 09:56:31,565 INFO: Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.505050505051
- - Score on test : 0.543209876543
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.505050505051
- - Score on test : 0.543209876543
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0644812208979
- - Score on test : 0.169618786115
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.520833333333
- - Score on test : 0.55
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.490196078431
- - Score on test : 0.536585365854
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.532135076253
- - Score on test : 0.584619213539
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,565 INFO: Done: Result Analysis
-2016-09-08 09:56:31,800 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:31,800 DEBUG: Start: Training
-2016-09-08 09:56:31,848 DEBUG: Info: Time for Training: 0.380412101746[s]
-2016-09-08 09:56:31,848 DEBUG: Done: Training
-2016-09-08 09:56:31,848 DEBUG: Start: Predicting
-2016-09-08 09:56:31,855 DEBUG: Done: Predicting
-2016-09-08 09:56:31,855 DEBUG: Start: Getting Results
-2016-09-08 09:56:31,882 DEBUG: Done: Getting Results
-2016-09-08 09:56:31,882 INFO: Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 19, max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.99512195122
- - Score on test : 0.488372093023
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.99512195122
- - Score on test : 0.488372093023
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.990515975943
- - Score on test : 0.0223105374127
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.990291262136
- - Score on test : 0.466666666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.99537037037
- - Score on test : 0.511199601792
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
-2016-09-08 09:56:31,882 INFO: Done: Result Analysis
-2016-09-08 09:56:32,016 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:32,016 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:56:32,016 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
-2016-09-08 09:56:32,016 DEBUG: ### Classification - Database:Fake Feature:View3 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
-2016-09-08 09:56:32,016 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:32,016 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:56:32,017 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:32,017 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:56:32,017 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:32,017 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:56:32,017 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:32,017 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:56:32,017 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:32,017 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:56:32,062 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:32,062 DEBUG: Start: Training
-2016-09-08 09:56:32,062 DEBUG: Info: Time for Training: 0.04727602005[s]
-2016-09-08 09:56:32,063 DEBUG: Done: Training
-2016-09-08 09:56:32,063 DEBUG: Start: Predicting
-2016-09-08 09:56:32,066 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:56:32,067 DEBUG: Start: Training
-2016-09-08 09:56:32,075 DEBUG: Done: Predicting
-2016-09-08 09:56:32,076 DEBUG: Start: Getting Results
-2016-09-08 09:56:32,089 DEBUG: Info: Time for Training: 0.0742139816284[s]
-2016-09-08 09:56:32,090 DEBUG: Done: Training
-2016-09-08 09:56:32,090 DEBUG: Start: Predicting
-2016-09-08 09:56:32,093 DEBUG: Done: Predicting
-2016-09-08 09:56:32,093 DEBUG: Start: Getting Results
-2016-09-08 09:56:32,100 DEBUG: Done: Getting Results
-2016-09-08 09:56:32,100 INFO: Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
-2016-09-08 09:56:32,100 INFO: Done: Result Analysis
-2016-09-08 09:56:32,122 DEBUG: Done: Getting Results
-2016-09-08 09:56:32,122 INFO: Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.566666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.520408163265
- - Score on test : 0.506329113924
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.520408163265
- - Score on test : 0.506329113924
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.447619047619
- - Score on test : 0.433333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.566666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.10237359911
- - Score on test : 0.121459622637
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.542553191489
- - Score on test : 0.526315789474
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.487804878049
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.550925925926
- - Score on test : 0.560228969637
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.447619047619
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
-2016-09-08 09:56:32,122 INFO: Done: Result Analysis
-2016-09-08 09:56:32,424 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:56:32,425 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-08 09:56:32,426 INFO: Info: Shape of View0 :(300, 12)
-2016-09-08 09:56:32,426 INFO: Info: Shape of View1 :(300, 19)
-2016-09-08 09:56:32,427 INFO: Info: Shape of View2 :(300, 20)
-2016-09-08 09:56:32,427 INFO: Info: Shape of View3 :(300, 12)
-2016-09-08 09:56:32,428 INFO: Done: Read Database Files
-2016-09-08 09:56:32,428 INFO: Start: Determine validation split for ratio 0.7
-2016-09-08 09:56:32,431 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:56:32,432 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-08 09:56:32,432 INFO: Info: Shape of View0 :(300, 12)
-2016-09-08 09:56:32,433 INFO: Done: Determine validation split
-2016-09-08 09:56:32,433 INFO: Start: Determine 5 folds
-2016-09-08 09:56:32,433 INFO: Info: Shape of View1 :(300, 19)
-2016-09-08 09:56:32,434 INFO: Info: Shape of View2 :(300, 20)
-2016-09-08 09:56:32,434 INFO: Info: Shape of View3 :(300, 12)
-2016-09-08 09:56:32,434 INFO: Done: Read Database Files
-2016-09-08 09:56:32,434 INFO: Start: Determine validation split for ratio 0.7
-2016-09-08 09:56:32,439 INFO: Done: Determine validation split
-2016-09-08 09:56:32,439 INFO: Start: Determine 5 folds
-2016-09-08 09:56:32,445 INFO: Info: Length of Learning Sets: 169
-2016-09-08 09:56:32,445 INFO: Info: Length of Testing Sets: 42
-2016-09-08 09:56:32,445 INFO: Info: Length of Validation Set: 89
-2016-09-08 09:56:32,445 INFO: Done: Determine folds
-2016-09-08 09:56:32,445 INFO: Start: Learning with Fusion and 5 folds
-2016-09-08 09:56:32,445 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:32,445 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:32,448 INFO: Info: Length of Learning Sets: 169
-2016-09-08 09:56:32,448 INFO: Info: Length of Testing Sets: 42
-2016-09-08 09:56:32,449 INFO: Info: Length of Validation Set: 89
-2016-09-08 09:56:32,449 INFO: Done: Determine folds
-2016-09-08 09:56:32,449 INFO: Start: Learning with Fusion and 5 folds
-2016-09-08 09:56:32,449 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:32,449 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:32,500 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:32,501 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:32,504 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:32,504 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:32,551 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:32,551 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:32,556 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:32,556 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:32,600 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:32,600 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:32,607 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:32,607 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:32,651 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:32,660 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:32,717 INFO: Done: Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:32,717 INFO: Start: Classification
-2016-09-08 09:56:32,717 INFO: Start: Fold number 1
-2016-09-08 09:56:32,745 INFO: Start: Classification
-2016-09-08 09:56:32,751 INFO: Done: Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:32,751 INFO: Start: Classification
-2016-09-08 09:56:32,751 INFO: Start: Fold number 1
-2016-09-08 09:56:32,771 INFO: Done: Fold number 1
-2016-09-08 09:56:32,772 INFO: Start: Fold number 2
-2016-09-08 09:56:32,778 INFO: Start: Classification
-2016-09-08 09:56:32,798 INFO: Start: Classification
-2016-09-08 09:56:32,824 INFO: Done: Fold number 2
-2016-09-08 09:56:32,824 INFO: Start: Fold number 3
-2016-09-08 09:56:32,847 INFO: Done: Fold number 1
-2016-09-08 09:56:32,848 INFO: Start: Fold number 2
-2016-09-08 09:56:32,850 INFO: Start: Classification
-2016-09-08 09:56:32,874 INFO: Start: Classification
-2016-09-08 09:56:32,876 INFO: Done: Fold number 3
-2016-09-08 09:56:32,877 INFO: Start: Fold number 4
-2016-09-08 09:56:32,903 INFO: Start: Classification
-2016-09-08 09:56:32,928 INFO: Done: Fold number 4
-2016-09-08 09:56:32,928 INFO: Start: Fold number 5
-2016-09-08 09:56:32,944 INFO: Done: Fold number 2
-2016-09-08 09:56:32,944 INFO: Start: Fold number 3
-2016-09-08 09:56:32,955 INFO: Start: Classification
-2016-09-08 09:56:32,971 INFO: Start: Classification
-2016-09-08 09:56:32,981 INFO: Done: Fold number 5
-2016-09-08 09:56:32,981 INFO: Done: Classification
-2016-09-08 09:56:32,981 INFO: Info: Time for Classification: 0[s]
-2016-09-08 09:56:32,981 INFO: Start: Result Analysis for Fusion
-2016-09-08 09:56:33,041 INFO: Done: Fold number 3
-2016-09-08 09:56:33,041 INFO: Start: Fold number 4
-2016-09-08 09:56:33,068 INFO: Start: Classification
-2016-09-08 09:56:33,109 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 64.9704142012
- -On Test : 42.380952381
- -On Validation : 48.7640449438
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.241839908393, 0.362121620258, 0.0533308084229, 0.342707662926
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : log, penalty : l2
- - SGDClassifier with loss : modified_huber, penalty : l2
- - SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-08 09:56:33,109 INFO: Done: Result Analysis
-2016-09-08 09:56:33,137 INFO: Done: Fold number 4
-2016-09-08 09:56:33,137 INFO: Start: Fold number 5
-2016-09-08 09:56:33,163 INFO: Start: Classification
-2016-09-08 09:56:33,229 INFO: Done: Fold number 5
-2016-09-08 09:56:33,229 INFO: Done: Classification
-2016-09-08 09:56:33,229 INFO: Info: Time for Classification: 0[s]
-2016-09-08 09:56:33,229 INFO: Start: Result Analysis for Fusion
-2016-09-08 09:56:33,351 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 62.2485207101
- -On Test : 47.619047619
- -On Validation : 52.1348314607
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : log, penalty : l2
- - SGDClassifier with loss : modified_huber, penalty : l2
- - SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-08 09:56:33,351 INFO: Done: Result Analysis
-2016-09-08 09:56:33,471 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:56:33,471 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-08 09:56:33,472 INFO: Info: Shape of View0 :(300, 12)
-2016-09-08 09:56:33,472 INFO: Info: Shape of View1 :(300, 19)
-2016-09-08 09:56:33,473 INFO: Info: Shape of View2 :(300, 20)
-2016-09-08 09:56:33,473 INFO: Info: Shape of View3 :(300, 12)
-2016-09-08 09:56:33,473 INFO: Done: Read Database Files
-2016-09-08 09:56:33,474 INFO: Start: Determine validation split for ratio 0.7
-2016-09-08 09:56:33,478 INFO: ### Main Programm for Multiview Classification
-2016-09-08 09:56:33,478 INFO: ### Classification - Database : Fake ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
-2016-09-08 09:56:33,479 INFO: Info: Shape of View0 :(300, 12)
-2016-09-08 09:56:33,480 INFO: Info: Shape of View1 :(300, 19)
-2016-09-08 09:56:33,481 INFO: Done: Determine validation split
-2016-09-08 09:56:33,481 INFO: Start: Determine 5 folds
-2016-09-08 09:56:33,481 INFO: Info: Shape of View2 :(300, 20)
-2016-09-08 09:56:33,482 INFO: Info: Shape of View3 :(300, 12)
-2016-09-08 09:56:33,482 INFO: Done: Read Database Files
-2016-09-08 09:56:33,482 INFO: Start: Determine validation split for ratio 0.7
-2016-09-08 09:56:33,485 INFO: Done: Determine validation split
-2016-09-08 09:56:33,485 INFO: Start: Determine 5 folds
-2016-09-08 09:56:33,488 INFO: Info: Length of Learning Sets: 169
-2016-09-08 09:56:33,489 INFO: Info: Length of Testing Sets: 42
-2016-09-08 09:56:33,489 INFO: Info: Length of Validation Set: 89
-2016-09-08 09:56:33,489 INFO: Done: Determine folds
-2016-09-08 09:56:33,489 INFO: Start: Learning with Fusion and 5 folds
-2016-09-08 09:56:33,489 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:33,489 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:33,495 INFO: Info: Length of Learning Sets: 169
-2016-09-08 09:56:33,495 INFO: Info: Length of Testing Sets: 42
-2016-09-08 09:56:33,495 INFO: Info: Length of Validation Set: 89
-2016-09-08 09:56:33,495 INFO: Done: Determine folds
-2016-09-08 09:56:33,496 INFO: Start: Learning with Fusion and 5 folds
-2016-09-08 09:56:33,496 INFO: Start: Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:33,496 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:33,544 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:33,545 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:33,550 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:33,550 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:33,596 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:33,596 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:33,600 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:33,601 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:33,646 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:33,646 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:33,650 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:33,650 DEBUG: Start: Random search for SGD with 1 iterations
-2016-09-08 09:56:33,698 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:33,698 INFO: Done: Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:33,698 INFO: Start: Classification
-2016-09-08 09:56:33,699 INFO: Start: Fold number 1
-2016-09-08 09:56:33,702 DEBUG: Done: Random search for SGD
-2016-09-08 09:56:33,760 INFO: Start: Classification
-2016-09-08 09:56:33,767 INFO: Done: Randomsearching best settings for monoview classifiers
-2016-09-08 09:56:33,767 INFO: Start: Classification
-2016-09-08 09:56:33,767 INFO: Start: Fold number 1
-2016-09-08 09:56:33,790 INFO: Done: Fold number 1
-2016-09-08 09:56:33,790 INFO: Start: Fold number 2
-2016-09-08 09:56:33,794 INFO: Start: Classification
-2016-09-08 09:56:33,825 INFO: Done: Fold number 1
-2016-09-08 09:56:33,825 INFO: Start: Fold number 2
-2016-09-08 09:56:33,835 INFO: Start: Classification
-2016-09-08 09:56:33,851 INFO: Start: Classification
-2016-09-08 09:56:33,864 INFO: Done: Fold number 2
-2016-09-08 09:56:33,865 INFO: Start: Fold number 3
-2016-09-08 09:56:33,882 INFO: Done: Fold number 2
-2016-09-08 09:56:33,882 INFO: Start: Fold number 3
-2016-09-08 09:56:33,908 INFO: Start: Classification
-2016-09-08 09:56:33,909 INFO: Start: Classification
-2016-09-08 09:56:33,938 INFO: Done: Fold number 3
-2016-09-08 09:56:33,938 INFO: Start: Fold number 4
-2016-09-08 09:56:33,938 INFO: Done: Fold number 3
-2016-09-08 09:56:33,939 INFO: Start: Fold number 4
-2016-09-08 09:56:33,965 INFO: Start: Classification
-2016-09-08 09:56:33,984 INFO: Start: Classification
-2016-09-08 09:56:33,997 INFO: Done: Fold number 4
-2016-09-08 09:56:33,997 INFO: Start: Fold number 5
-2016-09-08 09:56:34,014 INFO: Done: Fold number 4
-2016-09-08 09:56:34,014 INFO: Start: Fold number 5
-2016-09-08 09:56:34,024 INFO: Start: Classification
-2016-09-08 09:56:34,054 INFO: Done: Fold number 5
-2016-09-08 09:56:34,054 INFO: Done: Classification
-2016-09-08 09:56:34,054 INFO: Info: Time for Classification: 0[s]
-2016-09-08 09:56:34,055 INFO: Start: Result Analysis for Fusion
-2016-09-08 09:56:34,059 INFO: Start: Classification
-2016-09-08 09:56:34,089 INFO: Done: Fold number 5
-2016-09-08 09:56:34,089 INFO: Done: Classification
-2016-09-08 09:56:34,089 INFO: Info: Time for Classification: 0[s]
-2016-09-08 09:56:34,089 INFO: Start: Result Analysis for Fusion
-2016-09-08 09:56:34,221 INFO: Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 63.4319526627
- -On Test : 47.619047619
- -On Validation : 44.2696629213
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : log, penalty : l2
- - SGDClassifier with loss : modified_huber, penalty : l2
- - SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
-
-2016-09-08 09:56:34,222 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 03f454c1907151b2cd4700f0b9fa21f634a895a3..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.377777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.622222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.377777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.249628898234
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.325581395349
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.341463414634
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.37481333997
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1121217156862cbd341038acd8d0bb7420aea4f5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.377777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.377777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.348837209302
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.348837209302
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.622222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.377777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.245415911539
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.365853658537
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.376804380289
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.622222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 717561a91111c362546c229613b377405c534f58..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095627Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.609523809524
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.467532467532
- - Score on test : 0.212121212121
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.467532467532
- - Score on test : 0.212121212121
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.390476190476
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.609523809524
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.237135686833
- - Score on test : -0.218615245335
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.692307692308
- - Score on test : 0.28
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.352941176471
- - Score on test : 0.170731707317
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.602396514161
- - Score on test : 0.401692384271
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.390476190476
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index db639d874780820a793d5b425131f599b619d1a1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.390243902439
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.390243902439
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.119960179194
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.390243902439
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.390243902439
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.440019910403
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 63f60667fbbb850f1c17b27d456a7ad94bf2a1cd..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 0.957142857143
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.957142857143
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.957746478873
- - Score on test : 0.444444444444
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.957746478873
- - Score on test : 0.444444444444
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0428571428571
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.957142857143
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.917792101918
- - Score on test : -0.104031856645
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.918918918919
- - Score on test : 0.408163265306
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.487804878049
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.958333333333
- - Score on test : 0.447984071677
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0428571428571
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 450f0ea162ee5681ba44e7e36c2da99ff7c208db..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.561904761905
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.559139784946
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.559139784946
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.438095238095
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.561904761905
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.133359904768
- - Score on test : 0.104395047556
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.5390625
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.676470588235
- - Score on test : 0.634146341463
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.565087145969
- - Score on test : 0.551767048283
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.438095238095
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3d2d8158208053f14d8d289f1b53da5a0ff267a7..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 19, max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.423529411765
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.423529411765
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0912478416452
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.409090909091
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.439024390244
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.454206072673
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 511f2feec38b0ab80eca16401cbe7d50a5a52472..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 2bda3cf3e9865595e5900f4a617e2f7210d1b111..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.52380952381
-accuracy_score on test : 0.477777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.477777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.532710280374
- - Score on test : 0.459770114943
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.532710280374
- - Score on test : 0.459770114943
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.522222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.477777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0496545019224
- - Score on test : -0.0426484477255
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.508928571429
- - Score on test : 0.434782608696
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.558823529412
- - Score on test : 0.487804878049
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.524782135076
- - Score on test : 0.478596316575
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.522222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 59cdb6c697d146d7d3ed98f76ae3654d60df8f00..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.475
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.475
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0555284586866
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.487179487179
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.463414634146
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.52762568442
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a96b05b6ca83ab7a200d3d56ec086a92a28bbee4..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095628Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.409090909091
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.409090909091
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.152357995542
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.382978723404
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.439024390244
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.423593827775
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 46f9acd1a6f415e8ac5c14de01639553c10c8914..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 19, max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.995073891626
- - Score on test : 0.516853932584
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.995073891626
- - Score on test : 0.516853932584
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.990510833227
- - Score on test : 0.0506833064614
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.479166666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.990196078431
- - Score on test : 0.560975609756
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.995098039216
- - Score on test : 0.525385764062
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c2daea4e1eb4967649e0a7bd2c099f23f75f51e8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 65a1d806d6c532ff7273d35788ad31ff125bf076..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.514285714286
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.577777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.547619047619
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.547619047619
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.422222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.577777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0294117647059
- - Score on test : 0.152357995542
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.53488372093
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.529411764706
- - Score on test : 0.560975609756
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.514705882353
- - Score on test : 0.576406172225
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ffa02970261e5fd6c6ee7c7c89f28a71c8c1cb80..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.394366197183
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.394366197183
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0157759322964
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.466666666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.341463414634
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.507466401195
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 664dd67dd1b310bf15826196f3f32bed9c3db30e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095629Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 19)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0193709711057
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.44
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.268292682927
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.491289198606
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1026306a026375c58d260dba33e7e4dd921b4d8c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.375
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.375
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.124563839757
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.384615384615
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.365853658537
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.438028870085
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index e47d02bff46273cea19d86bfd2d436741ac501b5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.385542168675
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.385542168675
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.140124435511
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.380952380952
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.390243902439
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.429815828771
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 15b07a9ccd577250db3514055248e247ee8ca5ac..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.542857142857
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.323943661972
- - Score on test : 0.327272727273
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.323943661972
- - Score on test : 0.327272727273
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.542857142857
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0866552427925
- - Score on test : 0.161417724438
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.575
- - Score on test : 0.642857142857
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.225490196078
- - Score on test : 0.219512195122
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.534041394336
- - Score on test : 0.558735689398
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.457142857143
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 70517c8c47fe2344c26b87465cad2ea40f5e082b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 19, max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.55421686747
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.55421686747
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.172919516163
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.547619047619
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.560975609756
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.586610253858
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fd86ceca50e091c5d36f8d06c22854be9865701e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ea0b1413c2f836e668154e840087b108a965c0cf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095630Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.514851485149
- - Score on test : 0.543209876543
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.514851485149
- - Score on test : 0.543209876543
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0654069940168
- - Score on test : 0.169618786115
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.52
- - Score on test : 0.55
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.509803921569
- - Score on test : 0.536585365854
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.532679738562
- - Score on test : 0.584619213539
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 7ea3a02f3a5aa6f8340cf74af6bcb60cdc9941ac..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 10, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511627906977
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511627906977
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.066931612238
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488888888889
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.536585365854
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533598805376
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a3cc29473420a6a6e84a2cba2dca5272ed452636..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 0.985714285714
-accuracy_score on test : 0.522222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.522222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.985507246377
- - Score on test : 0.516853932584
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.985507246377
- - Score on test : 0.516853932584
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0142857142857
- - Score on test : 0.477777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.985714285714
- - Score on test : 0.522222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.971825315808
- - Score on test : 0.0506833064614
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.971428571429
- - Score on test : 0.479166666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.560975609756
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.986111111111
- - Score on test : 0.525385764062
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0142857142857
- - Score on test : 0.477777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 889e2b6cb95717c6978b1c1df26d27a2cf59c58b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.533333333333
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 42
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.505050505051
- - Score on test : 0.543209876543
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.505050505051
- - Score on test : 0.543209876543
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0644812208979
- - Score on test : 0.169618786115
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.520833333333
- - Score on test : 0.55
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.490196078431
- - Score on test : 0.536585365854
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.532135076253
- - Score on test : 0.584619213539
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.466666666667
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index df0c84da34a8bbfff60cea62bfe7affe7827d513..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 19, max_depth : 10
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.99512195122
- - Score on test : 0.488372093023
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.99512195122
- - Score on test : 0.488372093023
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.990515975943
- - Score on test : 0.0223105374127
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.990291262136
- - Score on test : 0.466666666667
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.512195121951
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.99537037037
- - Score on test : 0.511199601792
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 58bf957cbce20026512b4f37e8e68de25eb1edc6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588235294118
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588235294118
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0350036509618
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.448717948718
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.853658536585
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.488053758089
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b171204485ec1e649cf7ce79673da7cf18354e76..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095631Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 20)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.453333333333
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.453333333333
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0695369227879
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.414634146341
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533847685416
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ac66b07e027445e598fa09e5c12feee404ee19f5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : log, penalty : l1
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.653846153846
- - Score on test : 0.625954198473
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.455555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 1.0
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c4a246c96517e4894fffb96593914dc482cc196e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095632Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.552380952381
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 8627
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.566666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.520408163265
- - Score on test : 0.506329113924
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.520408163265
- - Score on test : 0.506329113924
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.447619047619
- - Score on test : 0.433333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.552380952381
- - Score on test : 0.566666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.10237359911
- - Score on test : 0.121459622637
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.542553191489
- - Score on test : 0.526315789474
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.5
- - Score on test : 0.487804878049
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.550925925926
- - Score on test : 0.560228969637
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.447619047619
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 5ec167846b7f8da92aa16cc60fec7b475c81e8a8..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 64.9704142012
- -On Test : 42.380952381
- -On Validation : 48.7640449438
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.241839908393, 0.362121620258, 0.0533308084229, 0.342707662926
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : log, penalty : l2
- - SGDClassifier with loss : modified_huber, penalty : l2
- - SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index f9c3ded12ed06616f706139519336a54251ed8d1..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095633Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 62.2485207101
- -On Test : 47.619047619
- -On Validation : 52.1348314607
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : log, penalty : l2
- - SGDClassifier with loss : modified_huber, penalty : l2
- - SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095634Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-095634Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 457213b07a7b6ea964f2fc98f31d8784d8005e60..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095634Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 63.4319526627
- -On Test : 47.619047619
- -On Validation : 44.2696629213
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : elasticnet
- - SGDClassifier with loss : log, penalty : l2
- - SGDClassifier with loss : modified_huber, penalty : l2
- - SGDClassifier with loss : log, penalty : elasticnet
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-095845-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160908-095845-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
deleted file mode 100644
index d434781c220fac77883c4cff413c638b54560262..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-095845-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-Fake-LOG.log
+++ /dev/null
@@ -1,32 +0,0 @@
-2016-09-08 09:58:45,489 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
-2016-09-08 09:58:45,489 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.00010759375 Gbytes /!\
-2016-09-08 09:58:50,503 DEBUG: Start: Creating datasets for multiprocessing
-2016-09-08 09:58:50,507 INFO: Start: Finding all available mono- & multiview algorithms
-2016-09-08 09:58:50,558 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:58:50,558 DEBUG: ### Main Programm for Classification MonoView
-2016-09-08 09:58:50,559 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
-2016-09-08 09:58:50,559 DEBUG: ### Classification - Database:Fake Feature:View0 train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
-2016-09-08 09:58:50,559 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:58:50,559 DEBUG: Start: Determine Train/Test split
-2016-09-08 09:58:50,559 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:58:50,559 DEBUG: Info: Shape X_train:(210, 12), Length of y_train:210
-2016-09-08 09:58:50,559 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:58:50,559 DEBUG: Info: Shape X_test:(90, 12), Length of y_test:90
-2016-09-08 09:58:50,560 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:58:50,560 DEBUG: Done: Determine Train/Test split
-2016-09-08 09:58:50,560 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:58:50,560 DEBUG: Start: RandomSearch best settings with 1 iterations
-2016-09-08 09:58:50,594 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:58:50,595 DEBUG: Start: Training
-2016-09-08 09:58:50,596 DEBUG: Info: Time for Training: 0.0384030342102[s]
-2016-09-08 09:58:50,596 DEBUG: Done: Training
-2016-09-08 09:58:50,596 DEBUG: Start: Predicting
-2016-09-08 09:58:50,599 DEBUG: Done: Predicting
-2016-09-08 09:58:50,599 DEBUG: Start: Getting Results
-2016-09-08 09:58:50,609 DEBUG: Done: RandomSearch best settings
-2016-09-08 09:58:50,609 DEBUG: Start: Training
-2016-09-08 09:58:50,613 DEBUG: Info: Time for Training: 0.0557579994202[s]
-2016-09-08 09:58:50,614 DEBUG: Done: Training
-2016-09-08 09:58:50,614 DEBUG: Start: Predicting
-2016-09-08 09:58:50,616 DEBUG: Done: Predicting
-2016-09-08 09:58:50,617 DEBUG: Start: Getting Results
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index c10750d44ceac649cbe0a6d590287c9f5c97238b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View0 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.576923076923
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.576923076923
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -2.5337258102e-17
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ba0382dbd9355b910467e7c3ea45cf08fcbdcc56..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.56862745098
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.56862745098
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.00503027272866
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.557692307692
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.58
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5025
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index fc54b504882519b574c965ecf4d8eedeef1cc3aa..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with KNN
-
-accuracy_score on train : 0.580952380952
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 24
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.6
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.661538461538
- - Score on test : 0.678571428571
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.661538461538
- - Score on test : 0.678571428571
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.419047619048
- - Score on test : 0.4
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.580952380952
- - Score on test : 0.6
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.130162282504
- - Score on test : 0.171735516296
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.601398601399
- - Score on test : 0.612903225806
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.735042735043
- - Score on test : 0.76
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.561069754618
- - Score on test : 0.58
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.419047619048
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index b24a64e65016e9e0263c359295f6bef2f1a42a50..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100004Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 24, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.52427184466
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.52427184466
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.111088444626
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.509433962264
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.54
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.445
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9d76693a39a94571bc41ea125ba5b364719e7777..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View1 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.655172413793
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.655172413793
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.444444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0674199862463
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.575757575758
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.76
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.53
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 867521e73e098d35580ec0f95af5982d09e98991..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.637168141593
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.637168141593
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0487950036474
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.571428571429
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.72
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5225
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 589edf906844f420e689c2580b484c971d25c7bb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with KNN
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 24
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.659176029963
- - Score on test : 0.542056074766
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.659176029963
- - Score on test : 0.542056074766
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.433333333333
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0939782359481
- - Score on test : -0.123737644978
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.586666666667
- - Score on test : 0.508771929825
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.752136752137
- - Score on test : 0.58
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.542735042735
- - Score on test : 0.44
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.433333333333
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1881529c6a507787f96a3c34d3eae90866b6286f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SGD
-
-accuracy_score on train : 0.57619047619
-accuracy_score on test : 0.644444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.644444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.661596958175
- - Score on test : 0.724137931034
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.661596958175
- - Score on test : 0.724137931034
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.42380952381
- - Score on test : 0.355555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.57619047619
- - Score on test : 0.644444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.117819078215
- - Score on test : 0.269679944985
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.595890410959
- - Score on test : 0.636363636364
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.74358974359
- - Score on test : 0.84
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.55459057072
- - Score on test : 0.62
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.42380952381
- - Score on test : 0.355555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index cbbd1a0d3ac70db81be73d8fe7ce021f46f832ab..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMLinear
-
-accuracy_score on train : 0.485714285714
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7704
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.609523809524
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.609523809524
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.485714285714
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.0643090141189
- - Score on test : 0.0662541348869
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.533333333333
- - Score on test : 0.581818181818
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.615384615385
- - Score on test : 0.64
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.468982630273
- - Score on test : 0.5325
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.514285714286
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 23cf02f75457800ec7e365c2a0e5f522c0a89edf..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7704
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0973655073258
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.617647058824
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.42
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5475
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d276635c447b2d4b0302a0207ec8ee2adf70e5cb..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100005Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View0 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.6
-
-Database configuration :
- - Database name : Fake
- - View name : View0 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7704
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.714285714286
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.714285714286
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.4
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.6
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.171377655346
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.592105263158
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.9
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5625
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.4
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index caa7c9681cce2811ed8c3da610ba1810d410339e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View2 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.43956043956
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.43956043956
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.124719695673
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.487804878049
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4375
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 836977b7ee31de2f3257684bc8347ee0eacbd0e2..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.433333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.43956043956
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.43956043956
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.433333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.124719695673
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.487804878049
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4375
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.566666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 4dbb4c25550cab8b428692b93e320234b1a075c9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.566666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 24, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.621359223301
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.621359223301
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.433333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.566666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.116137919381
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.603773584906
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.64
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5575
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.433333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d66ec8a4ffb7a89486e0947c4e3dbb3653ab71b9..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SGD
-
-accuracy_score on train : 0.566666666667
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.555555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.640316205534
- - Score on test : 0.666666666667
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.640316205534
- - Score on test : 0.666666666667
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.433333333333
- - Score on test : 0.444444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.566666666667
- - Score on test : 0.555555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.104925880155
- - Score on test : 0.0597614304667
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.595588235294
- - Score on test : 0.571428571429
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.692307692308
- - Score on test : 0.8
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.550454921423
- - Score on test : 0.525
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.433333333333
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 3b75a933e277742b98aa3bc5f78a4a226c873602..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMLinear
-
-accuracy_score on train : 0.490476190476
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7704
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.566801619433
- - Score on test : 0.576923076923
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.566801619433
- - Score on test : 0.576923076923
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.490476190476
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.0479423260647
- - Score on test : -3.54721613428e-17
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.538461538462
- - Score on test : 0.555555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.598290598291
- - Score on test : 0.6
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.476564653984
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.509523809524
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d128db52bc2a7a322700fc4fe91f03eff1d6eb7f..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.422222222222
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7704
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0714285714286
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.0714285714286
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.422222222222
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.119522860933
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.333333333333
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.04
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.47
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.577777777778
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d2a21462829ea28a11dcac00d479a150f15505c0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100006Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View1 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.5
-
-Database configuration :
- - Database name : Fake
- - View name : View1 View shape : (300, 15)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7704
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.64
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.64
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.5
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.1
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.8
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4625
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.5
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d9a3f5a3e6c9f9314127026a077a825115efd21d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with KNN
-
-accuracy_score on train : 0.547619047619
-accuracy_score on test : 0.466666666667
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 24
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.466666666667
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.649446494465
- - Score on test : 0.586206896552
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.649446494465
- - Score on test : 0.586206896552
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.452380952381
- - Score on test : 0.533333333333
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.547619047619
- - Score on test : 0.466666666667
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.0476928413215
- - Score on test : -0.134839972493
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.571428571429
- - Score on test : 0.515151515152
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.752136752137
- - Score on test : 0.68
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.521229666391
- - Score on test : 0.44
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.452380952381
- - Score on test : 0.533333333333
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 8e7a8c2dac3a63ea72ac1a1d2b1f00e6057b3021..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with RandomForest
-
-accuracy_score on train : 0.995238095238
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 24, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.995708154506
- - Score on test : 0.468085106383
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.995708154506
- - Score on test : 0.468085106383
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.995238095238
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.990406794809
- - Score on test : -0.109345881217
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.991452991453
- - Score on test : 0.44
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.995726495726
- - Score on test : 0.445
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0047619047619
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 9ee2ef1d9e95ac7aee25aa51fcbfbce5d49d2e57..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SGD
-
-accuracy_score on train : 0.619047619048
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.701492537313
- - Score on test : 0.592592592593
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.701492537313
- - Score on test : 0.592592592593
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.380952380952
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.619047619048
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.210547659218
- - Score on test : -0.0103806849817
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.622516556291
- - Score on test : 0.551724137931
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.803418803419
- - Score on test : 0.64
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.595257788806
- - Score on test : 0.495
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.380952380952
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d16950a5645f14abfffcd24404af4b15fb8acd2c..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100007Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMLinear
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.555555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7704
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.555555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.521739130435
- - Score on test : 0.565217391304
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.521739130435
- - Score on test : 0.565217391304
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.444444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.555555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.0568633060564
- - Score on test : 0.119522860933
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.530973451327
- - Score on test : 0.619047619048
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.512820512821
- - Score on test : 0.52
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.471464019851
- - Score on test : 0.56
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.444444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 1fadae0e8031c1efee27d914a774f3215f055b8e..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-Adaboost-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,57 +0,0 @@
-Classification on Fake database for View3 with Adaboost
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.577777777778
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Adaboost with num_esimators : 9, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.62
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.62
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.422222222222
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.577777777778
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.145
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.62
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.62
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5725
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.422222222222
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ade21df13c32fc925324446cc5c0f45faebc650b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-DecisionTree-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with DecisionTree
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.588888888889
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Decision Tree with max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.626262626263
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.626262626263
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.411111111111
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.588888888889
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.169618786115
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.632653061224
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.62
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.585
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.411111111111
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-KNN-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-KNN-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index ce39d2332c0af66ff80f32cb189f674d2518b6f0..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-KNN-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with KNN
-
-accuracy_score on train : 0.6
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - K nearest Neighbors with n_neighbors: 24
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.688888888889
- - Score on test : 0.637168141593
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.688888888889
- - Score on test : 0.637168141593
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.4
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.6
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.167225897665
- - Score on test : 0.0487950036474
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.607843137255
- - Score on test : 0.571428571429
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.794871794872
- - Score on test : 0.72
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.574855252275
- - Score on test : 0.5225
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.4
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index a6d23daee024b72d994eff7b8664df8932567014..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-RandomForest-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with RandomForest
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.455555555556
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - Random Forest with num_esimators : 24, max_depth : 25
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.533333333333
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.455555555556
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.117218854031
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.509090909091
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.56
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4425
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.544444444444
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 53f3a855a561b6e9c7a74c9197c38666faa2a1be..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMPoly-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMPoly
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.444444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7704
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.107142857143
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.107142857143
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.444444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : -0.0298807152334
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.06
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.4925
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.555555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index bf98d86d42889b815483d5075d682abc85a87071..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100008Results-SVMRBF-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View2 with SVMRBF
-
-accuracy_score on train : 1.0
-accuracy_score on test : 0.544444444444
-
-Database configuration :
- - Database name : Fake
- - View name : View2 View shape : (300, 18)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7704
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.687022900763
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.687022900763
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.544444444444
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 1.0
- - Score on test : 4.13755692208e-17
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.555555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.9
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 1.0
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.0
- - Score on test : 0.455555555556
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SGD-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SGD-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index 04d9aee02671ca751ba32e0fde801c90fc433b58..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SGD-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SGD
-
-accuracy_score on train : 0.642857142857
-accuracy_score on test : 0.533333333333
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SGDClassifier with loss : modified_huber, penalty : l2
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.642857142857
- - Score on test : 0.533333333333
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.701195219124
- - Score on test : 0.596153846154
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.701195219124
- - Score on test : 0.596153846154
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.357142857143
- - Score on test : 0.466666666667
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.642857142857
- - Score on test : 0.533333333333
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : 0.266179454365
- - Score on test : 0.0456435464588
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.65671641791
- - Score on test : 0.574074074074
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.752136752137
- - Score on test : 0.62
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.628756548111
- - Score on test : 0.5225
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.357142857143
- - Score on test : 0.466666666667
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
deleted file mode 100644
index d52376ecd580c0741a63b611a9a5411bd55bef66..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100009Results-SVMLinear-Non-Oui-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,54 +0,0 @@
-Classification on Fake database for View3 with SVMLinear
-
-accuracy_score on train : 0.47619047619
-accuracy_score on test : 0.511111111111
-
-Database configuration :
- - Database name : Fake
- - View name : View3 View shape : (300, 12)
- - Learning Rate : 0.7
- - Labels used : Non, Oui
- - Number of cross validation folds : 5
-
-Classifier configuration :
- - SVM Linear with C : 7704
- - Executed on 1 core(s)
- - Got configuration using randomized search with 1 iterations
-
-
- For Accuracy score using None as sample_weights (higher is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.511111111111
- For F1 score using None as sample_weights, None as labels, 1 as pos_label, micro as average (higher is better) :
- - Score on train : 0.541666666667
- - Score on test : 0.576923076923
- For F-beta score using None as sample_weights, None as labels, 1 as pos_label, micro as average, 1.0 as beta (higher is better) :
- - Score on train : 0.541666666667
- - Score on test : 0.576923076923
- For Hamming loss using None as classes (lower is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.488888888889
- For Jaccard similarity score using None as sample_weights (higher is better) :
- - Score on train : 0.47619047619
- - Score on test : 0.511111111111
- For Log loss using None as sample_weights, 1e-15 as eps (lower is better) :
- - Score on train : nan
- - Score on test : nan
- For Matthews correlation coefficient (higher is better) :
- - Score on train : -0.068670723144
- - Score on test : 0.0
- For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.528455284553
- - Score on test : 0.555555555556
- For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
- - Score on train : 0.555555555556
- - Score on test : 0.6
- For ROC AUC score using None as sample_weights, micro as average (higher is better) :
- - Score on train : 0.465949820789
- - Score on test : 0.5
- For Zero one loss using None as sample_weights (lower is better) :
- - Score on train : 0.52380952381
- - Score on test : 0.488888888889
-
-
- Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 31c5952590b71df2f7b9326a2fed03e501181cfa..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-BayesianInference-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 59.6470588235
- -On Test : 50.7317073171
- -On Validation : 47.191011236
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.395042964582, 0.135468886361, 0.187401197987, 0.282086951071
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - SGDClassifier with loss : log, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 4d7b96cd623963b3db009f942a91b9ba9c0ae0ea..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100010Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 55.2941176471
- -On Test : 56.0975609756
- -On Validation : 56.1797752809
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Majority Voting
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - SGDClassifier with loss : log, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:03 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 92e2e7eb2640f7c3de43250d3304533afdc9196b..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 60.4705882353
- -On Test : 47.8048780488
- -On Validation : 53.2584269663
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with SVM for linear
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - SGDClassifier with loss : log, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 9035751820a94da279bbd7183290a6cbe0201fb6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100011Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-SGD-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,32 +0,0 @@
- Result for Multiview classification with LateFusion
-
-Average accuracy :
- -On Train : 25.6470588235
- -On Test : 23.4146341463
- -On Validation : 27.4157303371
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : LateFusion with Weighted linear using a weight for each view : 1.0, 0.342921905986, 0.474381813597, 0.714066510131
- -With monoview classifiers :
- - SGDClassifier with loss : modified_huber, penalty : l1
- - SGDClassifier with loss : log, penalty : l2
- - SGDClassifier with loss : log, penalty : elasticnet
- - SGDClassifier with loss : modified_huber, penalty : l2
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3ebeedf2c579860400c701b722f7662e73fd3d20..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,31 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 100.0
- -On Test : 54.6341463415
- -On Validation : 49.2134831461
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.444263234099, 1.0, 0.292116326168, 0.822047817174 with monoview classifier :
- - Adaboost with num_esimators : 8, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
- max_features=None, max_leaf_nodes=None, min_samples_leaf=1,
- min_samples_split=2, min_weight_fraction_leaf=0.0,
- presort=False, random_state=None, splitter='best')
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:02 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 007add077fe4b19b82933921162236df028dcfe6..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100012Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 97.4117647059
- -On Test : 47.3170731707
- -On Validation : 50.7865168539
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.564923899429, 0.171414234739, 1.0, 0.282773686486 with monoview classifier :
- - Decision Tree with max_depth : 8
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index d10e58096a314c7404284f1d9762253358d9e56a..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-KNN-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 60.4705882353
- -On Test : 54.6341463415
- -On Validation : 51.9101123596
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 0.567673336435, 0.401953729602, 0.0761117950819 with monoview classifier :
- - K nearest Neighbors with n_neighbors: 40
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 3109c44cf165ee1ea89651472f5ebd130af9afd5..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 81.0588235294
- -On Test : 43.4146341463
- -On Validation : 48.9887640449
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.073909136797, 0.326197494021, 1.0, 0.0290483308675 with monoview classifier :
- - Random Forest with num_esimators : 1, max_depth : 8
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index e63a7f8163f3366b568c1f02b93096e9fba2ed76..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SGD-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 55.2941176471
- -On Test : 56.0975609756
- -On Validation : 56.1797752809
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 1.0, 0.728775264645, 0.482876097673, 0.365130635662 with monoview classifier :
- - SGDClassifier with loss : modified_huber, penalty : l1
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt b/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
deleted file mode 100644
index 6c39250e0e355ae42944f0ee6289bcb576f2e11d..0000000000000000000000000000000000000000
--- a/Code/MonoMutliViewClassifiers/Results/20160908-100013Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-Methyl-MiRNA_-RNASeq-Clinic-MiRNA_-RNASeq-Clinic-Methyl-learnRate0.7-Fake.txt
+++ /dev/null
@@ -1,28 +0,0 @@
- Result for Multiview classification with EarlyFusion
-
-Average accuracy :
- -On Train : 57.7647058824
- -On Test : 52.1951219512
- -On Validation : 49.2134831461
-
-Dataset info :
- -Database name : Fake
- -Labels : Methyl, MiRNA_, RNASeq, Clinic
- -Views : Methyl, MiRNA_, RNASeq, Clinic
- -5 folds
-
-Classification configuration :
- -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.073909136797, 0.326197494021, 1.0, 0.0290483308675 with monoview classifier :
- - SVM Linear with C : 3073
-
-Computation time on 1 cores :
- Database extraction time : 0:00:00
- Learn Prediction
- Fold 1 0:00:00 0:00:00
- Fold 2 0:00:00 0:00:00
- Fold 3 0:00:00 0:00:00
- Fold 4 0:00:00 0:00:00
- Fold 5 0:00:00 0:00:00
- Total 0:00:01 0:00:00
- So a total classification time of 0:00:00.
-
diff --git a/Code/MonoMutliViewClassifiers/utils/Transformations.py b/Code/MonoMutliViewClassifiers/utils/Transformations.py
new file mode 100644
index 0000000000000000000000000000000000000000..a28cccd4edf709b62e030381a781fc9854d77f81
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/utils/Transformations.py
@@ -0,0 +1,7 @@
+import numpy as np
+
+def signLabels(labels):
+ if set(labels) == (0,1):
+ return np.array([label if label != 0 else -1 for label in labels])
+ else:
+ return labels