diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index 1e9cc7f94db31ac14c37bde93b10e2faad4350ce..caa064be3ae474ff4150cab8c1437b1040e0be33 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -12,11 +12,13 @@ import numpy as np
import logging
import matplotlib
matplotlib.use('Agg')
+import math
+import time
# Import own modules
import Multiview
-from Multiview.ExecMultiview import ExecMultiview
-from Monoview.ExecClassifMonoView import ExecMonoview
+from Multiview.ExecMultiview import ExecMultiview, ExecMultiview_multicore
+from Monoview.ExecClassifMonoView import ExecMonoview, ExecMonoview_multicore
import Multiview.GetMultiviewDb as DB
import Monoview
from ResultAnalysis import resultAnalysis
@@ -160,6 +162,8 @@ os.nice(args.nice)
nbCores = args.CL_cores
if args.name not in ["MultiOmic", "ModifiedMultiOmic", "Caltech"]:
getDatabase = getattr(DB, "getClassicDB" + args.type[1:])
+else:
+ getDatabase = getattr(DB, "get" + args.name + "DB" + args.type[1:])
try:
gridSearch = args.CL_NoGS
@@ -183,9 +187,17 @@ logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', filename=lo
if args.log:
logging.getLogger().addHandler(logging.StreamHandler())
-getDatabase = getattr(DB, "get" + args.name + "DB" + args.type[1:])
+
DATASET, LABELS_DICTIONARY = getDatabase(args.views.split(":"), args.pathF, args.name, len(args.CL_classes), args.CL_classes)
datasetLength = DATASET.get("Metadata").attrs["datasetLength"]
+if nbCores>1:
+ logging.debug("Start:\t Creating "+str(nbCores)+" temporary datasets for multiprocessing")
+ logging.warning(" WARNING : /!\ This may use a lot of HDD storage space : "+
+ str(os.path.getsize(args.pathF+args.name+".hdf5")*nbCores/float(1024)/1000/1000)+" Gbytes /!\ ")
+ time.sleep(5)
+ datasetFiles = DB.copyHDF5(args.pathF, args.name, nbCores)
+ logging.debug("Start:\t Creating datasets for multiprocessing")
+
NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
views = [str(DATASET.get("View"+str(viewIndex)).attrs["name"]) for viewIndex in range(NB_VIEW)]
NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
@@ -257,31 +269,50 @@ AdaboostKWARGS = {"0": args.CL_Ada_n_est.split(":")[0], "1": args.CL_Ada_b_est.s
argumentDictionaries = {"Monoview": {}, "Multiview": []}
try:
if benchmark["Monoview"]:
+ argumentDictionaries["Monoview"] = []
for view in views:
- argumentDictionaries["Monoview"][str(view)] = []
for classifier in benchmark["Monoview"]:
-
- arguments = {classifier+"KWARGS": globals()[classifier+"KWARGS"], "feat":view, "fileFeat": args.fileFeat,
- "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": classifier}
-
- argumentDictionaries["Monoview"][str(view)].append(arguments)
+ arguments = {"args":{classifier+"KWARGS": globals()[classifier+"KWARGS"], "feat":view, "fileFeat": args.fileFeat,
+ "fileCL": args.fileCL, "fileCLD": args.fileCLD, "CL_type": classifier}, "viewIndex":views.index(view)}
+ argumentDictionaries["Monoview"].append(arguments)
except:
pass
bestClassifiers = []
bestClassifiersConfigs = []
resultsMonoview = []
-for viewIndex, viewArguments in enumerate(argumentDictionaries["Monoview"].values()):
- resultsMonoview.append( (Parallel(n_jobs=nbCores)(
- delayed(ExecMonoview)(DATASET.get("View"+str(viewIndex)), DATASET.get("labels").value, args.name,
- args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF, gridSearch=gridSearch,
- metric=metric, nIter=args.CL_GS_iter, **arguments)
- for arguments in viewArguments)))
-
- accuracies = [result[1] for result in resultsMonoview[viewIndex]]
- classifiersNames = [result[0] for result in resultsMonoview[viewIndex]]
- classifiersConfigs = [result[2] for result in resultsMonoview[viewIndex]]
- bestClassifiers.append(classifiersNames[np.argmax(np.array(accuracies))])
- bestClassifiersConfigs.append(classifiersConfigs[np.argmax(np.array(accuracies))])
+if nbCores>1:
+ nbExperiments = len(argumentDictionaries["Monoview"])
+ print nbExperiments
+ for stepIndex in range(int(math.ceil(float(nbExperiments)/nbCores))):
+ resultsMonoview+=(Parallel(n_jobs=nbCores)(
+ delayed(ExecMonoview_multicore)(args.name, args.CL_split, args.CL_nbFolds, coreIndex, args.type, args.pathF, gridSearch=gridSearch,
+ metric=metric, nIter=args.CL_GS_iter, **argumentDictionaries["Monoview"][coreIndex+stepIndex*nbCores])
+ for coreIndex in range(min(nbCores, nbExperiments - (stepIndex + 1) * nbCores))))
+ accuracies = [[result[1][1] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
+ print accuracies
+ for result in resultsMonoview:
+ print result[0]
+ print resultsMonoview[0][0]
+ classifiersNames = [[result[1][0] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
+ classifiersConfigs = [[result[1][2] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
+ for viewIndex, view in enumerate(views):
+ bestClassifiers.append(classifiersNames[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
+ bestClassifiersConfigs.append(classifiersConfigs[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
+
+else:
+ resultsMonoview.append([ExecMonoview(datasetFiles[viewIndex].get("View"+str(viewIndex)),
+ datasetFiles[viewIndex].get("labels").value, args.name,
+ args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
+ gridSearch=gridSearch, metric=metric, nIter=args.CL_GS_iter,
+ **arguments["args"])
+ for arguments in argumentDictionaries["Monoview"]])
+
+ 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][2] for result in resultsMonoview if result[0]==viewIndex] for viewIndex in range(NB_VIEW)]
+ for viewIndex, view in enumerate(views):
+ bestClassifiers.append(classifiersNames[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
+ bestClassifiersConfigs.append(classifiersConfigs[viewIndex][np.argmax(np.array(accuracies[viewIndex]))])
try:
if benchmark["Multiview"]:
try:
@@ -337,13 +368,24 @@ try:
pass
except:
pass
-
-
-resultsMultiview = Parallel(n_jobs=nbCores)(
- delayed(ExecMultiview)(DATASET, args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
- LABELS_DICTIONARY, gridSearch=gridSearch,
- metrics=metrics, **arguments)
- for arguments in argumentDictionaries["Multiview"])
+if nbCores>1:
+ resultsMultiview = []
+ nbExperiments = len(argumentDictionaries["Multiview"])
+ for stepIndex in range(int(math.ceil(float(nbExperiments)/nbCores))):
+ resultsMultiview += Parallel(n_jobs=nbCores)(
+ delayed(ExecMultiview_multicore)(coreIndex, args.name, args.CL_split, args.CL_nbFolds, args.type, args.pathF,
+ LABELS_DICTIONARY, gridSearch=gridSearch,
+ metrics=metrics, **argumentDictionaries["Multiview"][stepIndex*nbCores+coreIndex])
+ for coreIndex in range(min(nbCores, nbExperiments - (stepIndex + 1) * nbCores)))
+else:
+ resultsMultiview = [ExecMultiview(DATASET, args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
+ LABELS_DICTIONARY, gridSearch=gridSearch,
+ metrics=metrics, **arguments) for arguments in argumentDictionaries["Multiview"]]
+
+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")
results = (resultsMonoview, resultsMultiview)
resultAnalysis(benchmark, results)
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index bde1b63408b8446e9992e52e1bc0c2225220d5e1..e878bb57e1d1c7644f08f4c29fc54108c9242f11 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -29,7 +29,18 @@ __status__ = "Prototype" # Production, Development, Prototype
__date__ = 2016-03-25
-### Argument Parser
+def ExecMonoview_multicore(name, learningRate, nbFolds, datasetFileIndex, databaseType, path, gridSearch=True,
+ metrics=[["accuracy_score", None]], nIter=30, **args):
+ DATASET = h5py.File(path+name+str(datasetFileIndex)+".hdf5", "r")
+ kwargs = args["args"]
+
+ views = [DATASET.get("View"+str(viewIndex)).attrs["name"] for viewIndex in range(DATASET.get("Metadata").attrs["nbView"])]
+ neededViewIndex = views.index(kwargs["feat"])
+ X = DATASET.get("View"+str(neededViewIndex))
+ Y = DATASET.get("labels").value
+ returnedViewIndex = args["viewIndex"]
+ return returnedViewIndex, ExecMonoview(X, Y, name, learningRate, nbFolds, 1, databaseType, path, gridSearch=gridSearch,
+ metrics=metrics, nIter=nIter, **kwargs)
def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path, gridSearch=True,
@@ -99,7 +110,6 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
labelsString = "-".join(classLabelsNames)
timestr = time.strftime("%Y%m%d-%H%M%S")
CL_type_string = CL_type
- print CL_type_string
outputFileName = "Results/" + timestr + "Results-" + CL_type_string + "-" + labelsString + \
'-learnRate' + str(learningRate) + '-' + name
diff --git a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
index 1d7b5804ec8bf88d2a67c87a2aaf4ec1a3c3e726..b932d33b33cd4d362fadd8590da5c8da2a783de0 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
@@ -13,6 +13,7 @@ import datetime
import os
import logging
import time
+import h5py
# Author-Info
@@ -21,6 +22,13 @@ __status__ = "Prototype" # Production, Development, P
+def ExecMultiview_multicore(coreIndex, name, learningRate, nbFolds, databaseType, path, LABELS_DICTIONARY ,
+ gridSearch=False, nbCores=1, metrics=None, nIter=30, **arguments):
+ DATASET = h5py.File(path+name+str(coreIndex)+".hdf5", "r")
+ return ExecMultiview(DATASET, name, learningRate, nbFolds, 1, databaseType, path, LABELS_DICTIONARY,
+ gridSearch=False, metrics=None, nIter=30, **arguments)
+
+
def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, path, LABELS_DICTIONARY,
gridSearch=False, metrics=None, nIter=30, **kwargs):
diff --git a/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
index b43371299cfeadb746fd91f2f9452731bebb4bab..b944ffa99b61febd59bce637c34cd459f7d6602f 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
@@ -355,6 +355,19 @@ def getMultiOmicDBhdf5(features, path, name, NB_CLASS, LABELS_NAMES):
return datasetFile, labelDictionary
+def copyHDF5(pathF, name, nbCores):
+ datasetFile = h5py.File(pathF+name+".hdf5", "r")
+ for coreIndex in range(nbCores):
+ newDataSet = h5py.File(pathF+name+str(coreIndex)+".hdf5", "w")
+ for dataset in datasetFile:
+ datasetFile.copy("/"+dataset, newDataSet["/"])
+ newDataSet.close()
+
+
+def deleteHDF5(pathF, name, nbCores):
+ for coreIndex in range(nbCores):
+ os.remove(pathF+name+str(coreIndex)+".hdf5")
+
# def getOneViewFromDB(viewName, pathToDB, DBName):
# view = np.genfromtxt(pathToDB + DBName +"-" + viewName, delimiter=';')
# return view
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index 07083188acb2cd4819dd62181f63dedde1da64c7..03fb0f6636a7301190852a7318c2c3916ce3019c 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -14,14 +14,10 @@ __status__ = "Prototype" # Production, Development, P
def resultAnalysis(benchmark, results):
mono, multi = results
- flattenedMono = []
- for view in mono:
- for res in view:
- flattenedMono.append(res)
- names = [res[0]+res[3] for res in flattenedMono]
+ names = [res[1][0]+res[1][3] for res in mono]
names+=[type_ if type_ != "Fusion" else type_+a["fusionType"]+a["fusionMethod"] for type_, a, b, c, d in multi]
- nbResults = len(flattenedMono)+len(multi)
- accuracies = [float(res[1]) for res in flattenedMono]
+ nbResults = len(mono)+len(multi)
+ accuracies = [float(res[1][1]) for res in mono]
accuracies += [float(accuracy) for a, b, c, d, accuracy in multi]
f = pylab.figure(figsize=(40, 30))
fig = plt.gcf()
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-143017-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-143017-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150157-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-150157-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..09e6e234fc06f237f24cef7b7a7f4cfe819e96e8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150157-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,4 @@
+2016-09-02 15:01:57,259 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:01:57,259 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:02:00,228 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:02:00,231 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150235-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-150235-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..bc0bf0044810f378f4be43929ec4ab8258025582
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150235-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,4 @@
+2016-09-02 15:02:35,409 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:02:35,409 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:02:37,811 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:02:37,814 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150254-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-150254-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..573066d7d76225191278bb74fc96e3abf7d4480e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150254-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,4 @@
+2016-09-02 15:02:54,797 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:02:54,797 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:02:56,866 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:02:57,786 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150432-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-150432-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..40039b756e27c13d09bb858b368b59990250c921
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150432-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,4 @@
+2016-09-02 15:04:32,394 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:04:32,394 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:04:34,167 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:04:34,169 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150540-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-150540-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..d736afbf99119c29835a67418cd67a71ac0b3207
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150540-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,4 @@
+2016-09-02 15:05:40,604 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:05:40,604 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:05:42,136 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:05:42,138 INFO: Start: Finding all available mono- & multiview algorithms
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150721-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-150721-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..0a94b99234c66d55e2938ed6ef78eaca3aceb32b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150721-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,617 @@
+2016-09-02 15:07:21,981 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:07:21,981 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:07:23,723 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:07:23,724 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:07:24,608 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:24,608 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:24,608 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:07:24,608 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:07:24,608 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:24,609 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:24,610 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:07:24,610 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:07:24,610 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:07:24,610 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:07:24,610 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:24,610 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:24,610 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:24,610 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:26,524 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:26,524 DEBUG: Start: Training
+2016-09-02 15:07:26,558 DEBUG: Info: Time for Training: 1.95062017441[s]
+2016-09-02 15:07:26,558 DEBUG: Done: Training
+2016-09-02 15:07:26,558 DEBUG: Start: Predicting
+2016-09-02 15:07:26,624 DEBUG: Done: Predicting
+2016-09-02 15:07:26,624 DEBUG: Start: Getting Results
+2016-09-02 15:07:26,625 DEBUG: Done: Getting Results
+2016-09-02 15:07:26,625 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 0.933884297521
+accuracy_score on test : 0.819047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.933884297521
+ - Score on test : 0.819047619048
+
+
+ Classification took 0:00:01
+2016-09-02 15:07:26,625 INFO: Done: Result Analysis
+2016-09-02 15:07:26,787 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:26,788 DEBUG: Start: Training
+2016-09-02 15:07:26,836 DEBUG: Info: Time for Training: 2.22867298126[s]
+2016-09-02 15:07:26,836 DEBUG: Done: Training
+2016-09-02 15:07:26,836 DEBUG: Start: Predicting
+2016-09-02 15:07:26,839 DEBUG: Done: Predicting
+2016-09-02 15:07:26,839 DEBUG: Start: Getting Results
+2016-09-02 15:07:26,841 DEBUG: Done: Getting Results
+2016-09-02 15:07:26,841 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.819047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.819047619048
+
+
+ Classification took 0:00:02
+2016-09-02 15:07:26,841 INFO: Done: Result Analysis
+2016-09-02 15:07:26,947 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:26,948 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:07:26,948 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:26,948 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:26,949 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:07:26,949 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:26,950 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:07:26,950 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:07:26,950 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:26,950 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:26,950 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:07:26,950 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:07:26,950 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:26,951 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:27,999 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:27,999 DEBUG: Start: Training
+2016-09-02 15:07:28,004 DEBUG: Info: Time for Training: 1.05736112595[s]
+2016-09-02 15:07:28,004 DEBUG: Done: Training
+2016-09-02 15:07:28,004 DEBUG: Start: Predicting
+2016-09-02 15:07:28,150 DEBUG: Done: Predicting
+2016-09-02 15:07:28,150 DEBUG: Start: Getting Results
+2016-09-02 15:07:28,151 DEBUG: Done: Getting Results
+2016-09-02 15:07:28,151 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.793388429752
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 17
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.793388429752
+ - Score on test : 0.8
+
+
+ Classification took 0:00:01
+2016-09-02 15:07:28,151 INFO: Done: Result Analysis
+2016-09-02 15:07:29,306 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:29,306 DEBUG: Start: Training
+2016-09-02 15:07:29,357 DEBUG: Info: Time for Training: 2.4102139473[s]
+2016-09-02 15:07:29,358 DEBUG: Done: Training
+2016-09-02 15:07:29,358 DEBUG: Start: Predicting
+2016-09-02 15:07:29,363 DEBUG: Done: Predicting
+2016-09-02 15:07:29,364 DEBUG: Start: Getting Results
+2016-09-02 15:07:29,365 DEBUG: Done: Getting Results
+2016-09-02 15:07:29,365 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.87619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 24
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.87619047619
+
+
+ Classification took 0:00:02
+2016-09-02 15:07:29,365 INFO: Done: Result Analysis
+2016-09-02 15:07:29,505 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:29,505 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:29,506 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:07:29,506 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:07:29,506 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:29,506 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:29,508 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:07:29,508 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:07:29,508 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:07:29,508 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:07:29,508 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:29,508 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:29,508 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:29,508 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:30,646 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:30,646 DEBUG: Start: Training
+2016-09-02 15:07:30,661 DEBUG: Info: Time for Training: 1.15715694427[s]
+2016-09-02 15:07:30,661 DEBUG: Done: Training
+2016-09-02 15:07:30,661 DEBUG: Start: Predicting
+2016-09-02 15:07:30,665 DEBUG: Done: Predicting
+2016-09-02 15:07:30,665 DEBUG: Start: Getting Results
+2016-09-02 15:07:30,666 DEBUG: Done: Getting Results
+2016-09-02 15:07:30,666 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.797520661157
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.797520661157
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:01
+2016-09-02 15:07:30,667 INFO: Done: Result Analysis
+2016-09-02 15:07:31,446 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:31,446 DEBUG: Start: Training
+2016-09-02 15:07:31,621 DEBUG: Info: Time for Training: 2.116740942[s]
+2016-09-02 15:07:31,621 DEBUG: Done: Training
+2016-09-02 15:07:31,621 DEBUG: Start: Predicting
+2016-09-02 15:07:31,661 DEBUG: Done: Predicting
+2016-09-02 15:07:31,661 DEBUG: Start: Getting Results
+2016-09-02 15:07:31,662 DEBUG: Done: Getting Results
+2016-09-02 15:07:31,662 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.570247933884
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4921
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.570247933884
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:02
+2016-09-02 15:07:31,662 INFO: Done: Result Analysis
+2016-09-02 15:07:31,754 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:31,754 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:31,754 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:07:31,754 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:07:31,755 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:31,755 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:31,758 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:07:31,758 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:07:31,758 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:07:31,758 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:07:31,758 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:31,758 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:31,758 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:31,758 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:32,243 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:32,244 DEBUG: Start: Training
+2016-09-02 15:07:32,253 DEBUG: Info: Time for Training: 0.500488996506[s]
+2016-09-02 15:07:32,253 DEBUG: Done: Training
+2016-09-02 15:07:32,253 DEBUG: Start: Predicting
+2016-09-02 15:07:32,259 DEBUG: Done: Predicting
+2016-09-02 15:07:32,259 DEBUG: Start: Getting Results
+2016-09-02 15:07:32,261 DEBUG: Done: Getting Results
+2016-09-02 15:07:32,261 INFO: Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.289256198347
+accuracy_score on test : 0.219047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4921
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.289256198347
+ - Score on test : 0.219047619048
+
+
+ Classification took 0:00:00
+2016-09-02 15:07:32,261 INFO: Done: Result Analysis
+2016-09-02 15:07:34,759 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:34,759 DEBUG: Start: Training
+2016-09-02 15:07:35,071 DEBUG: Info: Time for Training: 3.31841206551[s]
+2016-09-02 15:07:35,071 DEBUG: Done: Training
+2016-09-02 15:07:35,071 DEBUG: Start: Predicting
+2016-09-02 15:07:35,162 DEBUG: Done: Predicting
+2016-09-02 15:07:35,163 DEBUG: Start: Getting Results
+2016-09-02 15:07:35,164 DEBUG: Done: Getting Results
+2016-09-02 15:07:35,164 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4921
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:03
+2016-09-02 15:07:35,164 INFO: Done: Result Analysis
+2016-09-02 15:07:35,315 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:35,315 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:35,315 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:07:35,315 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:07:35,316 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:35,316 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:35,317 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:07:35,317 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:07:35,317 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:07:35,317 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:07:35,317 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:35,317 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:35,317 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:35,317 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:35,566 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:35,566 DEBUG: Start: Training
+2016-09-02 15:07:35,568 DEBUG: Info: Time for Training: 0.253885984421[s]
+2016-09-02 15:07:35,568 DEBUG: Done: Training
+2016-09-02 15:07:35,568 DEBUG: Start: Predicting
+2016-09-02 15:07:35,571 DEBUG: Done: Predicting
+2016-09-02 15:07:35,571 DEBUG: Start: Getting Results
+2016-09-02 15:07:35,572 DEBUG: Done: Getting Results
+2016-09-02 15:07:35,572 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 0.979338842975
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.979338842975
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:07:35,572 INFO: Done: Result Analysis
+2016-09-02 15:07:35,691 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:35,691 DEBUG: Start: Training
+2016-09-02 15:07:35,695 DEBUG: Info: Time for Training: 0.380985021591[s]
+2016-09-02 15:07:35,695 DEBUG: Done: Training
+2016-09-02 15:07:35,695 DEBUG: Start: Predicting
+2016-09-02 15:07:35,698 DEBUG: Done: Predicting
+2016-09-02 15:07:35,698 DEBUG: Start: Getting Results
+2016-09-02 15:07:35,700 DEBUG: Done: Getting Results
+2016-09-02 15:07:35,700 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:07:35,700 INFO: Done: Result Analysis
+2016-09-02 15:07:35,761 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:35,761 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:35,761 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:07:35,761 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:07:35,761 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:35,761 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:35,762 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:07:35,762 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:07:35,762 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:07:35,762 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:07:35,762 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:35,762 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:35,762 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:35,762 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:36,048 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:36,048 DEBUG: Start: Training
+2016-09-02 15:07:36,049 DEBUG: Info: Time for Training: 0.289021968842[s]
+2016-09-02 15:07:36,049 DEBUG: Done: Training
+2016-09-02 15:07:36,049 DEBUG: Start: Predicting
+2016-09-02 15:07:36,068 DEBUG: Done: Predicting
+2016-09-02 15:07:36,068 DEBUG: Start: Getting Results
+2016-09-02 15:07:36,069 DEBUG: Done: Getting Results
+2016-09-02 15:07:36,069 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.761904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:07:36,069 INFO: Done: Result Analysis
+2016-09-02 15:07:37,682 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:37,682 DEBUG: Start: Training
+2016-09-02 15:07:37,720 DEBUG: Info: Time for Training: 1.95997905731[s]
+2016-09-02 15:07:37,720 DEBUG: Done: Training
+2016-09-02 15:07:37,720 DEBUG: Start: Predicting
+2016-09-02 15:07:37,726 DEBUG: Done: Predicting
+2016-09-02 15:07:37,726 DEBUG: Start: Getting Results
+2016-09-02 15:07:37,727 DEBUG: Done: Getting Results
+2016-09-02 15:07:37,727 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 24
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:01
+2016-09-02 15:07:37,727 INFO: Done: Result Analysis
+2016-09-02 15:07:37,814 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:37,814 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:37,814 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:07:37,814 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:07:37,814 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:37,814 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:37,815 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:07:37,815 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:07:37,815 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:07:37,815 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:07:37,815 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:37,815 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:37,815 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:37,816 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:38,187 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:38,187 DEBUG: Start: Training
+2016-09-02 15:07:38,188 DEBUG: Info: Time for Training: 0.375222921371[s]
+2016-09-02 15:07:38,189 DEBUG: Done: Training
+2016-09-02 15:07:38,189 DEBUG: Start: Predicting
+2016-09-02 15:07:38,201 DEBUG: Done: Predicting
+2016-09-02 15:07:38,201 DEBUG: Start: Getting Results
+2016-09-02 15:07:38,202 DEBUG: Done: Getting Results
+2016-09-02 15:07:38,202 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:07:38,202 INFO: Done: Result Analysis
+2016-09-02 15:07:38,371 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:38,371 DEBUG: Start: Training
+2016-09-02 15:07:38,419 DEBUG: Info: Time for Training: 0.605732917786[s]
+2016-09-02 15:07:38,419 DEBUG: Done: Training
+2016-09-02 15:07:38,419 DEBUG: Start: Predicting
+2016-09-02 15:07:38,429 DEBUG: Done: Predicting
+2016-09-02 15:07:38,429 DEBUG: Start: Getting Results
+2016-09-02 15:07:38,430 DEBUG: Done: Getting Results
+2016-09-02 15:07:38,430 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.545454545455
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4921
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.545454545455
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:07:38,430 INFO: Done: Result Analysis
+2016-09-02 15:07:38,565 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:38,565 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:07:38,565 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:07:38,565 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:07:38,565 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:38,565 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:07:38,566 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:07:38,566 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:07:38,566 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:07:38,567 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:38,566 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:07:38,567 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:38,567 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:07:38,567 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:07:39,377 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:07:39,377 DEBUG: Start: Training
+2016-09-02 15:07:39,454 DEBUG: Info: Time for Training: 0.890295982361[s]
+2016-09-02 15:07:39,455 DEBUG: Done: Training
+2016-09-02 15:07:39,455 DEBUG: Start: Predicting
+2016-09-02 15:07:39,472 DEBUG: Done: Predicting
+2016-09-02 15:07:39,472 DEBUG: Start: Getting Results
+2016-09-02 15:07:39,473 DEBUG: Done: Getting Results
+2016-09-02 15:07:39,473 INFO: Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4921
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:07:39,473 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150726Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150726Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1dd47a489f3de0e3150e853bd04aac2d10101ca4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150726Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.819047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.819047619048
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150726Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150726Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dc27d7cd939fda3137d2bae6443d9f758357de7a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150726Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 0.933884297521
+accuracy_score on test : 0.819047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.933884297521
+ - Score on test : 0.819047619048
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150728Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150728Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8f10c45d32ab31234156a69de93b3bc9b21356c8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150728Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.793388429752
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 17
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.793388429752
+ - Score on test : 0.8
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150729Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150729Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..01136525302ca960dc4347f653ddd1e65981dc85
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150729Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.87619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 24
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.87619047619
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150730Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150730Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3b4645bf3f31ef85e87230ec690a92b028155651
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150730Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.797520661157
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.797520661157
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150731Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150731Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c15f780226c8ba175276654dd8dd78398b23c5de
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150731Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.570247933884
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4921
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.570247933884
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150732Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150732Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..285ca676a7138202dd9956ebef5ccff2833639c9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150732Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.289256198347
+accuracy_score on test : 0.219047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4921
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.289256198347
+ - Score on test : 0.219047619048
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150735Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150735Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3874a8ce4b5d88ff142da1f29cc67f1d1b05a94a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150735Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150735Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150735Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d6fc7b7ac4f7eee34b9e9b6db7d2f89687a27756
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150735Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 0.979338842975
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.979338842975
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150735Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150735Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..71b06e626eca02e47eabb9750fb749021e7297ba
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150735Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4921
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150736Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150736Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7698661086d4ad4c49d31211b70c59b21f2b10a3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150736Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.761904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150737Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150737Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..10a5f4a6d199cbba0130c98cd69be3cda6188647
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150737Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, max_depth : 24
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150738Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150738Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..49e02d9adc4f13c95aa152e6ea7b875de254635c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150738Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150738Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150738Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e4ad8a924eafda35f009d8968be6a049030acc4d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150738Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.545454545455
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4921
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.545454545455
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150739Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150739Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a53f7cf18f14da68e1963021b9ba8e9622d9db0a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150739Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4921
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150953-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-150953-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..aeff40cac9ad3661588ac8f12d898674b158cf26
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150953-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,617 @@
+2016-09-02 15:09:53,630 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:09:53,630 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:09:55,512 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:09:55,514 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:09:55,885 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:09:55,885 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:09:55,885 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:09:55,885 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:09:55,885 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:09:55,885 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:09:55,888 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:09:55,888 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:09:55,888 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:09:55,888 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:09:55,888 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:09:55,888 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:09:55,888 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:09:55,888 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:09:58,036 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:09:58,037 DEBUG: Start: Training
+2016-09-02 15:09:58,097 DEBUG: Info: Time for Training: 2.21334981918[s]
+2016-09-02 15:09:58,097 DEBUG: Done: Training
+2016-09-02 15:09:58,097 DEBUG: Start: Predicting
+2016-09-02 15:09:58,099 DEBUG: Done: Predicting
+2016-09-02 15:09:58,099 DEBUG: Start: Getting Results
+2016-09-02 15:09:58,101 DEBUG: Done: Getting Results
+2016-09-02 15:09:58,101 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:02
+2016-09-02 15:09:58,101 INFO: Done: Result Analysis
+2016-09-02 15:09:58,151 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:09:58,151 DEBUG: Start: Training
+2016-09-02 15:09:58,212 DEBUG: Info: Time for Training: 2.32872891426[s]
+2016-09-02 15:09:58,212 DEBUG: Done: Training
+2016-09-02 15:09:58,212 DEBUG: Start: Predicting
+2016-09-02 15:09:58,215 DEBUG: Done: Predicting
+2016-09-02 15:09:58,215 DEBUG: Start: Getting Results
+2016-09-02 15:09:58,217 DEBUG: Done: Getting Results
+2016-09-02 15:09:58,217 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:02
+2016-09-02 15:09:58,218 INFO: Done: Result Analysis
+2016-09-02 15:09:58,337 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:09:58,337 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:09:58,338 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:09:58,338 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:09:58,338 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:09:58,338 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:09:58,340 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:09:58,340 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:09:58,340 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:09:58,340 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:09:58,340 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:09:58,340 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:09:58,340 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:09:58,340 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:09:59,326 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:09:59,327 DEBUG: Start: Training
+2016-09-02 15:09:59,331 DEBUG: Info: Time for Training: 0.995538949966[s]
+2016-09-02 15:09:59,331 DEBUG: Done: Training
+2016-09-02 15:09:59,331 DEBUG: Start: Predicting
+2016-09-02 15:09:59,471 DEBUG: Done: Predicting
+2016-09-02 15:09:59,471 DEBUG: Start: Getting Results
+2016-09-02 15:09:59,472 DEBUG: Done: Getting Results
+2016-09-02 15:09:59,472 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.805785123967
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.805785123967
+ - Score on test : 0.742857142857
+
+
+ Classification took 0:00:00
+2016-09-02 15:09:59,472 INFO: Done: Result Analysis
+2016-09-02 15:09:59,884 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:09:59,884 DEBUG: Start: Training
+2016-09-02 15:09:59,959 DEBUG: Info: Time for Training: 1.62274599075[s]
+2016-09-02 15:09:59,959 DEBUG: Done: Training
+2016-09-02 15:09:59,959 DEBUG: Start: Predicting
+2016-09-02 15:09:59,966 DEBUG: Done: Predicting
+2016-09-02 15:09:59,966 DEBUG: Start: Getting Results
+2016-09-02 15:09:59,967 DEBUG: Done: Getting Results
+2016-09-02 15:09:59,967 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.8
+
+
+ Classification took 0:00:01
+2016-09-02 15:09:59,967 INFO: Done: Result Analysis
+2016-09-02 15:10:00,087 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:00,088 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:10:00,088 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:00,088 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:00,088 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:10:00,088 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:00,090 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:10:00,090 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:10:00,090 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:10:00,090 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:10:00,090 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:00,090 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:00,090 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:00,090 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:01,070 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:01,070 DEBUG: Start: Training
+2016-09-02 15:10:01,075 DEBUG: Info: Time for Training: 0.989665985107[s]
+2016-09-02 15:10:01,075 DEBUG: Done: Training
+2016-09-02 15:10:01,076 DEBUG: Start: Predicting
+2016-09-02 15:10:01,080 DEBUG: Done: Predicting
+2016-09-02 15:10:01,080 DEBUG: Start: Getting Results
+2016-09-02 15:10:01,082 DEBUG: Done: Getting Results
+2016-09-02 15:10:01,082 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.855371900826
+accuracy_score on test : 0.87619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.855371900826
+ - Score on test : 0.87619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:10:01,082 INFO: Done: Result Analysis
+2016-09-02 15:10:01,654 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:01,654 DEBUG: Start: Training
+2016-09-02 15:10:01,811 DEBUG: Info: Time for Training: 1.72560691833[s]
+2016-09-02 15:10:01,811 DEBUG: Done: Training
+2016-09-02 15:10:01,811 DEBUG: Start: Predicting
+2016-09-02 15:10:01,851 DEBUG: Done: Predicting
+2016-09-02 15:10:01,851 DEBUG: Start: Getting Results
+2016-09-02 15:10:01,852 DEBUG: Done: Getting Results
+2016-09-02 15:10:01,852 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.619834710744
+accuracy_score on test : 0.514285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 442
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.619834710744
+ - Score on test : 0.514285714286
+
+
+ Classification took 0:00:01
+2016-09-02 15:10:01,852 INFO: Done: Result Analysis
+2016-09-02 15:10:01,935 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:01,935 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:01,935 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:10:01,935 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:10:01,935 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:01,935 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:01,938 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:10:01,938 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:10:01,938 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:10:01,938 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:10:01,938 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:01,938 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:01,938 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:01,938 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:02,636 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:02,637 DEBUG: Start: Training
+2016-09-02 15:10:02,793 DEBUG: Info: Time for Training: 0.85941696167[s]
+2016-09-02 15:10:02,794 DEBUG: Done: Training
+2016-09-02 15:10:02,794 DEBUG: Start: Predicting
+2016-09-02 15:10:02,832 DEBUG: Done: Predicting
+2016-09-02 15:10:02,832 DEBUG: Start: Getting Results
+2016-09-02 15:10:02,833 DEBUG: Done: Getting Results
+2016-09-02 15:10:02,833 INFO: Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7693
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
+2016-09-02 15:10:02,834 INFO: Done: Result Analysis
+2016-09-02 15:10:04,770 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:04,770 DEBUG: Start: Training
+2016-09-02 15:10:05,082 DEBUG: Info: Time for Training: 3.14772391319[s]
+2016-09-02 15:10:05,082 DEBUG: Done: Training
+2016-09-02 15:10:05,082 DEBUG: Start: Predicting
+2016-09-02 15:10:05,173 DEBUG: Done: Predicting
+2016-09-02 15:10:05,173 DEBUG: Start: Getting Results
+2016-09-02 15:10:05,174 DEBUG: Done: Getting Results
+2016-09-02 15:10:05,174 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 442
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:03
+2016-09-02 15:10:05,175 INFO: Done: Result Analysis
+2016-09-02 15:10:05,290 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:05,290 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:05,291 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:10:05,291 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:10:05,291 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:05,291 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:05,292 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:10:05,292 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:10:05,292 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:10:05,292 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:10:05,293 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:05,293 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:05,293 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:05,293 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:05,545 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:05,545 DEBUG: Start: Training
+2016-09-02 15:10:05,547 DEBUG: Info: Time for Training: 0.257043838501[s]
+2016-09-02 15:10:05,547 DEBUG: Done: Training
+2016-09-02 15:10:05,547 DEBUG: Start: Predicting
+2016-09-02 15:10:05,549 DEBUG: Done: Predicting
+2016-09-02 15:10:05,550 DEBUG: Start: Getting Results
+2016-09-02 15:10:05,550 DEBUG: Done: Getting Results
+2016-09-02 15:10:05,551 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 0.95867768595
+accuracy_score on test : 0.695238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.95867768595
+ - Score on test : 0.695238095238
+
+
+ Classification took 0:00:00
+2016-09-02 15:10:05,551 INFO: Done: Result Analysis
+2016-09-02 15:10:05,661 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:05,661 DEBUG: Start: Training
+2016-09-02 15:10:05,666 DEBUG: Info: Time for Training: 0.375769853592[s]
+2016-09-02 15:10:05,666 DEBUG: Done: Training
+2016-09-02 15:10:05,666 DEBUG: Start: Predicting
+2016-09-02 15:10:05,668 DEBUG: Done: Predicting
+2016-09-02 15:10:05,669 DEBUG: Start: Getting Results
+2016-09-02 15:10:05,670 DEBUG: Done: Getting Results
+2016-09-02 15:10:05,670 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.714285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.714285714286
+
+
+ Classification took 0:00:00
+2016-09-02 15:10:05,670 INFO: Done: Result Analysis
+2016-09-02 15:10:05,738 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:05,738 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:05,738 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:10:05,738 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:10:05,738 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:05,738 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:05,739 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:10:05,739 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:10:05,739 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:10:05,739 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:10:05,739 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:05,739 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:05,739 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:05,739 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:06,037 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:06,037 DEBUG: Start: Training
+2016-09-02 15:10:06,038 DEBUG: Info: Time for Training: 0.300408840179[s]
+2016-09-02 15:10:06,038 DEBUG: Done: Training
+2016-09-02 15:10:06,038 DEBUG: Start: Predicting
+2016-09-02 15:10:06,059 DEBUG: Done: Predicting
+2016-09-02 15:10:06,059 DEBUG: Start: Getting Results
+2016-09-02 15:10:06,060 DEBUG: Done: Getting Results
+2016-09-02 15:10:06,060 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.760330578512
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:10:06,061 INFO: Done: Result Analysis
+2016-09-02 15:10:06,968 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:06,968 DEBUG: Start: Training
+2016-09-02 15:10:06,996 DEBUG: Info: Time for Training: 1.2587788105[s]
+2016-09-02 15:10:06,996 DEBUG: Done: Training
+2016-09-02 15:10:06,996 DEBUG: Start: Predicting
+2016-09-02 15:10:07,001 DEBUG: Done: Predicting
+2016-09-02 15:10:07,001 DEBUG: Start: Getting Results
+2016-09-02 15:10:07,002 DEBUG: Done: Getting Results
+2016-09-02 15:10:07,002 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 11, max_depth : 21
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:01
+2016-09-02 15:10:07,002 INFO: Done: Result Analysis
+2016-09-02 15:10:07,090 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:07,090 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:07,090 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:10:07,090 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:10:07,090 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:07,090 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:07,091 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:10:07,091 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:10:07,092 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:10:07,092 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:10:07,092 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:07,092 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:07,092 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:07,092 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:07,462 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:07,463 DEBUG: Start: Training
+2016-09-02 15:10:07,464 DEBUG: Info: Time for Training: 0.375479221344[s]
+2016-09-02 15:10:07,465 DEBUG: Done: Training
+2016-09-02 15:10:07,465 DEBUG: Start: Predicting
+2016-09-02 15:10:07,472 DEBUG: Done: Predicting
+2016-09-02 15:10:07,472 DEBUG: Start: Getting Results
+2016-09-02 15:10:07,473 DEBUG: Done: Getting Results
+2016-09-02 15:10:07,474 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.760330578512
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:10:07,474 INFO: Done: Result Analysis
+2016-09-02 15:10:07,654 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:07,654 DEBUG: Start: Training
+2016-09-02 15:10:07,702 DEBUG: Info: Time for Training: 0.613485097885[s]
+2016-09-02 15:10:07,703 DEBUG: Done: Training
+2016-09-02 15:10:07,703 DEBUG: Start: Predicting
+2016-09-02 15:10:07,712 DEBUG: Done: Predicting
+2016-09-02 15:10:07,713 DEBUG: Start: Getting Results
+2016-09-02 15:10:07,714 DEBUG: Done: Getting Results
+2016-09-02 15:10:07,714 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.586776859504
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 442
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.586776859504
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:10:07,714 INFO: Done: Result Analysis
+2016-09-02 15:10:07,835 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:07,835 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:10:07,835 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:10:07,835 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:10:07,835 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:07,835 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:10:07,836 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:10:07,836 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:10:07,836 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:10:07,836 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:10:07,836 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:07,836 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:10:07,836 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:07,836 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:10:08,640 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:10:08,640 DEBUG: Start: Training
+2016-09-02 15:10:08,717 DEBUG: Info: Time for Training: 0.882653951645[s]
+2016-09-02 15:10:08,717 DEBUG: Done: Training
+2016-09-02 15:10:08,717 DEBUG: Start: Predicting
+2016-09-02 15:10:08,735 DEBUG: Done: Predicting
+2016-09-02 15:10:08,735 DEBUG: Start: Getting Results
+2016-09-02 15:10:08,736 DEBUG: Done: Getting Results
+2016-09-02 15:10:08,736 INFO: Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 442
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:10:08,737 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150958Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150958Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7557a3532a54bd3c8d399cbbb2c8524a7de9d5fe
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150958Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150958Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150958Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..84d3868365da242ffad072c5f67b8ae1e7cf7a68
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150958Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150959Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150959Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b5300f3513c57f0f0f30d03e0fa4bb411bf82d94
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150959Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.805785123967
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 11
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.805785123967
+ - Score on test : 0.742857142857
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-150959Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-150959Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dc24e5ab3ce4311f779e10615a9ac448f3c31825
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-150959Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.8
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151001Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151001Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0d133a07f23120155185db195588333976737a80
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151001Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.855371900826
+accuracy_score on test : 0.87619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.855371900826
+ - Score on test : 0.87619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151001Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151001Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9af703333941341dd7fb8f9bf44c9ac2bb0bb193
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151001Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.619834710744
+accuracy_score on test : 0.514285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 442
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.619834710744
+ - Score on test : 0.514285714286
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151002Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151002Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..911b344f24c9da4bd7a8980d52a75862f71727c5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151002Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7693
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151005Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151005Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3e1a6565cb640da8fc276c974e6bb8c6c80023d1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151005Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.714285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.714285714286
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151005Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151005Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bcf04b3117b3fa0924a3d33f1deaa9b1e4dfd6e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151005Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 0.95867768595
+accuracy_score on test : 0.695238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.95867768595
+ - Score on test : 0.695238095238
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151005Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151005Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..734e28ff3fa8be3412dbed79ee0a9296aea41b2a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151005Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 442
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151006Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151006Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d3952acfdcfff32c20b7e8b8a26169b3ad7721a2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151006Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.760330578512
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151007Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151007Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..29973b950153b9ab49495a28ac1e7cce958b4caa
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151007Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 11, max_depth : 21
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151007Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151007Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..71130f6e3cd3ef2b58ef33ed21fb4bf8e788d2e2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151007Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.760330578512
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151007Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151007Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c3d7ce3a9f3f5841727d49982084ae179f3c4562
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151007Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.586776859504
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 442
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.586776859504
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151008Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151008Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f036d32027c4f697273a74744078a0911f041693
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151008Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 442
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151146-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-151146-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..0628371d9136559d593d877e3241bac6f4eb94e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151146-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,617 @@
+2016-09-02 15:11:46,547 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:11:46,547 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:11:48,345 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:11:48,346 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:11:48,404 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:48,404 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:48,404 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:11:48,404 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:11:48,404 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:48,404 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:48,406 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:11:48,406 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:11:48,406 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:11:48,406 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:11:48,406 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:48,406 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:48,406 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:48,406 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:50,166 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:50,166 DEBUG: Start: Training
+2016-09-02 15:11:50,215 DEBUG: Info: Time for Training: 1.81216716766[s]
+2016-09-02 15:11:50,215 DEBUG: Done: Training
+2016-09-02 15:11:50,215 DEBUG: Start: Predicting
+2016-09-02 15:11:50,218 DEBUG: Done: Predicting
+2016-09-02 15:11:50,218 DEBUG: Start: Getting Results
+2016-09-02 15:11:50,219 DEBUG: Done: Getting Results
+2016-09-02 15:11:50,219 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.838095238095
+
+
+ Classification took 0:00:01
+2016-09-02 15:11:50,219 INFO: Done: Result Analysis
+2016-09-02 15:11:50,314 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:50,315 DEBUG: Start: Training
+2016-09-02 15:11:50,364 DEBUG: Info: Time for Training: 1.96153998375[s]
+2016-09-02 15:11:50,364 DEBUG: Done: Training
+2016-09-02 15:11:50,365 DEBUG: Start: Predicting
+2016-09-02 15:11:50,368 DEBUG: Done: Predicting
+2016-09-02 15:11:50,368 DEBUG: Start: Getting Results
+2016-09-02 15:11:50,369 DEBUG: Done: Getting Results
+2016-09-02 15:11:50,369 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.838095238095
+
+
+ Classification took 0:00:01
+2016-09-02 15:11:50,370 INFO: Done: Result Analysis
+2016-09-02 15:11:50,460 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:50,460 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:50,460 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:11:50,460 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:11:50,460 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:50,461 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:50,463 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:11:50,463 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:11:50,464 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:11:50,464 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:11:50,464 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:50,464 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:50,464 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:50,464 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:51,473 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:51,473 DEBUG: Start: Training
+2016-09-02 15:11:51,478 DEBUG: Info: Time for Training: 1.01959395409[s]
+2016-09-02 15:11:51,478 DEBUG: Done: Training
+2016-09-02 15:11:51,478 DEBUG: Start: Predicting
+2016-09-02 15:11:51,611 DEBUG: Done: Predicting
+2016-09-02 15:11:51,612 DEBUG: Start: Getting Results
+2016-09-02 15:11:51,613 DEBUG: Done: Getting Results
+2016-09-02 15:11:51,613 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.826446280992
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.826446280992
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:01
+2016-09-02 15:11:51,613 INFO: Done: Result Analysis
+2016-09-02 15:11:52,979 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:52,979 DEBUG: Start: Training
+2016-09-02 15:11:53,073 DEBUG: Info: Time for Training: 2.61436080933[s]
+2016-09-02 15:11:53,073 DEBUG: Done: Training
+2016-09-02 15:11:53,073 DEBUG: Start: Predicting
+2016-09-02 15:11:53,081 DEBUG: Done: Predicting
+2016-09-02 15:11:53,081 DEBUG: Start: Getting Results
+2016-09-02 15:11:53,082 DEBUG: Done: Getting Results
+2016-09-02 15:11:53,082 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.904761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 28, max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.904761904762
+
+
+ Classification took 0:00:02
+2016-09-02 15:11:53,082 INFO: Done: Result Analysis
+2016-09-02 15:11:53,214 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:53,214 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:11:53,214 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:53,214 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:53,214 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:11:53,214 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:53,216 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:11:53,216 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:11:53,216 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:11:53,216 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:11:53,216 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:53,216 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:53,216 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:53,217 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:54,018 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:54,019 DEBUG: Start: Training
+2016-09-02 15:11:54,032 DEBUG: Info: Time for Training: 0.81964802742[s]
+2016-09-02 15:11:54,032 DEBUG: Done: Training
+2016-09-02 15:11:54,032 DEBUG: Start: Predicting
+2016-09-02 15:11:54,036 DEBUG: Done: Predicting
+2016-09-02 15:11:54,036 DEBUG: Start: Getting Results
+2016-09-02 15:11:54,037 DEBUG: Done: Getting Results
+2016-09-02 15:11:54,037 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.826446280992
+accuracy_score on test : 0.819047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.826446280992
+ - Score on test : 0.819047619048
+
+
+ Classification took 0:00:00
+2016-09-02 15:11:54,038 INFO: Done: Result Analysis
+2016-09-02 15:11:54,812 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:54,812 DEBUG: Start: Training
+2016-09-02 15:11:54,975 DEBUG: Info: Time for Training: 1.76223707199[s]
+2016-09-02 15:11:54,975 DEBUG: Done: Training
+2016-09-02 15:11:54,975 DEBUG: Start: Predicting
+2016-09-02 15:11:55,015 DEBUG: Done: Predicting
+2016-09-02 15:11:55,015 DEBUG: Start: Getting Results
+2016-09-02 15:11:55,016 DEBUG: Done: Getting Results
+2016-09-02 15:11:55,017 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.628571428571
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1076
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.628571428571
+
+
+ Classification took 0:00:01
+2016-09-02 15:11:55,017 INFO: Done: Result Analysis
+2016-09-02 15:11:55,167 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:55,167 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:55,168 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:11:55,168 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:11:55,168 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:55,168 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:55,170 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:11:55,170 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:11:55,170 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:11:55,170 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:11:55,170 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:55,170 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:55,170 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:55,170 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:55,842 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:55,842 DEBUG: Start: Training
+2016-09-02 15:11:56,009 DEBUG: Info: Time for Training: 0.84378695488[s]
+2016-09-02 15:11:56,009 DEBUG: Done: Training
+2016-09-02 15:11:56,009 DEBUG: Start: Predicting
+2016-09-02 15:11:56,050 DEBUG: Done: Predicting
+2016-09-02 15:11:56,050 DEBUG: Start: Getting Results
+2016-09-02 15:11:56,051 DEBUG: Done: Getting Results
+2016-09-02 15:11:56,051 INFO: Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3488
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
+2016-09-02 15:11:56,051 INFO: Done: Result Analysis
+2016-09-02 15:11:58,076 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:58,077 DEBUG: Start: Training
+2016-09-02 15:11:58,390 DEBUG: Info: Time for Training: 3.2248609066[s]
+2016-09-02 15:11:58,390 DEBUG: Done: Training
+2016-09-02 15:11:58,390 DEBUG: Start: Predicting
+2016-09-02 15:11:58,481 DEBUG: Done: Predicting
+2016-09-02 15:11:58,481 DEBUG: Start: Getting Results
+2016-09-02 15:11:58,482 DEBUG: Done: Getting Results
+2016-09-02 15:11:58,482 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1076
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:03
+2016-09-02 15:11:58,483 INFO: Done: Result Analysis
+2016-09-02 15:11:58,620 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:58,621 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:58,621 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:11:58,621 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:11:58,621 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:58,621 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:58,622 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:11:58,622 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:11:58,623 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:11:58,623 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:11:58,623 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:58,623 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:58,623 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:58,623 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:58,879 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:58,879 DEBUG: Start: Training
+2016-09-02 15:11:58,881 DEBUG: Info: Time for Training: 0.261435985565[s]
+2016-09-02 15:11:58,881 DEBUG: Done: Training
+2016-09-02 15:11:58,882 DEBUG: Start: Predicting
+2016-09-02 15:11:58,884 DEBUG: Done: Predicting
+2016-09-02 15:11:58,884 DEBUG: Start: Getting Results
+2016-09-02 15:11:58,885 DEBUG: Done: Getting Results
+2016-09-02 15:11:58,885 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 24
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:11:58,885 INFO: Done: Result Analysis
+2016-09-02 15:11:58,993 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:58,993 DEBUG: Start: Training
+2016-09-02 15:11:58,997 DEBUG: Info: Time for Training: 0.377254962921[s]
+2016-09-02 15:11:58,997 DEBUG: Done: Training
+2016-09-02 15:11:58,997 DEBUG: Start: Predicting
+2016-09-02 15:11:59,000 DEBUG: Done: Predicting
+2016-09-02 15:11:59,000 DEBUG: Start: Getting Results
+2016-09-02 15:11:59,002 DEBUG: Done: Getting Results
+2016-09-02 15:11:59,002 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:11:59,002 INFO: Done: Result Analysis
+2016-09-02 15:11:59,068 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:59,068 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:11:59,068 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:11:59,068 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:11:59,069 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:59,069 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:11:59,069 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:11:59,069 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:11:59,069 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:11:59,069 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:11:59,070 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:59,070 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:11:59,070 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:59,070 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:11:59,361 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:11:59,361 DEBUG: Start: Training
+2016-09-02 15:11:59,362 DEBUG: Info: Time for Training: 0.294367074966[s]
+2016-09-02 15:11:59,362 DEBUG: Done: Training
+2016-09-02 15:11:59,362 DEBUG: Start: Predicting
+2016-09-02 15:11:59,384 DEBUG: Done: Predicting
+2016-09-02 15:11:59,384 DEBUG: Start: Getting Results
+2016-09-02 15:11:59,385 DEBUG: Done: Getting Results
+2016-09-02 15:11:59,386 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.723140495868
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 38
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.723140495868
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:11:59,386 INFO: Done: Result Analysis
+2016-09-02 15:12:01,079 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:12:01,079 DEBUG: Start: Training
+2016-09-02 15:12:01,142 DEBUG: Info: Time for Training: 2.07436490059[s]
+2016-09-02 15:12:01,142 DEBUG: Done: Training
+2016-09-02 15:12:01,142 DEBUG: Start: Predicting
+2016-09-02 15:12:01,149 DEBUG: Done: Predicting
+2016-09-02 15:12:01,150 DEBUG: Start: Getting Results
+2016-09-02 15:12:01,151 DEBUG: Done: Getting Results
+2016-09-02 15:12:01,151 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 25, max_depth : 17
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:02
+2016-09-02 15:12:01,151 INFO: Done: Result Analysis
+2016-09-02 15:12:01,225 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:12:01,225 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:12:01,225 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:12:01,225 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:12:01,225 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:12:01,225 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:12:01,227 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:12:01,227 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:12:01,227 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:12:01,227 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:12:01,227 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:12:01,227 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:12:01,227 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:12:01,227 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:12:01,580 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:12:01,580 DEBUG: Start: Training
+2016-09-02 15:12:01,582 DEBUG: Info: Time for Training: 0.357192993164[s]
+2016-09-02 15:12:01,582 DEBUG: Done: Training
+2016-09-02 15:12:01,582 DEBUG: Start: Predicting
+2016-09-02 15:12:01,591 DEBUG: Done: Predicting
+2016-09-02 15:12:01,591 DEBUG: Start: Getting Results
+2016-09-02 15:12:01,592 DEBUG: Done: Getting Results
+2016-09-02 15:12:01,592 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.305785123967
+accuracy_score on test : 0.285714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.305785123967
+ - Score on test : 0.285714285714
+
+
+ Classification took 0:00:00
+2016-09-02 15:12:01,592 INFO: Done: Result Analysis
+2016-09-02 15:12:01,754 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:12:01,754 DEBUG: Start: Training
+2016-09-02 15:12:01,803 DEBUG: Info: Time for Training: 0.578412771225[s]
+2016-09-02 15:12:01,803 DEBUG: Done: Training
+2016-09-02 15:12:01,803 DEBUG: Start: Predicting
+2016-09-02 15:12:01,813 DEBUG: Done: Predicting
+2016-09-02 15:12:01,813 DEBUG: Start: Getting Results
+2016-09-02 15:12:01,814 DEBUG: Done: Getting Results
+2016-09-02 15:12:01,814 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.54132231405
+accuracy_score on test : 0.552380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1076
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.54132231405
+ - Score on test : 0.552380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:12:01,814 INFO: Done: Result Analysis
+2016-09-02 15:12:01,873 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:12:01,873 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:12:01,873 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:12:01,874 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:12:01,874 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:12:01,874 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:12:01,874 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:12:01,874 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:12:01,874 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:12:01,874 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:12:01,875 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:12:01,875 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:12:01,875 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:12:01,875 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:12:02,678 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:12:02,678 DEBUG: Start: Training
+2016-09-02 15:12:02,755 DEBUG: Info: Time for Training: 0.882038831711[s]
+2016-09-02 15:12:02,755 DEBUG: Done: Training
+2016-09-02 15:12:02,755 DEBUG: Start: Predicting
+2016-09-02 15:12:02,773 DEBUG: Done: Predicting
+2016-09-02 15:12:02,773 DEBUG: Start: Getting Results
+2016-09-02 15:12:02,774 DEBUG: Done: Getting Results
+2016-09-02 15:12:02,774 INFO: Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1076
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:12:02,774 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151150Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151150Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ad000e16585fdd90b06d274133b08493c4a31708
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151150Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.838095238095
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151150Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151150Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9a00f7c3c94d3b893b7950adf6b51d6eef795d1a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151150Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.838095238095
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151151Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151151Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..54d6201e63522cb170dc0794570ed3aca0577e73
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151151Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.826446280992
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.826446280992
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151153Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151153Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..839a7bcc9a9b295a694669d8b24dee210f089296
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151153Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.904761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 28, max_depth : 8
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.904761904762
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151154Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151154Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..27ab06b82949bbff86b7b66ca3297c79b43e5228
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151154Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.826446280992
+accuracy_score on test : 0.819047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.826446280992
+ - Score on test : 0.819047619048
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151155Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151155Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..738115a071d6573947971fce5434b3d20c9acd54
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151155Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.628571428571
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1076
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.628571428571
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151156Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151156Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aa9dc933087d82a3c46c002d62fdc02fdb641d53
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151156Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3488
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151158Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151158Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..92ad8b4074ba0d5574f9572b863e54946e3375af
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151158Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 24
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151158Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151158Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..227ff78848e519f378faff5f0f7fbb4dcd2cbc57
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151158Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1076
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151159Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151159Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..517042e492cf4ee12b715a429ee540bea981b339
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151159Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151159Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151159Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6d23e429df931d01c78c8c6b63323dbf51b70dc8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151159Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.723140495868
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 38
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.723140495868
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151201Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151201Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7f9ab243c2341f543526985848e6101ef47fbf1a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151201Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 25, max_depth : 17
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151201Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151201Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..478ef7de80883ba84867e75cceaa115e75dbdd0b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151201Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.305785123967
+accuracy_score on test : 0.285714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.305785123967
+ - Score on test : 0.285714285714
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151201Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151201Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b169af315cf0ce3ebdf6c9a89cbfa630abf931a2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151201Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.54132231405
+accuracy_score on test : 0.552380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1076
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.54132231405
+ - Score on test : 0.552380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151202Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151202Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9205c0ba6d2dcaba734c489afc774e298b612bfd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151202Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 1076
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151255-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-151255-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..35befef751a620ae9d188fd760170a953894a817
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151255-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,617 @@
+2016-09-02 15:12:55,593 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:12:55,593 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:12:57,959 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:12:57,960 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:12:58,006 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:12:58,006 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:12:58,006 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:12:58,006 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:12:58,006 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:12:58,006 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:12:58,008 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:12:58,008 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:12:58,008 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:12:58,008 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:12:58,008 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:12:58,008 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:12:58,008 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:12:58,008 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:12:59,776 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:12:59,777 DEBUG: Start: Training
+2016-09-02 15:12:59,825 DEBUG: Info: Time for Training: 1.8196310997[s]
+2016-09-02 15:12:59,825 DEBUG: Done: Training
+2016-09-02 15:12:59,825 DEBUG: Start: Predicting
+2016-09-02 15:12:59,827 DEBUG: Done: Predicting
+2016-09-02 15:12:59,828 DEBUG: Start: Getting Results
+2016-09-02 15:12:59,829 DEBUG: Done: Getting Results
+2016-09-02 15:12:59,829 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 24
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:01
+2016-09-02 15:12:59,829 INFO: Done: Result Analysis
+2016-09-02 15:13:00,048 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:00,048 DEBUG: Start: Training
+2016-09-02 15:13:00,110 DEBUG: Info: Time for Training: 2.10455513[s]
+2016-09-02 15:13:00,110 DEBUG: Done: Training
+2016-09-02 15:13:00,110 DEBUG: Start: Predicting
+2016-09-02 15:13:00,113 DEBUG: Done: Predicting
+2016-09-02 15:13:00,113 DEBUG: Start: Getting Results
+2016-09-02 15:13:00,115 DEBUG: Done: Getting Results
+2016-09-02 15:13:00,115 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:02
+2016-09-02 15:13:00,115 INFO: Done: Result Analysis
+2016-09-02 15:13:00,269 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:00,269 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:00,269 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:13:00,269 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:13:00,269 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:00,269 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:00,271 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:00,271 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:00,271 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:00,271 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:00,271 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:00,271 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:00,272 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:00,272 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:01,250 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:01,250 DEBUG: Start: Training
+2016-09-02 15:13:01,254 DEBUG: Info: Time for Training: 0.986948013306[s]
+2016-09-02 15:13:01,254 DEBUG: Done: Training
+2016-09-02 15:13:01,255 DEBUG: Start: Predicting
+2016-09-02 15:13:01,375 DEBUG: Done: Predicting
+2016-09-02 15:13:01,376 DEBUG: Start: Getting Results
+2016-09-02 15:13:01,377 DEBUG: Done: Getting Results
+2016-09-02 15:13:01,377 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.859504132231
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.859504132231
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:01,377 INFO: Done: Result Analysis
+2016-09-02 15:13:02,680 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:02,680 DEBUG: Start: Training
+2016-09-02 15:13:02,755 DEBUG: Info: Time for Training: 2.487210989[s]
+2016-09-02 15:13:02,755 DEBUG: Done: Training
+2016-09-02 15:13:02,755 DEBUG: Start: Predicting
+2016-09-02 15:13:02,762 DEBUG: Done: Predicting
+2016-09-02 15:13:02,762 DEBUG: Start: Getting Results
+2016-09-02 15:13:02,763 DEBUG: Done: Getting Results
+2016-09-02 15:13:02,763 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.904761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 23, max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.904761904762
+
+
+ Classification took 0:00:02
+2016-09-02 15:13:02,763 INFO: Done: Result Analysis
+2016-09-02 15:13:02,927 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:02,927 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:02,927 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:13:02,928 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:13:02,928 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:02,928 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:02,930 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:02,930 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:02,931 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:02,931 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:02,931 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:02,931 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:02,931 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:02,931 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:03,649 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:03,649 DEBUG: Start: Training
+2016-09-02 15:13:03,662 DEBUG: Info: Time for Training: 0.737128019333[s]
+2016-09-02 15:13:03,663 DEBUG: Done: Training
+2016-09-02 15:13:03,663 DEBUG: Start: Predicting
+2016-09-02 15:13:03,666 DEBUG: Done: Predicting
+2016-09-02 15:13:03,666 DEBUG: Start: Getting Results
+2016-09-02 15:13:03,667 DEBUG: Done: Getting Results
+2016-09-02 15:13:03,667 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.847107438017
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.847107438017
+ - Score on test : 0.838095238095
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:03,668 INFO: Done: Result Analysis
+2016-09-02 15:13:04,536 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:04,536 DEBUG: Start: Training
+2016-09-02 15:13:04,695 DEBUG: Info: Time for Training: 1.76975512505[s]
+2016-09-02 15:13:04,695 DEBUG: Done: Training
+2016-09-02 15:13:04,695 DEBUG: Start: Predicting
+2016-09-02 15:13:04,737 DEBUG: Done: Predicting
+2016-09-02 15:13:04,738 DEBUG: Start: Getting Results
+2016-09-02 15:13:04,739 DEBUG: Done: Getting Results
+2016-09-02 15:13:04,739 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.747933884298
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2306
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.747933884298
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:01
+2016-09-02 15:13:04,739 INFO: Done: Result Analysis
+2016-09-02 15:13:04,886 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:04,886 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:04,887 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:13:04,887 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:13:04,887 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:04,887 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:04,888 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:04,888 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:04,889 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:04,889 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:04,889 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:04,889 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:04,889 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:04,889 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:05,461 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:05,461 DEBUG: Start: Training
+2016-09-02 15:13:05,606 DEBUG: Info: Time for Training: 0.721157073975[s]
+2016-09-02 15:13:05,606 DEBUG: Done: Training
+2016-09-02 15:13:05,607 DEBUG: Start: Predicting
+2016-09-02 15:13:05,641 DEBUG: Done: Predicting
+2016-09-02 15:13:05,642 DEBUG: Start: Getting Results
+2016-09-02 15:13:05,642 DEBUG: Done: Getting Results
+2016-09-02 15:13:05,643 INFO: Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2869
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:05,643 INFO: Done: Result Analysis
+2016-09-02 15:13:07,769 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:07,769 DEBUG: Start: Training
+2016-09-02 15:13:08,081 DEBUG: Info: Time for Training: 3.19563484192[s]
+2016-09-02 15:13:08,081 DEBUG: Done: Training
+2016-09-02 15:13:08,081 DEBUG: Start: Predicting
+2016-09-02 15:13:08,172 DEBUG: Done: Predicting
+2016-09-02 15:13:08,172 DEBUG: Start: Getting Results
+2016-09-02 15:13:08,173 DEBUG: Done: Getting Results
+2016-09-02 15:13:08,173 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2306
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:03
+2016-09-02 15:13:08,190 INFO: Done: Result Analysis
+2016-09-02 15:13:08,345 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:08,346 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:13:08,346 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:08,347 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:08,348 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:08,348 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:08,348 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:08,364 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:08,365 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:13:08,365 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:08,365 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:08,365 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:08,366 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:08,366 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:08,584 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:08,584 DEBUG: Start: Training
+2016-09-02 15:13:08,586 DEBUG: Info: Time for Training: 0.241389989853[s]
+2016-09-02 15:13:08,586 DEBUG: Done: Training
+2016-09-02 15:13:08,586 DEBUG: Start: Predicting
+2016-09-02 15:13:08,588 DEBUG: Done: Predicting
+2016-09-02 15:13:08,589 DEBUG: Start: Getting Results
+2016-09-02 15:13:08,589 DEBUG: Done: Getting Results
+2016-09-02 15:13:08,590 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 0.863636363636
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.863636363636
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:08,590 INFO: Done: Result Analysis
+2016-09-02 15:13:08,727 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:08,727 DEBUG: Start: Training
+2016-09-02 15:13:08,731 DEBUG: Info: Time for Training: 0.386875867844[s]
+2016-09-02 15:13:08,731 DEBUG: Done: Training
+2016-09-02 15:13:08,731 DEBUG: Start: Predicting
+2016-09-02 15:13:08,734 DEBUG: Done: Predicting
+2016-09-02 15:13:08,734 DEBUG: Start: Getting Results
+2016-09-02 15:13:08,736 DEBUG: Done: Getting Results
+2016-09-02 15:13:08,736 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:08,736 INFO: Done: Result Analysis
+2016-09-02 15:13:08,893 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:08,893 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:08,893 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:13:08,893 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:13:08,893 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:08,893 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:08,894 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:08,894 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:08,895 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:08,895 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:08,895 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:08,895 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:08,895 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:08,895 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:09,357 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:09,357 DEBUG: Start: Training
+2016-09-02 15:13:09,358 DEBUG: Info: Time for Training: 0.466574192047[s]
+2016-09-02 15:13:09,359 DEBUG: Done: Training
+2016-09-02 15:13:09,359 DEBUG: Start: Predicting
+2016-09-02 15:13:09,390 DEBUG: Done: Predicting
+2016-09-02 15:13:09,390 DEBUG: Start: Getting Results
+2016-09-02 15:13:09,391 DEBUG: Done: Getting Results
+2016-09-02 15:13:09,392 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.731404958678
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.731404958678
+ - Score on test : 0.742857142857
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:09,392 INFO: Done: Result Analysis
+2016-09-02 15:13:11,047 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:11,047 DEBUG: Start: Training
+2016-09-02 15:13:11,106 DEBUG: Info: Time for Training: 2.21439313889[s]
+2016-09-02 15:13:11,106 DEBUG: Done: Training
+2016-09-02 15:13:11,106 DEBUG: Start: Predicting
+2016-09-02 15:13:11,113 DEBUG: Done: Predicting
+2016-09-02 15:13:11,113 DEBUG: Start: Getting Results
+2016-09-02 15:13:11,114 DEBUG: Done: Getting Results
+2016-09-02 15:13:11,114 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 21, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.847619047619
+
+
+ Classification took 0:00:02
+2016-09-02 15:13:11,114 INFO: Done: Result Analysis
+2016-09-02 15:13:11,247 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:11,247 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:11,248 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:13:11,248 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:13:11,248 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:11,248 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:11,250 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:11,250 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:11,250 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:11,250 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:11,251 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:11,251 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:11,251 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:11,251 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:11,603 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:11,603 DEBUG: Start: Training
+2016-09-02 15:13:11,604 DEBUG: Info: Time for Training: 0.358716964722[s]
+2016-09-02 15:13:11,605 DEBUG: Done: Training
+2016-09-02 15:13:11,605 DEBUG: Start: Predicting
+2016-09-02 15:13:11,619 DEBUG: Done: Predicting
+2016-09-02 15:13:11,620 DEBUG: Start: Getting Results
+2016-09-02 15:13:11,621 DEBUG: Done: Getting Results
+2016-09-02 15:13:11,621 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.268595041322
+accuracy_score on test : 0.266666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.268595041322
+ - Score on test : 0.266666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:11,621 INFO: Done: Result Analysis
+2016-09-02 15:13:11,865 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:11,865 DEBUG: Start: Training
+2016-09-02 15:13:11,933 DEBUG: Info: Time for Training: 0.687331914902[s]
+2016-09-02 15:13:11,933 DEBUG: Done: Training
+2016-09-02 15:13:11,933 DEBUG: Start: Predicting
+2016-09-02 15:13:11,947 DEBUG: Done: Predicting
+2016-09-02 15:13:11,947 DEBUG: Start: Getting Results
+2016-09-02 15:13:11,948 DEBUG: Done: Getting Results
+2016-09-02 15:13:11,948 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.553719008264
+accuracy_score on test : 0.580952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2306
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.553719008264
+ - Score on test : 0.580952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:11,949 INFO: Done: Result Analysis
+2016-09-02 15:13:12,090 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:12,090 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:12,090 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:13:12,090 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:13:12,090 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:12,090 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:12,091 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:12,091 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:12,091 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:12,091 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:12,091 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:12,091 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:12,091 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:12,091 DEBUG: Start: RandomSearch best settings with 10 iterations
+2016-09-02 15:13:12,891 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:12,891 DEBUG: Start: Training
+2016-09-02 15:13:12,965 DEBUG: Info: Time for Training: 0.875777006149[s]
+2016-09-02 15:13:12,965 DEBUG: Done: Training
+2016-09-02 15:13:12,965 DEBUG: Start: Predicting
+2016-09-02 15:13:12,983 DEBUG: Done: Predicting
+2016-09-02 15:13:12,983 DEBUG: Start: Getting Results
+2016-09-02 15:13:12,984 DEBUG: Done: Getting Results
+2016-09-02 15:13:12,984 INFO: Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2306
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:12,984 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151259Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151259Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3a74a47e8cda47eb8e91bfa4095e5a6cc37fd271
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151259Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 24
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151300Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151300Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f31b3b29a100586a8e397e1685f7b60c8e8aa78b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151300Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151301Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151301Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d8f1a232cb4c1efcf5aebcca746694fe415caba3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151301Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.859504132231
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.859504132231
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151302Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151302Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ee89468918c1bb4350841badd60aaa6947f7f0a5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151302Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.904761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 23, max_depth : 7
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.904761904762
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151303Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151303Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..020f2991b5b13513c0f1ed459e1aead23273860b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151303Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.847107438017
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.847107438017
+ - Score on test : 0.838095238095
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151304Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151304Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a7b7aee1277b176ee07af32099f9e937519870d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151304Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.747933884298
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2306
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.747933884298
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151305Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151305Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0ca5c30d47edf083d4235de4c1b8a86cedc85ef8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151305Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2869
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151308Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151308Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0b0c087c45f9a11f4cfc82f8863814a950c0b6dc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151308Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151308Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151308Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..906e2478ea2468893d04910e8320e65414e0e676
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151308Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 0.863636363636
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.863636363636
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151308Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151308Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c141c5934f2ba985e8b5e206202dec33f6e6ecba
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151308Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2306
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151309Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151309Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bb6315fffc8ec6736ea3ff15bd0b3d501d04fbe8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151309Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.731404958678
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.731404958678
+ - Score on test : 0.742857142857
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151311Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151311Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a9aace9419585db2816274a6eed64d65aa02125f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151311Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 21, max_depth : 18
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.847619047619
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151311Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151311Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d253a3dc019941b6e5226470de7b605e1c241119
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151311Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.268595041322
+accuracy_score on test : 0.266666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.268595041322
+ - Score on test : 0.266666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151311Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151311Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..05fac9a00a257f3f96ab9b831cfe44e5a6d8e36a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151311Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.553719008264
+accuracy_score on test : 0.580952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2306
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.553719008264
+ - Score on test : 0.580952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151312Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151312Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..41bf267adc0a94cb55293c087d271b101d80a855
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151312Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2306
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 10 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151347-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-151347-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..dbb0027a52d3111e075764fd63207a810db3fa2b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151347-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,617 @@
+2016-09-02 15:13:47,347 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:13:47,347 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:13:49,198 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:13:49,200 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:13:49,247 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:49,247 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:49,248 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:13:49,248 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:13:49,248 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:49,248 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:49,251 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:49,251 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:49,251 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:49,251 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:49,251 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:49,251 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:49,251 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:49,251 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:49,643 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:49,643 DEBUG: Start: Training
+2016-09-02 15:13:49,691 DEBUG: Info: Time for Training: 0.444986104965[s]
+2016-09-02 15:13:49,691 DEBUG: Done: Training
+2016-09-02 15:13:49,691 DEBUG: Start: Predicting
+2016-09-02 15:13:49,691 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:49,691 DEBUG: Start: Training
+2016-09-02 15:13:49,694 DEBUG: Done: Predicting
+2016-09-02 15:13:49,694 DEBUG: Start: Getting Results
+2016-09-02 15:13:49,695 DEBUG: Done: Getting Results
+2016-09-02 15:13:49,695 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.771428571429
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:49,695 INFO: Done: Result Analysis
+2016-09-02 15:13:49,740 DEBUG: Info: Time for Training: 0.494187116623[s]
+2016-09-02 15:13:49,740 DEBUG: Done: Training
+2016-09-02 15:13:49,740 DEBUG: Start: Predicting
+2016-09-02 15:13:49,743 DEBUG: Done: Predicting
+2016-09-02 15:13:49,743 DEBUG: Start: Getting Results
+2016-09-02 15:13:49,745 DEBUG: Done: Getting Results
+2016-09-02 15:13:49,745 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.761904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:49,745 INFO: Done: Result Analysis
+2016-09-02 15:13:49,894 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:49,894 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:49,894 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:13:49,894 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:13:49,894 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:49,894 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:49,897 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:49,897 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:49,897 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:49,897 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:49,897 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:49,898 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:49,898 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:49,898 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:50,119 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:50,120 DEBUG: Start: Training
+2016-09-02 15:13:50,124 DEBUG: Info: Time for Training: 0.23109292984[s]
+2016-09-02 15:13:50,124 DEBUG: Done: Training
+2016-09-02 15:13:50,124 DEBUG: Start: Predicting
+2016-09-02 15:13:50,266 DEBUG: Done: Predicting
+2016-09-02 15:13:50,266 DEBUG: Start: Getting Results
+2016-09-02 15:13:50,267 DEBUG: Done: Getting Results
+2016-09-02 15:13:50,267 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.789256198347
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 19
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.789256198347
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:50,268 INFO: Done: Result Analysis
+2016-09-02 15:13:50,387 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:50,388 DEBUG: Start: Training
+2016-09-02 15:13:50,451 DEBUG: Info: Time for Training: 0.558418989182[s]
+2016-09-02 15:13:50,451 DEBUG: Done: Training
+2016-09-02 15:13:50,452 DEBUG: Start: Predicting
+2016-09-02 15:13:50,458 DEBUG: Done: Predicting
+2016-09-02 15:13:50,458 DEBUG: Start: Getting Results
+2016-09-02 15:13:50,459 DEBUG: Done: Getting Results
+2016-09-02 15:13:50,459 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 19, 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 : 0.847619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:50,459 INFO: Done: Result Analysis
+2016-09-02 15:13:50,550 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:50,550 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:50,550 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:13:50,551 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:13:50,551 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:50,551 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:50,553 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:50,553 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:50,553 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:50,553 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:50,553 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:50,553 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:50,554 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:50,554 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:50,686 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:50,686 DEBUG: Start: Training
+2016-09-02 15:13:50,690 DEBUG: Info: Time for Training: 0.141922950745[s]
+2016-09-02 15:13:50,690 DEBUG: Done: Training
+2016-09-02 15:13:50,690 DEBUG: Start: Predicting
+2016-09-02 15:13:50,694 DEBUG: Done: Predicting
+2016-09-02 15:13:50,694 DEBUG: Start: Getting Results
+2016-09-02 15:13:50,695 DEBUG: Done: Getting Results
+2016-09-02 15:13:50,695 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.772727272727
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.772727272727
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:50,695 INFO: Done: Result Analysis
+2016-09-02 15:13:50,927 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:50,927 DEBUG: Start: Training
+2016-09-02 15:13:51,101 DEBUG: Info: Time for Training: 0.55322599411[s]
+2016-09-02 15:13:51,102 DEBUG: Done: Training
+2016-09-02 15:13:51,102 DEBUG: Start: Predicting
+2016-09-02 15:13:51,143 DEBUG: Done: Predicting
+2016-09-02 15:13:51,143 DEBUG: Start: Getting Results
+2016-09-02 15:13:51,144 DEBUG: Done: Getting Results
+2016-09-02 15:13:51,144 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.694214876033
+accuracy_score on test : 0.685714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9918
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.694214876033
+ - Score on test : 0.685714285714
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:51,144 INFO: Done: Result Analysis
+2016-09-02 15:13:51,297 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:51,297 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:51,297 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:13:51,297 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:13:51,297 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:51,297 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:51,299 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:51,299 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:13:51,299 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:51,299 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:13:51,299 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:51,299 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:51,299 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:51,299 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:51,370 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:51,370 DEBUG: Start: Training
+2016-09-02 15:13:51,375 DEBUG: Info: Time for Training: 0.0801219940186[s]
+2016-09-02 15:13:51,375 DEBUG: Done: Training
+2016-09-02 15:13:51,375 DEBUG: Start: Predicting
+2016-09-02 15:13:51,379 DEBUG: Done: Predicting
+2016-09-02 15:13:51,379 DEBUG: Start: Getting Results
+2016-09-02 15:13:51,380 DEBUG: Done: Getting Results
+2016-09-02 15:13:51,380 INFO: Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.264462809917
+accuracy_score on test : 0.27619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9918
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.264462809917
+ - Score on test : 0.27619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:51,380 INFO: Done: Result Analysis
+2016-09-02 15:13:51,923 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:51,923 DEBUG: Start: Training
+2016-09-02 15:13:52,236 DEBUG: Info: Time for Training: 0.940616846085[s]
+2016-09-02 15:13:52,236 DEBUG: Done: Training
+2016-09-02 15:13:52,236 DEBUG: Start: Predicting
+2016-09-02 15:13:52,329 DEBUG: Done: Predicting
+2016-09-02 15:13:52,329 DEBUG: Start: Getting Results
+2016-09-02 15:13:52,330 DEBUG: Done: Getting Results
+2016-09-02 15:13:52,330 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9918
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:52,330 INFO: Done: Result Analysis
+2016-09-02 15:13:52,446 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:52,446 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:52,447 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:13:52,447 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:13:52,447 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:52,447 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:52,448 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:52,448 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:52,448 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:52,448 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:52,448 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:52,448 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:52,448 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:52,448 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:52,510 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:52,510 DEBUG: Start: Training
+2016-09-02 15:13:52,513 DEBUG: Info: Time for Training: 0.0666189193726[s]
+2016-09-02 15:13:52,513 DEBUG: Done: Training
+2016-09-02 15:13:52,513 DEBUG: Start: Predicting
+2016-09-02 15:13:52,515 DEBUG: Done: Predicting
+2016-09-02 15:13:52,515 DEBUG: Start: Getting Results
+2016-09-02 15:13:52,516 DEBUG: Done: Getting Results
+2016-09-02 15:13:52,516 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 14
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.857142857143
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:52,517 INFO: Done: Result Analysis
+2016-09-02 15:13:52,536 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:52,536 DEBUG: Start: Training
+2016-09-02 15:13:52,541 DEBUG: Info: Time for Training: 0.0951437950134[s]
+2016-09-02 15:13:52,541 DEBUG: Done: Training
+2016-09-02 15:13:52,541 DEBUG: Start: Predicting
+2016-09-02 15:13:52,544 DEBUG: Done: Predicting
+2016-09-02 15:13:52,544 DEBUG: Start: Getting Results
+2016-09-02 15:13:52,546 DEBUG: Done: Getting Results
+2016-09-02 15:13:52,546 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.857142857143
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:52,546 INFO: Done: Result Analysis
+2016-09-02 15:13:52,697 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:52,697 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:52,697 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:13:52,697 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:13:52,697 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:52,697 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:52,699 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:52,699 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:52,699 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:52,699 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:52,699 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:52,699 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:52,699 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:52,699 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:52,768 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:52,768 DEBUG: Start: Training
+2016-09-02 15:13:52,769 DEBUG: Info: Time for Training: 0.0731210708618[s]
+2016-09-02 15:13:52,769 DEBUG: Done: Training
+2016-09-02 15:13:52,769 DEBUG: Start: Predicting
+2016-09-02 15:13:52,792 DEBUG: Done: Predicting
+2016-09-02 15:13:52,793 DEBUG: Start: Getting Results
+2016-09-02 15:13:52,793 DEBUG: Done: Getting Results
+2016-09-02 15:13:52,794 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.735537190083
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.735537190083
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:52,794 INFO: Done: Result Analysis
+2016-09-02 15:13:53,091 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:53,091 DEBUG: Start: Training
+2016-09-02 15:13:53,139 DEBUG: Info: Time for Training: 0.443457126617[s]
+2016-09-02 15:13:53,139 DEBUG: Done: Training
+2016-09-02 15:13:53,140 DEBUG: Start: Predicting
+2016-09-02 15:13:53,146 DEBUG: Done: Predicting
+2016-09-02 15:13:53,146 DEBUG: Start: Getting Results
+2016-09-02 15:13:53,147 DEBUG: Done: Getting Results
+2016-09-02 15:13:53,147 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 19, 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 : 0.790476190476
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:53,147 INFO: Done: Result Analysis
+2016-09-02 15:13:53,247 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:53,248 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:13:53,248 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:53,249 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:53,249 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:13:53,249 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:53,250 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:53,250 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:53,250 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:53,251 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:53,251 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:53,251 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:53,251 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:53,251 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:53,344 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:53,344 DEBUG: Start: Training
+2016-09-02 15:13:53,345 DEBUG: Info: Time for Training: 0.0991361141205[s]
+2016-09-02 15:13:53,346 DEBUG: Done: Training
+2016-09-02 15:13:53,346 DEBUG: Start: Predicting
+2016-09-02 15:13:53,359 DEBUG: Done: Predicting
+2016-09-02 15:13:53,359 DEBUG: Start: Getting Results
+2016-09-02 15:13:53,360 DEBUG: Done: Getting Results
+2016-09-02 15:13:53,360 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.714876033058
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.714876033058
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:53,360 INFO: Done: Result Analysis
+2016-09-02 15:13:53,375 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:53,375 DEBUG: Start: Training
+2016-09-02 15:13:53,423 DEBUG: Info: Time for Training: 0.17630815506[s]
+2016-09-02 15:13:53,423 DEBUG: Done: Training
+2016-09-02 15:13:53,423 DEBUG: Start: Predicting
+2016-09-02 15:13:53,433 DEBUG: Done: Predicting
+2016-09-02 15:13:53,433 DEBUG: Start: Getting Results
+2016-09-02 15:13:53,434 DEBUG: Done: Getting Results
+2016-09-02 15:13:53,434 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.586776859504
+accuracy_score on test : 0.638095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9918
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.586776859504
+ - Score on test : 0.638095238095
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:53,435 INFO: Done: Result Analysis
+2016-09-02 15:13:53,490 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:53,490 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:13:53,490 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:13:53,490 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:13:53,490 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:53,490 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:13:53,491 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:53,491 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:13:53,491 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:53,491 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:13:53,491 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:53,491 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:13:53,491 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:53,491 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:13:53,680 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:13:53,680 DEBUG: Start: Training
+2016-09-02 15:13:53,757 DEBUG: Info: Time for Training: 0.26763510704[s]
+2016-09-02 15:13:53,757 DEBUG: Done: Training
+2016-09-02 15:13:53,757 DEBUG: Start: Predicting
+2016-09-02 15:13:53,774 DEBUG: Done: Predicting
+2016-09-02 15:13:53,774 DEBUG: Start: Getting Results
+2016-09-02 15:13:53,775 DEBUG: Done: Getting Results
+2016-09-02 15:13:53,775 INFO: Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9918
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:13:53,776 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151349Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151349Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ee36c675119856772bef5b13221c452bb3896fef
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151349Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.761904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151349Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151349Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b78ba0ce0efd1bec00182868191db3a46a2845c8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151349Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.771428571429
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151350Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151350Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6ed8e2be4e5783e11ca586382f036ce61cca3d80
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151350Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.789256198347
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 19
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.789256198347
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151350Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151350Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e4a09c6a88589ae6dedcbfbedf069626cb2287b4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151350Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 19, 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 : 0.847619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151350Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151350Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cd497553c912fd86f9ca34c97495c7836cee0815
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151350Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.772727272727
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.772727272727
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151351Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151351Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6b17d1bd0791b0f8974d2bef8d139cba858b028d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151351Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.694214876033
+accuracy_score on test : 0.685714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9918
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.694214876033
+ - Score on test : 0.685714285714
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151351Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151351Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9cd070019bf171c190e7a16d2cc977bb58d20c1e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151351Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.264462809917
+accuracy_score on test : 0.27619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9918
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.264462809917
+ - Score on test : 0.27619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d79091e6610a61dd6a296d48d4385087c202599a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.857142857143
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bf37c58fd1e647e1e2d6f21cd62f385f7d5c0dbb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 14
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.857142857143
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..814e02c0c24cba9195d883882b8f31a9577dda89
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.735537190083
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.735537190083
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aea61ee43a1fae6fd1aeb3e255b5b688dd4a3081
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151352Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9918
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6f6d3be86f43bff6a74cdef3390143596225c86b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 19, 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 : 0.790476190476
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f0a562189ea011d51d43bdd92c88e4f90016a7ea
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.714876033058
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.714876033058
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c1122e49846c252210f1ef9fbc25978711f7b6be
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.586776859504
+accuracy_score on test : 0.638095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9918
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.586776859504
+ - Score on test : 0.638095238095
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cfd1298ab8ae708096dc312d301f19d484adf0ed
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151353Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9918
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151628-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-151628-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..b92ffbe0072a05140a56e6d5935f45b5af6fdcb3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151628-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,943 @@
+2016-09-02 15:16:28,563 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:16:28,563 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:16:31,277 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:16:31,278 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:16:31,329 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:31,329 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:31,329 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:16:31,329 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:16:31,329 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:31,329 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:31,331 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:16:31,331 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:16:31,331 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:16:31,331 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:16:31,332 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:31,332 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:31,332 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:31,332 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:31,749 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:31,749 DEBUG: Start: Training
+2016-09-02 15:16:31,779 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:31,779 DEBUG: Start: Training
+2016-09-02 15:16:31,791 DEBUG: Info: Time for Training: 0.463835000992[s]
+2016-09-02 15:16:31,791 DEBUG: Done: Training
+2016-09-02 15:16:31,791 DEBUG: Start: Predicting
+2016-09-02 15:16:31,794 DEBUG: Done: Predicting
+2016-09-02 15:16:31,794 DEBUG: Start: Getting Results
+2016-09-02 15:16:31,795 DEBUG: Done: Getting Results
+2016-09-02 15:16:31,795 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.866666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:31,823 DEBUG: Info: Time for Training: 0.495595216751[s]
+2016-09-02 15:16:31,823 DEBUG: Done: Training
+2016-09-02 15:16:31,823 DEBUG: Start: Predicting
+2016-09-02 15:16:31,826 DEBUG: Done: Predicting
+2016-09-02 15:16:31,826 DEBUG: Start: Getting Results
+2016-09-02 15:16:31,828 DEBUG: Done: Getting Results
+2016-09-02 15:16:31,828 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.866666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:31,910 INFO: Done: Result Analysis
+2016-09-02 15:16:31,910 INFO: Done: Result Analysis
+2016-09-02 15:16:31,977 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:31,977 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:31,977 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:16:31,977 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:16:31,977 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:31,977 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:31,980 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:16:31,980 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:16:31,980 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:16:31,980 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:16:31,980 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:31,980 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:31,981 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:31,981 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:32,178 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:32,178 DEBUG: Start: Training
+2016-09-02 15:16:32,182 DEBUG: Info: Time for Training: 0.20650601387[s]
+2016-09-02 15:16:32,182 DEBUG: Done: Training
+2016-09-02 15:16:32,182 DEBUG: Start: Predicting
+2016-09-02 15:16:32,308 DEBUG: Done: Predicting
+2016-09-02 15:16:32,308 DEBUG: Start: Getting Results
+2016-09-02 15:16:32,309 DEBUG: Done: Getting Results
+2016-09-02 15:16:32,309 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.847107438017
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.847107438017
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:32,309 INFO: Done: Result Analysis
+2016-09-02 15:16:32,603 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:32,603 DEBUG: Start: Training
+2016-09-02 15:16:32,682 DEBUG: Info: Time for Training: 0.706362962723[s]
+2016-09-02 15:16:32,682 DEBUG: Done: Training
+2016-09-02 15:16:32,682 DEBUG: Start: Predicting
+2016-09-02 15:16:32,690 DEBUG: Done: Predicting
+2016-09-02 15:16:32,690 DEBUG: Start: Getting Results
+2016-09-02 15:16:32,691 DEBUG: Done: Getting Results
+2016-09-02 15:16:32,691 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 0.962809917355
+accuracy_score on test : 0.904761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 27, 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 : 0.962809917355
+ - Score on test : 0.904761904762
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:32,691 INFO: Done: Result Analysis
+2016-09-02 15:16:32,837 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:32,837 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:32,838 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:16:32,838 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:16:32,838 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:32,838 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:32,841 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:16:32,841 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:16:32,842 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:16:32,842 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:16:32,842 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:32,842 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:32,842 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:32,842 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:33,143 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:33,143 DEBUG: Start: Training
+2016-09-02 15:16:33,158 DEBUG: Info: Time for Training: 0.324208974838[s]
+2016-09-02 15:16:33,158 DEBUG: Done: Training
+2016-09-02 15:16:33,158 DEBUG: Start: Predicting
+2016-09-02 15:16:33,163 DEBUG: Done: Predicting
+2016-09-02 15:16:33,163 DEBUG: Start: Getting Results
+2016-09-02 15:16:33,164 DEBUG: Done: Getting Results
+2016-09-02 15:16:33,164 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.814049586777
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.814049586777
+ - Score on test : 0.742857142857
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:33,165 INFO: Done: Result Analysis
+2016-09-02 15:16:33,344 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:33,345 DEBUG: Start: Training
+2016-09-02 15:16:33,592 DEBUG: Info: Time for Training: 0.757379055023[s]
+2016-09-02 15:16:33,592 DEBUG: Done: Training
+2016-09-02 15:16:33,592 DEBUG: Start: Predicting
+2016-09-02 15:16:33,632 DEBUG: Done: Predicting
+2016-09-02 15:16:33,632 DEBUG: Start: Getting Results
+2016-09-02 15:16:33,633 DEBUG: Done: Getting Results
+2016-09-02 15:16:33,633 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.793388429752
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.793388429752
+ - Score on test : 0.761904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:33,634 INFO: Done: Result Analysis
+2016-09-02 15:16:33,783 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:33,784 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:16:33,784 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:33,784 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:33,785 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:16:33,785 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:33,785 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:16:33,786 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:16:33,786 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:33,786 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:33,787 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:16:33,787 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:16:33,788 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:33,788 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:33,920 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:33,920 DEBUG: Start: Training
+2016-09-02 15:16:33,927 DEBUG: Info: Time for Training: 0.144746780396[s]
+2016-09-02 15:16:33,927 DEBUG: Done: Training
+2016-09-02 15:16:33,927 DEBUG: Start: Predicting
+2016-09-02 15:16:33,931 DEBUG: Done: Predicting
+2016-09-02 15:16:33,931 DEBUG: Start: Getting Results
+2016-09-02 15:16:33,934 DEBUG: Done: Getting Results
+2016-09-02 15:16:33,934 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:33,934 INFO: Done: Result Analysis
+2016-09-02 15:16:34,468 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:34,468 DEBUG: Start: Training
+2016-09-02 15:16:34,779 DEBUG: Info: Time for Training: 0.996672868729[s]
+2016-09-02 15:16:34,779 DEBUG: Done: Training
+2016-09-02 15:16:34,779 DEBUG: Start: Predicting
+2016-09-02 15:16:34,869 DEBUG: Done: Predicting
+2016-09-02 15:16:34,869 DEBUG: Start: Getting Results
+2016-09-02 15:16:34,870 DEBUG: Done: Getting Results
+2016-09-02 15:16:34,870 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:34,870 INFO: Done: Result Analysis
+2016-09-02 15:16:34,937 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:34,937 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:34,938 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:16:34,938 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:16:34,938 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:34,938 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:34,939 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:16:34,939 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:16:34,939 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:16:34,939 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:16:34,939 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:34,939 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:34,939 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:34,939 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:35,031 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:35,031 DEBUG: Start: Training
+2016-09-02 15:16:35,034 DEBUG: Info: Time for Training: 0.0979828834534[s]
+2016-09-02 15:16:35,035 DEBUG: Done: Training
+2016-09-02 15:16:35,035 DEBUG: Start: Predicting
+2016-09-02 15:16:35,036 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:35,036 DEBUG: Start: Training
+2016-09-02 15:16:35,037 DEBUG: Info: Time for Training: 0.100709915161[s]
+2016-09-02 15:16:35,037 DEBUG: Done: Training
+2016-09-02 15:16:35,038 DEBUG: Start: Predicting
+2016-09-02 15:16:35,038 DEBUG: Done: Predicting
+2016-09-02 15:16:35,038 DEBUG: Start: Getting Results
+2016-09-02 15:16:35,040 DEBUG: Done: Getting Results
+2016-09-02 15:16:35,040 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:35,040 INFO: Done: Result Analysis
+2016-09-02 15:16:35,053 DEBUG: Done: Predicting
+2016-09-02 15:16:35,054 DEBUG: Start: Getting Results
+2016-09-02 15:16:35,054 DEBUG: Done: Getting Results
+2016-09-02 15:16:35,055 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.714876033058
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.714876033058
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:35,055 INFO: Done: Result Analysis
+2016-09-02 15:16:35,181 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:35,181 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:35,181 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:16:35,181 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:16:35,181 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:35,181 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:35,183 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:16:35,183 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:16:35,183 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:16:35,183 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:16:35,183 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:35,183 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:35,183 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:35,183 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:35,278 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:35,279 DEBUG: Start: Training
+2016-09-02 15:16:35,280 DEBUG: Info: Time for Training: 0.0997450351715[s]
+2016-09-02 15:16:35,280 DEBUG: Done: Training
+2016-09-02 15:16:35,280 DEBUG: Start: Predicting
+2016-09-02 15:16:35,292 DEBUG: Done: Predicting
+2016-09-02 15:16:35,293 DEBUG: Start: Getting Results
+2016-09-02 15:16:35,294 DEBUG: Done: Getting Results
+2016-09-02 15:16:35,294 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.628099173554
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.628099173554
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:35,294 INFO: Done: Result Analysis
+2016-09-02 15:16:35,770 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:35,770 DEBUG: Start: Training
+2016-09-02 15:16:35,834 DEBUG: Info: Time for Training: 0.653914928436[s]
+2016-09-02 15:16:35,834 DEBUG: Done: Training
+2016-09-02 15:16:35,834 DEBUG: Start: Predicting
+2016-09-02 15:16:35,841 DEBUG: Done: Predicting
+2016-09-02 15:16:35,841 DEBUG: Start: Getting Results
+2016-09-02 15:16:35,842 DEBUG: Done: Getting Results
+2016-09-02 15:16:35,842 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.752066115702
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 27, 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 : 0.752066115702
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:35,843 INFO: Done: Result Analysis
+2016-09-02 15:16:35,935 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:35,935 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:35,935 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:16:35,935 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:16:35,935 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:35,935 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:35,936 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:16:35,936 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:16:35,936 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:16:35,936 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:16:35,936 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:35,936 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:35,936 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:35,937 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:36,058 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:36,058 DEBUG: Start: Training
+2016-09-02 15:16:36,103 DEBUG: Info: Time for Training: 0.169160842896[s]
+2016-09-02 15:16:36,103 DEBUG: Done: Training
+2016-09-02 15:16:36,103 DEBUG: Start: Predicting
+2016-09-02 15:16:36,112 DEBUG: Done: Predicting
+2016-09-02 15:16:36,113 DEBUG: Start: Getting Results
+2016-09-02 15:16:36,113 DEBUG: Done: Getting Results
+2016-09-02 15:16:36,114 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.611570247934
+accuracy_score on test : 0.638095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.611570247934
+ - Score on test : 0.638095238095
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:36,114 INFO: Done: Result Analysis
+2016-09-02 15:16:36,143 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:36,143 DEBUG: Start: Training
+2016-09-02 15:16:36,218 DEBUG: Info: Time for Training: 0.284070014954[s]
+2016-09-02 15:16:36,218 DEBUG: Done: Training
+2016-09-02 15:16:36,218 DEBUG: Start: Predicting
+2016-09-02 15:16:36,236 DEBUG: Done: Predicting
+2016-09-02 15:16:36,236 DEBUG: Start: Getting Results
+2016-09-02 15:16:36,237 DEBUG: Done: Getting Results
+2016-09-02 15:16:36,237 INFO: Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:16:36,251 INFO: Done: Result Analysis
+2016-09-02 15:16:36,525 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:36,525 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:16:36,525 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:36,557 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:16:36,557 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:16:36,558 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:36,558 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:36,924 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:36,924 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:16:36,925 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:36,969 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:16:36,970 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:16:36,970 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:36,970 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:53,670 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:53,670 DEBUG: Start: Training
+2016-09-02 15:16:54,178 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:54,178 DEBUG: Start: Training
+2016-09-02 15:16:55,906 DEBUG: Info: Time for Training: 19.523277998[s]
+2016-09-02 15:16:55,907 DEBUG: Done: Training
+2016-09-02 15:16:55,907 DEBUG: Start: Predicting
+2016-09-02 15:16:55,915 DEBUG: Done: Predicting
+2016-09-02 15:16:55,915 DEBUG: Start: Getting Results
+2016-09-02 15:16:55,916 DEBUG: Done: Getting Results
+2016-09-02 15:16:55,916 INFO: Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 14
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.838095238095
+
+
+ Classification took 0:00:19
+2016-09-02 15:16:55,916 INFO: Done: Result Analysis
+2016-09-02 15:16:56,502 DEBUG: Info: Time for Training: 20.1187551022[s]
+2016-09-02 15:16:56,502 DEBUG: Done: Training
+2016-09-02 15:16:56,502 DEBUG: Start: Predicting
+2016-09-02 15:16:56,517 DEBUG: Done: Predicting
+2016-09-02 15:16:56,517 DEBUG: Start: Getting Results
+2016-09-02 15:16:56,519 DEBUG: Done: Getting Results
+2016-09-02 15:16:56,519 INFO: Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.838095238095
+
+
+ Classification took 0:00:20
+2016-09-02 15:16:56,519 INFO: Done: Result Analysis
+2016-09-02 15:16:56,726 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:56,727 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:16:56,727 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:56,728 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:16:56,729 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:16:56,729 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:16:56,756 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:16:56,756 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:16:56,756 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:56,756 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:56,769 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:16:56,769 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:16:56,769 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:16:56,769 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:16:58,897 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:16:58,897 DEBUG: Start: Training
+2016-09-02 15:16:59,165 DEBUG: Info: Time for Training: 2.46919798851[s]
+2016-09-02 15:16:59,165 DEBUG: Done: Training
+2016-09-02 15:16:59,165 DEBUG: Start: Predicting
+2016-09-02 15:16:59,181 DEBUG: Done: Predicting
+2016-09-02 15:16:59,181 DEBUG: Start: Getting Results
+2016-09-02 15:16:59,183 DEBUG: Done: Getting Results
+2016-09-02 15:16:59,183 INFO: Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.979338842975
+accuracy_score on test : 0.914285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 27, 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 : 0.979338842975
+ - Score on test : 0.914285714286
+
+
+ Classification took 0:00:02
+2016-09-02 15:16:59,183 INFO: Done: Result Analysis
+2016-09-02 15:17:02,613 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:17:02,614 DEBUG: Start: Training
+2016-09-02 15:17:02,788 DEBUG: Info: Time for Training: 6.09215688705[s]
+2016-09-02 15:17:02,788 DEBUG: Done: Training
+2016-09-02 15:17:02,788 DEBUG: Start: Predicting
+2016-09-02 15:17:07,701 DEBUG: Done: Predicting
+2016-09-02 15:17:07,702 DEBUG: Start: Getting Results
+2016-09-02 15:17:07,702 DEBUG: Done: Getting Results
+2016-09-02 15:17:07,703 INFO: Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.871900826446
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.871900826446
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:06
+2016-09-02 15:17:07,703 INFO: Done: Result Analysis
+2016-09-02 15:17:07,800 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:17:07,800 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:17:07,801 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:17:07,801 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:17:07,802 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:17:07,802 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:17:07,826 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:17:07,826 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:17:07,826 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:17:07,826 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:17:07,828 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:17:07,828 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:17:07,828 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:17:07,828 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:17:10,171 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:17:10,171 DEBUG: Start: Training
+2016-09-02 15:17:10,314 DEBUG: Info: Time for Training: 2.53464913368[s]
+2016-09-02 15:17:10,314 DEBUG: Done: Training
+2016-09-02 15:17:10,314 DEBUG: Start: Predicting
+2016-09-02 15:17:10,375 DEBUG: Done: Predicting
+2016-09-02 15:17:10,375 DEBUG: Start: Getting Results
+2016-09-02 15:17:10,376 DEBUG: Done: Getting Results
+2016-09-02 15:17:10,377 INFO: Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.892561983471
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.892561983471
+ - Score on test : 0.857142857143
+
+
+ Classification took 0:00:02
+2016-09-02 15:17:10,377 INFO: Done: Result Analysis
+2016-09-02 15:17:21,179 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:17:21,179 DEBUG: Start: Training
+2016-09-02 15:17:26,882 DEBUG: Info: Time for Training: 19.1025550365[s]
+2016-09-02 15:17:26,882 DEBUG: Done: Training
+2016-09-02 15:17:26,882 DEBUG: Start: Predicting
+2016-09-02 15:17:28,080 DEBUG: Done: Predicting
+2016-09-02 15:17:28,080 DEBUG: Start: Getting Results
+2016-09-02 15:17:28,081 DEBUG: Done: Getting Results
+2016-09-02 15:17:28,081 INFO: Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.904761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.904761904762
+
+
+ Classification took 0:00:19
+2016-09-02 15:17:28,081 INFO: Done: Result Analysis
+2016-09-02 15:17:28,196 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:17:28,196 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:17:28,196 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:17:28,220 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:17:28,221 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:17:28,221 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:17:28,221 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:17:29,065 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:17:29,065 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:17:29,065 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:17:29,149 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:17:29,149 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:17:29,149 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:17:29,149 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:17:40,316 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:17:40,316 DEBUG: Start: Training
+2016-09-02 15:17:46,451 DEBUG: Info: Time for Training: 18.2710399628[s]
+2016-09-02 15:17:46,451 DEBUG: Done: Training
+2016-09-02 15:17:46,451 DEBUG: Start: Predicting
+2016-09-02 15:17:47,809 DEBUG: Done: Predicting
+2016-09-02 15:17:47,809 DEBUG: Start: Getting Results
+2016-09-02 15:17:47,810 DEBUG: Done: Getting Results
+2016-09-02 15:17:47,810 INFO: Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.904761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.904761904762
+
+
+ Classification took 0:00:18
+2016-09-02 15:17:47,810 INFO: Done: Result Analysis
+2016-09-02 15:18:39,224 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:18:39,224 DEBUG: Start: Training
+2016-09-02 15:18:49,704 DEBUG: Info: Time for Training: 81.5240149498[s]
+2016-09-02 15:18:49,704 DEBUG: Done: Training
+2016-09-02 15:18:49,704 DEBUG: Start: Predicting
+2016-09-02 15:18:49,737 DEBUG: Done: Predicting
+2016-09-02 15:18:49,737 DEBUG: Start: Getting Results
+2016-09-02 15:18:49,739 DEBUG: Done: Getting Results
+2016-09-02 15:18:49,739 INFO: Classification on MultiOmic database for RANSeq with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.6
+
+
+ Classification took 0:01:21
+2016-09-02 15:18:49,739 INFO: Done: Result Analysis
+2016-09-02 15:18:51,751 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:18:51,751 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:18:51,752 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:18:51,820 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:18:51,820 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:18:51,820 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:18:51,820 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:18:52,079 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:18:52,080 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:18:52,080 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:18:52,173 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:18:52,173 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:18:52,173 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:18:52,174 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:19:08,543 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:19:08,595 DEBUG: Start: Training
+2016-09-02 15:19:09,180 DEBUG: Info: Time for Training: 19.330188036[s]
+2016-09-02 15:19:09,181 DEBUG: Done: Training
+2016-09-02 15:19:09,181 DEBUG: Start: Predicting
+2016-09-02 15:19:22,829 DEBUG: Done: Predicting
+2016-09-02 15:19:22,829 DEBUG: Start: Getting Results
+2016-09-02 15:19:22,839 DEBUG: Done: Getting Results
+2016-09-02 15:19:22,840 INFO: Classification on MultiOmic database for RANSeq with KNN
+
+accuracy_score on train : 0.735537190083
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.735537190083
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:19
+2016-09-02 15:19:22,840 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151631Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151631Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..eca2821928de7cc4ee3b64b95e463d3efb332b99
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151631Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.866666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151631Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151631Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3deed2fdfcba1b9acaeaf9713d22ca7952c9b25b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151631Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.866666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151632Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151632Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a9f2f015717b069320c263b9bd610321d2aba9a2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151632Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.847107438017
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.847107438017
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151632Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151632Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..efacd12edd90bd819260cd022516a146f553c80f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151632Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 0.962809917355
+accuracy_score on test : 0.904761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 27, 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 : 0.962809917355
+ - Score on test : 0.904761904762
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151633Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151633Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b1c3aeb991c9efdfa6bb3f669f4f9a76ef817b73
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151633Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151633Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151633Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cf9b12cf3c99f09d5d3265dfc4b523700bc7e7a2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151633Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.814049586777
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.814049586777
+ - Score on test : 0.742857142857
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151633Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151633Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8ac47fc68fe4948337a4d025665b3c80ffcbf2b1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151633Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.793388429752
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.793388429752
+ - Score on test : 0.761904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151634Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151634Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dd19d0073392560ab1b4bce8ef00c6677caf18a8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151634Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19726167bdc2997d1d6b9c2cd6de09b55b05b2b1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..abbf3c1913323adc148f8a394b2b3de25c13a863
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.714876033058
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.714876033058
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7315eebb810674293716119be8bd11c84a382340
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.752066115702
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 27, 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 : 0.752066115702
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ab17e44f3e37b6a44855e26bcafeb41273217184
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151635Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.628099173554
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.628099173554
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151636Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151636Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..11f2740f16f8dd702a481137a8b135aa4429e5f3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151636Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.611570247934
+accuracy_score on test : 0.638095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.611570247934
+ - Score on test : 0.638095238095
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151636Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151636Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7034eddf70acae5f2528142093195d94fe570709
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151636Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151655Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151655Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b2be6e88cdafdd3f3780ab37f892820906360c9e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151655Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 14
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.838095238095
+
+
+ Classification took 0:00:19
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151656Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151656Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..67a43d444841634ff01b642fd5961a4f9fd174a3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151656Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.838095238095
+
+
+ Classification took 0:00:20
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151659Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151659Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..19259acc1e97fca6d7bd5eb082af7d97fb102677
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151659Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.979338842975
+accuracy_score on test : 0.914285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 27, 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 : 0.979338842975
+ - Score on test : 0.914285714286
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151707Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151707Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fa3139841d2f921100a6ea0eb502fa6f290d5017
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151707Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.871900826446
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.871900826446
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:06
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151710Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151710Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..26bae83e6ba7f329191b6d79d21cd08fca62fc84
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151710Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.892561983471
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.892561983471
+ - Score on test : 0.857142857143
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151728Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151728Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..05f57d8db0f00df789a28c1d1e018ed4a1990c3c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151728Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.904761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.904761904762
+
+
+ Classification took 0:00:19
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151747Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151747Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a3f79463c08bc6376b8541975579983220d0bd65
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151747Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.904761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2310
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.904761904762
+
+
+ Classification took 0:00:18
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151849Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151849Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..101e679fc26ffcbdd96d8c9a4d8a5c367f386b50
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151849Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for RANSeq with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.6
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.6
+
+
+ Classification took 0:01:21
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-151922Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-151922Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a57539c14bcb962eb6699863cb77e05b8a3c56f4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-151922Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with KNN
+
+accuracy_score on train : 0.735537190083
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.735537190083
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:19
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-152015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..6502241d814dee39678b476ddeb0c2abd471a520
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152015-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,860 @@
+2016-09-02 15:20:15,203 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:20:15,203 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:20:18,982 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:20:18,984 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:20:20,039 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:20,039 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:20,039 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:20:20,039 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:20:20,040 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:20,040 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:20,042 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:20:20,042 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:20:20,043 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:20:20,043 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:20:20,043 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:20,043 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:20,043 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:20,043 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:20,542 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:20,542 DEBUG: Start: Training
+2016-09-02 15:20:20,566 DEBUG: Info: Time for Training: 0.528756141663[s]
+2016-09-02 15:20:20,566 DEBUG: Done: Training
+2016-09-02 15:20:20,566 DEBUG: Start: Predicting
+2016-09-02 15:20:20,569 DEBUG: Done: Predicting
+2016-09-02 15:20:20,569 DEBUG: Start: Getting Results
+2016-09-02 15:20:20,570 DEBUG: Done: Getting Results
+2016-09-02 15:20:20,570 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 0.913223140496
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.913223140496
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:20,571 INFO: Done: Result Analysis
+2016-09-02 15:20:20,701 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:20,701 DEBUG: Start: Training
+2016-09-02 15:20:20,758 DEBUG: Info: Time for Training: 0.720571994781[s]
+2016-09-02 15:20:20,758 DEBUG: Done: Training
+2016-09-02 15:20:20,758 DEBUG: Start: Predicting
+2016-09-02 15:20:20,761 DEBUG: Done: Predicting
+2016-09-02 15:20:20,761 DEBUG: Start: Getting Results
+2016-09-02 15:20:20,763 DEBUG: Done: Getting Results
+2016-09-02 15:20:20,763 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.828571428571
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.828571428571
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:20,763 INFO: Done: Result Analysis
+2016-09-02 15:20:20,883 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:20,883 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:20,883 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:20:20,883 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:20:20,883 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:20,883 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:20,885 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:20:20,885 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:20:20,885 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:20:20,885 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:20:20,885 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:20,885 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:20,885 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:20,885 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:21,087 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:21,087 DEBUG: Start: Training
+2016-09-02 15:20:21,091 DEBUG: Info: Time for Training: 0.209568977356[s]
+2016-09-02 15:20:21,091 DEBUG: Done: Training
+2016-09-02 15:20:21,091 DEBUG: Start: Predicting
+2016-09-02 15:20:21,182 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:21,183 DEBUG: Start: Training
+2016-09-02 15:20:21,201 DEBUG: Info: Time for Training: 0.319441080093[s]
+2016-09-02 15:20:21,201 DEBUG: Done: Training
+2016-09-02 15:20:21,201 DEBUG: Start: Predicting
+2016-09-02 15:20:21,205 DEBUG: Done: Predicting
+2016-09-02 15:20:21,205 DEBUG: Start: Getting Results
+2016-09-02 15:20:21,206 DEBUG: Done: Getting Results
+2016-09-02 15:20:21,206 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 0.971074380165
+accuracy_score on test : 0.87619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 5, max_depth : 12
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.971074380165
+ - Score on test : 0.87619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:21,207 INFO: Done: Result Analysis
+2016-09-02 15:20:21,222 DEBUG: Done: Predicting
+2016-09-02 15:20:21,222 DEBUG: Start: Getting Results
+2016-09-02 15:20:21,223 DEBUG: Done: Getting Results
+2016-09-02 15:20:21,223 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.863636363636
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.863636363636
+ - Score on test : 0.761904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:21,223 INFO: Done: Result Analysis
+2016-09-02 15:20:21,335 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:21,335 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:21,336 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:20:21,336 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:20:21,336 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:21,336 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:21,339 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:20:21,339 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:20:21,339 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:20:21,339 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:20:21,339 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:21,339 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:21,339 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:21,340 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:21,743 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:21,744 DEBUG: Start: Training
+2016-09-02 15:20:21,785 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:21,785 DEBUG: Start: Training
+2016-09-02 15:20:21,802 DEBUG: Info: Time for Training: 0.467435121536[s]
+2016-09-02 15:20:21,802 DEBUG: Done: Training
+2016-09-02 15:20:21,802 DEBUG: Start: Predicting
+2016-09-02 15:20:21,807 DEBUG: Done: Predicting
+2016-09-02 15:20:21,807 DEBUG: Start: Getting Results
+2016-09-02 15:20:21,808 DEBUG: Done: Getting Results
+2016-09-02 15:20:21,808 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.789256198347
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.789256198347
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:21,809 INFO: Done: Result Analysis
+2016-09-02 15:20:21,923 DEBUG: Info: Time for Training: 0.589110851288[s]
+2016-09-02 15:20:21,924 DEBUG: Done: Training
+2016-09-02 15:20:21,924 DEBUG: Start: Predicting
+2016-09-02 15:20:21,970 DEBUG: Done: Predicting
+2016-09-02 15:20:21,971 DEBUG: Start: Getting Results
+2016-09-02 15:20:21,972 DEBUG: Done: Getting Results
+2016-09-02 15:20:21,972 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4101
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:21,972 INFO: Done: Result Analysis
+2016-09-02 15:20:22,088 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:22,088 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:22,088 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:20:22,088 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:20:22,088 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:22,088 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:22,091 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:20:22,091 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:20:22,091 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:20:22,091 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:20:22,091 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:22,091 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:22,092 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:22,092 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:22,207 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:22,208 DEBUG: Start: Training
+2016-09-02 15:20:22,217 DEBUG: Info: Time for Training: 0.130825042725[s]
+2016-09-02 15:20:22,217 DEBUG: Done: Training
+2016-09-02 15:20:22,217 DEBUG: Start: Predicting
+2016-09-02 15:20:22,223 DEBUG: Done: Predicting
+2016-09-02 15:20:22,223 DEBUG: Start: Getting Results
+2016-09-02 15:20:22,225 DEBUG: Done: Getting Results
+2016-09-02 15:20:22,225 INFO: Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.260330578512
+accuracy_score on test : 0.285714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4101
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.260330578512
+ - Score on test : 0.285714285714
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:22,225 INFO: Done: Result Analysis
+2016-09-02 15:20:22,767 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:22,767 DEBUG: Start: Training
+2016-09-02 15:20:23,080 DEBUG: Info: Time for Training: 0.993525028229[s]
+2016-09-02 15:20:23,080 DEBUG: Done: Training
+2016-09-02 15:20:23,080 DEBUG: Start: Predicting
+2016-09-02 15:20:23,170 DEBUG: Done: Predicting
+2016-09-02 15:20:23,170 DEBUG: Start: Getting Results
+2016-09-02 15:20:23,171 DEBUG: Done: Getting Results
+2016-09-02 15:20:23,171 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.714285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4101
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.714285714286
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:23,172 INFO: Done: Result Analysis
+2016-09-02 15:20:23,240 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:23,240 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:23,241 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:20:23,241 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:20:23,241 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:23,241 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:23,242 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:20:23,242 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:20:23,242 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:20:23,242 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:20:23,242 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:23,242 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:23,242 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:23,242 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:23,300 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:23,300 DEBUG: Start: Training
+2016-09-02 15:20:23,302 DEBUG: Info: Time for Training: 0.06258893013[s]
+2016-09-02 15:20:23,302 DEBUG: Done: Training
+2016-09-02 15:20:23,302 DEBUG: Start: Predicting
+2016-09-02 15:20:23,305 DEBUG: Done: Predicting
+2016-09-02 15:20:23,305 DEBUG: Start: Getting Results
+2016-09-02 15:20:23,306 DEBUG: Done: Getting Results
+2016-09-02 15:20:23,306 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:23,306 INFO: Done: Result Analysis
+2016-09-02 15:20:23,333 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:23,334 DEBUG: Start: Training
+2016-09-02 15:20:23,338 DEBUG: Info: Time for Training: 0.0981249809265[s]
+2016-09-02 15:20:23,338 DEBUG: Done: Training
+2016-09-02 15:20:23,338 DEBUG: Start: Predicting
+2016-09-02 15:20:23,341 DEBUG: Done: Predicting
+2016-09-02 15:20:23,341 DEBUG: Start: Getting Results
+2016-09-02 15:20:23,342 DEBUG: Done: Getting Results
+2016-09-02 15:20:23,342 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.790476190476
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:23,343 INFO: Done: Result Analysis
+2016-09-02 15:20:23,488 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:23,488 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:23,489 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:20:23,489 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:20:23,489 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:23,489 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:23,490 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:20:23,490 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:20:23,490 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:20:23,490 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:20:23,490 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:23,490 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:23,490 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:23,490 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:23,563 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:23,563 DEBUG: Start: Training
+2016-09-02 15:20:23,565 DEBUG: Info: Time for Training: 0.0772609710693[s]
+2016-09-02 15:20:23,565 DEBUG: Done: Training
+2016-09-02 15:20:23,565 DEBUG: Start: Predicting
+2016-09-02 15:20:23,587 DEBUG: Done: Predicting
+2016-09-02 15:20:23,587 DEBUG: Start: Getting Results
+2016-09-02 15:20:23,588 DEBUG: Done: Getting Results
+2016-09-02 15:20:23,588 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.743801652893
+accuracy_score on test : 0.704761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.743801652893
+ - Score on test : 0.704761904762
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:23,589 INFO: Done: Result Analysis
+2016-09-02 15:20:23,744 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:23,745 DEBUG: Start: Training
+2016-09-02 15:20:23,758 DEBUG: Info: Time for Training: 0.270296812057[s]
+2016-09-02 15:20:23,758 DEBUG: Done: Training
+2016-09-02 15:20:23,758 DEBUG: Start: Predicting
+2016-09-02 15:20:23,762 DEBUG: Done: Predicting
+2016-09-02 15:20:23,762 DEBUG: Start: Getting Results
+2016-09-02 15:20:23,763 DEBUG: Done: Getting Results
+2016-09-02 15:20:23,763 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.971074380165
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 5, max_depth : 12
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.971074380165
+ - Score on test : 0.761904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:23,763 INFO: Done: Result Analysis
+2016-09-02 15:20:23,842 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:23,842 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:23,843 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:20:23,843 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:20:23,843 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:23,843 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:23,844 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:20:23,844 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:20:23,845 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:20:23,845 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:20:23,845 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:23,845 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:23,845 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:23,845 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:23,948 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:23,949 DEBUG: Start: Training
+2016-09-02 15:20:23,950 DEBUG: Info: Time for Training: 0.109503984451[s]
+2016-09-02 15:20:23,950 DEBUG: Done: Training
+2016-09-02 15:20:23,951 DEBUG: Start: Predicting
+2016-09-02 15:20:23,956 DEBUG: Done: Predicting
+2016-09-02 15:20:23,956 DEBUG: Start: Getting Results
+2016-09-02 15:20:23,958 DEBUG: Done: Getting Results
+2016-09-02 15:20:23,958 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.756198347107
+accuracy_score on test : 0.695238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.756198347107
+ - Score on test : 0.695238095238
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:23,958 INFO: Done: Result Analysis
+2016-09-02 15:20:23,977 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:23,978 DEBUG: Start: Training
+2016-09-02 15:20:24,025 DEBUG: Info: Time for Training: 0.183743000031[s]
+2016-09-02 15:20:24,025 DEBUG: Done: Training
+2016-09-02 15:20:24,025 DEBUG: Start: Predicting
+2016-09-02 15:20:24,035 DEBUG: Done: Predicting
+2016-09-02 15:20:24,035 DEBUG: Start: Getting Results
+2016-09-02 15:20:24,036 DEBUG: Done: Getting Results
+2016-09-02 15:20:24,036 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.50826446281
+accuracy_score on test : 0.542857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4101
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.50826446281
+ - Score on test : 0.542857142857
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:24,036 INFO: Done: Result Analysis
+2016-09-02 15:20:24,189 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:24,189 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:24,190 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:20:24,190 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:20:24,190 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:24,190 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:24,191 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:20:24,191 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:20:24,191 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:20:24,191 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:20:24,191 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:24,191 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:24,191 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:24,191 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:24,256 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:24,256 DEBUG: Start: Training
+2016-09-02 15:20:24,294 DEBUG: Info: Time for Training: 0.10511803627[s]
+2016-09-02 15:20:24,294 DEBUG: Done: Training
+2016-09-02 15:20:24,294 DEBUG: Start: Predicting
+2016-09-02 15:20:24,301 DEBUG: Done: Predicting
+2016-09-02 15:20:24,301 DEBUG: Start: Getting Results
+2016-09-02 15:20:24,302 DEBUG: Done: Getting Results
+2016-09-02 15:20:24,302 INFO: Classification on MultiOmic database for Clinic with SVMPoly
+
+accuracy_score on train : 0.45041322314
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9450
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.45041322314
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:24,302 INFO: Done: Result Analysis
+2016-09-02 15:20:24,378 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:24,378 DEBUG: Start: Training
+2016-09-02 15:20:24,452 DEBUG: Info: Time for Training: 0.263296842575[s]
+2016-09-02 15:20:24,452 DEBUG: Done: Training
+2016-09-02 15:20:24,452 DEBUG: Start: Predicting
+2016-09-02 15:20:24,470 DEBUG: Done: Predicting
+2016-09-02 15:20:24,470 DEBUG: Start: Getting Results
+2016-09-02 15:20:24,471 DEBUG: Done: Getting Results
+2016-09-02 15:20:24,471 INFO: Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.714285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4101
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.714285714286
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:24,471 INFO: Done: Result Analysis
+2016-09-02 15:20:24,550 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:24,550 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:20:24,550 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:24,551 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:24,551 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:20:24,551 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:24,571 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:20:24,571 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:20:24,571 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:24,571 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:24,575 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:20:24,575 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:20:24,575 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:24,575 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:37,121 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:37,121 DEBUG: Start: Training
+2016-09-02 15:20:38,221 DEBUG: Info: Time for Training: 13.6871359348[s]
+2016-09-02 15:20:38,221 DEBUG: Done: Training
+2016-09-02 15:20:38,221 DEBUG: Start: Predicting
+2016-09-02 15:20:38,230 DEBUG: Done: Predicting
+2016-09-02 15:20:38,230 DEBUG: Start: Getting Results
+2016-09-02 15:20:38,231 DEBUG: Done: Getting Results
+2016-09-02 15:20:38,231 INFO: Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 0.917355371901
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.917355371901
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:13
+2016-09-02 15:20:38,231 INFO: Done: Result Analysis
+2016-09-02 15:20:40,783 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:40,783 DEBUG: Start: Training
+2016-09-02 15:20:42,771 DEBUG: Info: Time for Training: 18.2373490334[s]
+2016-09-02 15:20:42,771 DEBUG: Done: Training
+2016-09-02 15:20:42,772 DEBUG: Start: Predicting
+2016-09-02 15:20:42,785 DEBUG: Done: Predicting
+2016-09-02 15:20:42,785 DEBUG: Start: Getting Results
+2016-09-02 15:20:42,787 DEBUG: Done: Getting Results
+2016-09-02 15:20:42,787 INFO: Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.838095238095
+
+
+ Classification took 0:00:18
+2016-09-02 15:20:42,787 INFO: Done: Result Analysis
+2016-09-02 15:20:42,943 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:42,943 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:42,943 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:20:42,943 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:20:42,943 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:42,943 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:42,962 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:20:42,962 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:20:42,962 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:20:42,962 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:20:42,962 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:42,962 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:42,962 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:42,962 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:43,773 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:43,773 DEBUG: Start: Training
+2016-09-02 15:20:43,847 DEBUG: Info: Time for Training: 0.918445825577[s]
+2016-09-02 15:20:43,847 DEBUG: Done: Training
+2016-09-02 15:20:43,847 DEBUG: Start: Predicting
+2016-09-02 15:20:43,858 DEBUG: Done: Predicting
+2016-09-02 15:20:43,858 DEBUG: Start: Getting Results
+2016-09-02 15:20:43,859 DEBUG: Done: Getting Results
+2016-09-02 15:20:43,859 INFO: Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.925619834711
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 10, 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 : 0.925619834711
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:20:43,859 INFO: Done: Result Analysis
+2016-09-02 15:20:47,996 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:47,996 DEBUG: Start: Training
+2016-09-02 15:20:48,152 DEBUG: Info: Time for Training: 5.22381496429[s]
+2016-09-02 15:20:48,152 DEBUG: Done: Training
+2016-09-02 15:20:48,152 DEBUG: Start: Predicting
+2016-09-02 15:20:52,273 DEBUG: Done: Predicting
+2016-09-02 15:20:52,273 DEBUG: Start: Getting Results
+2016-09-02 15:20:52,274 DEBUG: Done: Getting Results
+2016-09-02 15:20:52,274 INFO: Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.867768595041
+accuracy_score on test : 0.87619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.867768595041
+ - Score on test : 0.87619047619
+
+
+ Classification took 0:00:05
+2016-09-02 15:20:52,275 INFO: Done: Result Analysis
+2016-09-02 15:20:52,422 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:52,422 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:20:52,423 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:20:52,423 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:20:52,423 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:52,423 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:20:52,447 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:20:52,447 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:20:52,447 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:20:52,447 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:20:52,447 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:52,447 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:20:52,447 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:52,447 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:20:55,309 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:20:55,309 DEBUG: Start: Training
+2016-09-02 15:20:55,653 DEBUG: Info: Time for Training: 3.24854302406[s]
+2016-09-02 15:20:55,654 DEBUG: Done: Training
+2016-09-02 15:20:55,654 DEBUG: Start: Predicting
+2016-09-02 15:20:55,704 DEBUG: Done: Predicting
+2016-09-02 15:20:55,704 DEBUG: Start: Getting Results
+2016-09-02 15:20:55,705 DEBUG: Done: Getting Results
+2016-09-02 15:20:55,705 INFO: Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.900826446281
+accuracy_score on test : 0.933333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.900826446281
+ - Score on test : 0.933333333333
+
+
+ Classification took 0:00:03
+2016-09-02 15:20:55,706 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152020Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152020Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2dcc0b1929310623f42e591c08d07cd5c2892cc2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152020Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.828571428571
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.828571428571
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152020Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152020Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..143f352efa986413a7e0970dc9a53bcf64b66c5e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152020Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 0.913223140496
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.913223140496
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..517e3182e4c69a1245cee9ba9f9c95b34dc15b36
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.863636363636
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.863636363636
+ - Score on test : 0.761904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1686ec7a7d51b1fd9ab4d8d2560bd4c42f67bbcf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 0.971074380165
+accuracy_score on test : 0.87619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 5, max_depth : 12
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.971074380165
+ - Score on test : 0.87619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e98e767307d742ade574d02b233db423cdc2123b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.789256198347
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.789256198347
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..91f23667017c9e65e10eb9bb74887413c6913414
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152021Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.710743801653
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4101
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.710743801653
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152022Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152022Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ededdbe8c1d14c13c612b086ea6239648690a9ed
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152022Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.260330578512
+accuracy_score on test : 0.285714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4101
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.260330578512
+ - Score on test : 0.285714285714
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..400050fd0b6fcf2a82ba25ef12a41240fa5b4959
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.790476190476
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5e8bc40588c2c03fc1a79718d2372ae3ec826259
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b320dcb572af417155d1a377d8935231201929f9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.743801652893
+accuracy_score on test : 0.704761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.743801652893
+ - Score on test : 0.704761904762
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7f226622ee75221a35ccdc6bb83fc240eafb5685
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.971074380165
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 5, max_depth : 12
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.971074380165
+ - Score on test : 0.761904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3b4c5ef9ad8f4b5ab6dbfcf040491250b65d5d2c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.756198347107
+accuracy_score on test : 0.695238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.756198347107
+ - Score on test : 0.695238095238
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..240d0dead643970c113adf5fab08d4a15d1006f7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152023Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.714285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4101
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.714285714286
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152024Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152024Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6185425432053958a52bb4d516dd9c90e35fcb78
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152024Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.50826446281
+accuracy_score on test : 0.542857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4101
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.50826446281
+ - Score on test : 0.542857142857
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152024Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152024Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6da8fa2231865937b52870ff0170ccbc47dc668f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152024Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMPoly
+
+accuracy_score on train : 0.45041322314
+accuracy_score on test : 0.466666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9450
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.45041322314
+ - Score on test : 0.466666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152024Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152024Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b3d37d2dc457e96fa4375cca8f40e2059bbfc119
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152024Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.714285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4101
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.714285714286
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152038Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152038Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b4460ffdc3fe8208840344056b80d47240a3507a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152038Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 0.917355371901
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.917355371901
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:13
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152042Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152042Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ec90c864c55010f037360ca9d2d996e324e4f827
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152042Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.838095238095
+
+
+ Classification took 0:00:18
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152043Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152043Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..abe46bdc9de28abb0a68f2af7fd6fde273bb1572
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152043Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 0.925619834711
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 10, 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 : 0.925619834711
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152052Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152052Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..73417e19089ea669ca7d6206f1c2a9d9fed752b9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152052Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.867768595041
+accuracy_score on test : 0.87619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.867768595041
+ - Score on test : 0.87619047619
+
+
+ Classification took 0:00:05
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152055Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152055Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ebfd966ff0086b59b9337a2b1d5a535f7f21293f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152055Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.900826446281
+accuracy_score on test : 0.933333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.900826446281
+ - Score on test : 0.933333333333
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-152115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..ee881f31ed1aa95b9f341a8c6ab6606ecfcb6440
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152115-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,1216 @@
+2016-09-02 15:21:15,594 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:21:15,594 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:21:17,320 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:21:17,322 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:21:18,302 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:18,302 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:18,302 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:21:18,302 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:21:18,302 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:18,302 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:18,304 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:21:18,304 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:21:18,304 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:21:18,304 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:21:18,304 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:18,304 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:18,304 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:18,304 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:18,607 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:18,607 DEBUG: Start: Training
+2016-09-02 15:21:18,653 DEBUG: Info: Time for Training: 0.352904081345[s]
+2016-09-02 15:21:18,653 DEBUG: Done: Training
+2016-09-02 15:21:18,654 DEBUG: Start: Predicting
+2016-09-02 15:21:18,656 DEBUG: Done: Predicting
+2016-09-02 15:21:18,656 DEBUG: Start: Getting Results
+2016-09-02 15:21:18,657 DEBUG: Done: Getting Results
+2016-09-02 15:21:18,658 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.742857142857
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:18,658 INFO: Done: Result Analysis
+2016-09-02 15:21:18,760 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:18,761 DEBUG: Start: Training
+2016-09-02 15:21:18,803 DEBUG: Info: Time for Training: 0.502971172333[s]
+2016-09-02 15:21:18,804 DEBUG: Done: Training
+2016-09-02 15:21:18,804 DEBUG: Start: Predicting
+2016-09-02 15:21:18,807 DEBUG: Done: Predicting
+2016-09-02 15:21:18,807 DEBUG: Start: Getting Results
+2016-09-02 15:21:18,809 DEBUG: Done: Getting Results
+2016-09-02 15:21:18,809 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.771428571429
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:18,809 INFO: Done: Result Analysis
+2016-09-02 15:21:18,955 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:18,955 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:18,956 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:21:18,956 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:21:18,957 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:18,957 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:18,959 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:21:18,959 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:21:18,960 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:21:18,960 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:21:18,960 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:18,960 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:18,960 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:18,960 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:19,179 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:19,179 DEBUG: Start: Training
+2016-09-02 15:21:19,184 DEBUG: Info: Time for Training: 0.230554103851[s]
+2016-09-02 15:21:19,184 DEBUG: Done: Training
+2016-09-02 15:21:19,184 DEBUG: Start: Predicting
+2016-09-02 15:21:19,337 DEBUG: Done: Predicting
+2016-09-02 15:21:19,337 DEBUG: Start: Getting Results
+2016-09-02 15:21:19,338 DEBUG: Done: Getting Results
+2016-09-02 15:21:19,338 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.768595041322
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.768595041322
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:19,338 INFO: Done: Result Analysis
+2016-09-02 15:21:19,805 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:19,806 DEBUG: Start: Training
+2016-09-02 15:21:19,891 DEBUG: Info: Time for Training: 0.937329053879[s]
+2016-09-02 15:21:19,891 DEBUG: Done: Training
+2016-09-02 15:21:19,891 DEBUG: Start: Predicting
+2016-09-02 15:21:19,898 DEBUG: Done: Predicting
+2016-09-02 15:21:19,899 DEBUG: Start: Getting Results
+2016-09-02 15:21:19,899 DEBUG: Done: Getting Results
+2016-09-02 15:21:19,900 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 0.98347107438
+accuracy_score on test : 0.819047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.98347107438
+ - Score on test : 0.819047619048
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:19,900 INFO: Done: Result Analysis
+2016-09-02 15:21:20,013 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:20,013 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:20,013 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:21:20,014 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:21:20,014 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:20,014 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:20,016 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:21:20,016 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:21:20,017 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:21:20,017 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:21:20,017 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:20,017 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:20,017 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:20,017 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:20,199 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:20,199 DEBUG: Start: Training
+2016-09-02 15:21:20,214 DEBUG: Info: Time for Training: 0.202126979828[s]
+2016-09-02 15:21:20,214 DEBUG: Done: Training
+2016-09-02 15:21:20,214 DEBUG: Start: Predicting
+2016-09-02 15:21:20,217 DEBUG: Done: Predicting
+2016-09-02 15:21:20,218 DEBUG: Start: Getting Results
+2016-09-02 15:21:20,219 DEBUG: Done: Getting Results
+2016-09-02 15:21:20,219 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.830578512397
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.830578512397
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:20,219 INFO: Done: Result Analysis
+2016-09-02 15:21:20,463 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:20,463 DEBUG: Start: Training
+2016-09-02 15:21:20,612 DEBUG: Info: Time for Training: 0.601065158844[s]
+2016-09-02 15:21:20,612 DEBUG: Done: Training
+2016-09-02 15:21:20,613 DEBUG: Start: Predicting
+2016-09-02 15:21:20,671 DEBUG: Done: Predicting
+2016-09-02 15:21:20,671 DEBUG: Start: Getting Results
+2016-09-02 15:21:20,672 DEBUG: Done: Getting Results
+2016-09-02 15:21:20,673 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.681818181818
+accuracy_score on test : 0.657142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.681818181818
+ - Score on test : 0.657142857143
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:20,673 INFO: Done: Result Analysis
+2016-09-02 15:21:20,759 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:20,759 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:20,759 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:21:20,759 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:21:20,759 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:20,759 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:20,761 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:21:20,761 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:21:20,761 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:20,761 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:20,762 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:21:20,762 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:21:20,762 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:20,762 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:20,838 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:20,838 DEBUG: Start: Training
+2016-09-02 15:21:20,843 DEBUG: Info: Time for Training: 0.0858111381531[s]
+2016-09-02 15:21:20,843 DEBUG: Done: Training
+2016-09-02 15:21:20,843 DEBUG: Start: Predicting
+2016-09-02 15:21:20,847 DEBUG: Done: Predicting
+2016-09-02 15:21:20,847 DEBUG: Start: Getting Results
+2016-09-02 15:21:20,848 DEBUG: Done: Getting Results
+2016-09-02 15:21:20,849 INFO: Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.239669421488
+accuracy_score on test : 0.333333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.239669421488
+ - Score on test : 0.333333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:20,849 INFO: Done: Result Analysis
+2016-09-02 15:21:21,423 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:21,423 DEBUG: Start: Training
+2016-09-02 15:21:21,751 DEBUG: Info: Time for Training: 0.993693113327[s]
+2016-09-02 15:21:21,751 DEBUG: Done: Training
+2016-09-02 15:21:21,752 DEBUG: Start: Predicting
+2016-09-02 15:21:21,845 DEBUG: Done: Predicting
+2016-09-02 15:21:21,845 DEBUG: Start: Getting Results
+2016-09-02 15:21:21,846 DEBUG: Done: Getting Results
+2016-09-02 15:21:21,846 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:21,846 INFO: Done: Result Analysis
+2016-09-02 15:21:21,914 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:21,914 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:21,914 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:21:21,914 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:21:21,914 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:21,914 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:21,916 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:21:21,916 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:21:21,916 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:21:21,916 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:21,916 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:21:21,916 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:21,916 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:21,916 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:21,999 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:21,999 DEBUG: Start: Training
+2016-09-02 15:21:22,003 DEBUG: Info: Time for Training: 0.089987039566[s]
+2016-09-02 15:21:22,003 DEBUG: Done: Training
+2016-09-02 15:21:22,003 DEBUG: Start: Predicting
+2016-09-02 15:21:22,007 DEBUG: Done: Predicting
+2016-09-02 15:21:22,007 DEBUG: Start: Getting Results
+2016-09-02 15:21:22,009 DEBUG: Done: Getting Results
+2016-09-02 15:21:22,009 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.742857142857
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:22,009 INFO: Done: Result Analysis
+2016-09-02 15:21:22,051 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:22,051 DEBUG: Start: Training
+2016-09-02 15:21:22,059 DEBUG: Info: Time for Training: 0.145761013031[s]
+2016-09-02 15:21:22,059 DEBUG: Done: Training
+2016-09-02 15:21:22,059 DEBUG: Start: Predicting
+2016-09-02 15:21:22,064 DEBUG: Done: Predicting
+2016-09-02 15:21:22,064 DEBUG: Start: Getting Results
+2016-09-02 15:21:22,067 DEBUG: Done: Getting Results
+2016-09-02 15:21:22,067 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.742857142857
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:22,067 INFO: Done: Result Analysis
+2016-09-02 15:21:22,171 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:22,171 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:22,172 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:21:22,172 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:21:22,172 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:22,172 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:22,173 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:21:22,173 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:21:22,173 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:21:22,173 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:21:22,173 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:22,173 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:22,174 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:22,174 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:22,298 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:22,298 DEBUG: Start: Training
+2016-09-02 15:21:22,299 DEBUG: Info: Time for Training: 0.128530025482[s]
+2016-09-02 15:21:22,299 DEBUG: Done: Training
+2016-09-02 15:21:22,299 DEBUG: Start: Predicting
+2016-09-02 15:21:22,323 DEBUG: Done: Predicting
+2016-09-02 15:21:22,323 DEBUG: Start: Getting Results
+2016-09-02 15:21:22,324 DEBUG: Done: Getting Results
+2016-09-02 15:21:22,324 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.760330578512
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:22,324 INFO: Done: Result Analysis
+2016-09-02 15:21:23,020 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:23,020 DEBUG: Start: Training
+2016-09-02 15:21:23,072 DEBUG: Info: Time for Training: 0.901878118515[s]
+2016-09-02 15:21:23,073 DEBUG: Done: Training
+2016-09-02 15:21:23,073 DEBUG: Start: Predicting
+2016-09-02 15:21:23,079 DEBUG: Done: Predicting
+2016-09-02 15:21:23,080 DEBUG: Start: Getting Results
+2016-09-02 15:21:23,081 DEBUG: Done: Getting Results
+2016-09-02 15:21:23,081 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, 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 : 0.733333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:23,081 INFO: Done: Result Analysis
+2016-09-02 15:21:23,226 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:23,226 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:23,226 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:21:23,226 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:21:23,226 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:23,226 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:23,227 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:21:23,227 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:21:23,227 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:21:23,227 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:21:23,227 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:23,227 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:23,227 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:23,227 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:23,327 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:23,327 DEBUG: Start: Training
+2016-09-02 15:21:23,329 DEBUG: Info: Time for Training: 0.103621006012[s]
+2016-09-02 15:21:23,329 DEBUG: Done: Training
+2016-09-02 15:21:23,329 DEBUG: Start: Predicting
+2016-09-02 15:21:23,344 DEBUG: Done: Predicting
+2016-09-02 15:21:23,344 DEBUG: Start: Getting Results
+2016-09-02 15:21:23,346 DEBUG: Done: Getting Results
+2016-09-02 15:21:23,346 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.603305785124
+accuracy_score on test : 0.628571428571
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.603305785124
+ - Score on test : 0.628571428571
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:23,346 INFO: Done: Result Analysis
+2016-09-02 15:21:23,350 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:23,350 DEBUG: Start: Training
+2016-09-02 15:21:23,398 DEBUG: Info: Time for Training: 0.172649145126[s]
+2016-09-02 15:21:23,398 DEBUG: Done: Training
+2016-09-02 15:21:23,398 DEBUG: Start: Predicting
+2016-09-02 15:21:23,408 DEBUG: Done: Predicting
+2016-09-02 15:21:23,408 DEBUG: Start: Getting Results
+2016-09-02 15:21:23,409 DEBUG: Done: Getting Results
+2016-09-02 15:21:23,409 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.599173553719
+accuracy_score on test : 0.580952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.599173553719
+ - Score on test : 0.580952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:23,409 INFO: Done: Result Analysis
+2016-09-02 15:21:23,472 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:23,472 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:23,472 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:21:23,472 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:21:23,472 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:23,472 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:23,473 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:21:23,473 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:21:23,473 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:21:23,473 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:21:23,473 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:23,473 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:23,473 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:23,473 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:23,534 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:23,534 DEBUG: Start: Training
+2016-09-02 15:21:23,535 DEBUG: Info: Time for Training: 0.0641498565674[s]
+2016-09-02 15:21:23,535 DEBUG: Done: Training
+2016-09-02 15:21:23,536 DEBUG: Start: Predicting
+2016-09-02 15:21:23,538 DEBUG: Done: Predicting
+2016-09-02 15:21:23,539 DEBUG: Start: Getting Results
+2016-09-02 15:21:23,540 DEBUG: Done: Getting Results
+2016-09-02 15:21:23,540 INFO: Classification on MultiOmic database for Clinic with SVMPoly
+
+accuracy_score on train : 0.239669421488
+accuracy_score on test : 0.333333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.239669421488
+ - Score on test : 0.333333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:23,540 INFO: Done: Result Analysis
+2016-09-02 15:21:23,660 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:23,660 DEBUG: Start: Training
+2016-09-02 15:21:23,736 DEBUG: Info: Time for Training: 0.26513504982[s]
+2016-09-02 15:21:23,737 DEBUG: Done: Training
+2016-09-02 15:21:23,737 DEBUG: Start: Predicting
+2016-09-02 15:21:23,754 DEBUG: Done: Predicting
+2016-09-02 15:21:23,754 DEBUG: Start: Getting Results
+2016-09-02 15:21:23,755 DEBUG: Done: Getting Results
+2016-09-02 15:21:23,755 INFO: Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
+2016-09-02 15:21:23,756 INFO: Done: Result Analysis
+2016-09-02 15:21:23,842 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:23,842 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:21:23,842 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:23,843 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:23,843 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:21:23,843 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:23,858 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:21:23,858 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:21:23,858 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:23,859 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:23,865 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:21:23,865 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:21:23,866 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:23,866 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:33,914 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:33,914 DEBUG: Start: Training
+2016-09-02 15:21:34,494 DEBUG: Info: Time for Training: 10.6685709953[s]
+2016-09-02 15:21:34,494 DEBUG: Done: Training
+2016-09-02 15:21:34,495 DEBUG: Start: Predicting
+2016-09-02 15:21:34,507 DEBUG: Done: Predicting
+2016-09-02 15:21:34,507 DEBUG: Start: Getting Results
+2016-09-02 15:21:34,509 DEBUG: Done: Getting Results
+2016-09-02 15:21:34,509 INFO: Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 0.880165289256
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.880165289256
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:10
+2016-09-02 15:21:34,510 INFO: Done: Result Analysis
+2016-09-02 15:21:40,439 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:40,440 DEBUG: Start: Training
+2016-09-02 15:21:42,160 DEBUG: Info: Time for Training: 18.3348929882[s]
+2016-09-02 15:21:42,160 DEBUG: Done: Training
+2016-09-02 15:21:42,160 DEBUG: Start: Predicting
+2016-09-02 15:21:42,174 DEBUG: Done: Predicting
+2016-09-02 15:21:42,174 DEBUG: Start: Getting Results
+2016-09-02 15:21:42,176 DEBUG: Done: Getting Results
+2016-09-02 15:21:42,176 INFO: Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.838095238095
+
+
+ Classification took 0:00:18
+2016-09-02 15:21:42,176 INFO: Done: Result Analysis
+2016-09-02 15:21:42,347 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:42,347 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:42,347 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:21:42,347 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:21:42,347 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:42,347 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:42,371 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:21:42,371 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:21:42,372 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:21:42,372 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:21:42,372 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:42,372 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:42,372 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:42,372 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:45,207 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:45,208 DEBUG: Start: Training
+2016-09-02 15:21:45,453 DEBUG: Info: Time for Training: 3.1251180172[s]
+2016-09-02 15:21:45,453 DEBUG: Done: Training
+2016-09-02 15:21:45,453 DEBUG: Start: Predicting
+2016-09-02 15:21:45,467 DEBUG: Done: Predicting
+2016-09-02 15:21:45,467 DEBUG: Start: Getting Results
+2016-09-02 15:21:45,468 DEBUG: Done: Getting Results
+2016-09-02 15:21:45,468 INFO: Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, 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 : 0.809523809524
+
+
+ Classification took 0:00:03
+2016-09-02 15:21:45,469 INFO: Done: Result Analysis
+2016-09-02 15:21:47,987 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:47,987 DEBUG: Start: Training
+2016-09-02 15:21:48,137 DEBUG: Info: Time for Training: 5.8091340065[s]
+2016-09-02 15:21:48,137 DEBUG: Done: Training
+2016-09-02 15:21:48,137 DEBUG: Start: Predicting
+2016-09-02 15:21:52,270 DEBUG: Done: Predicting
+2016-09-02 15:21:52,270 DEBUG: Start: Getting Results
+2016-09-02 15:21:52,271 DEBUG: Done: Getting Results
+2016-09-02 15:21:52,271 INFO: Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.826446280992
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.826446280992
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:05
+2016-09-02 15:21:52,271 INFO: Done: Result Analysis
+2016-09-02 15:21:52,405 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:52,405 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:21:52,405 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:21:52,405 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:21:52,406 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:52,406 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:21:52,423 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:21:52,423 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:21:52,423 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:21:52,423 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:21:52,423 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:52,423 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:21:52,423 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:52,423 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:21:55,309 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:55,310 DEBUG: Start: Training
+2016-09-02 15:21:55,563 DEBUG: Info: Time for Training: 3.17377495766[s]
+2016-09-02 15:21:55,563 DEBUG: Done: Training
+2016-09-02 15:21:55,564 DEBUG: Start: Predicting
+2016-09-02 15:21:55,621 DEBUG: Done: Predicting
+2016-09-02 15:21:55,621 DEBUG: Start: Getting Results
+2016-09-02 15:21:55,623 DEBUG: Done: Getting Results
+2016-09-02 15:21:55,623 INFO: Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.904958677686
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.904958677686
+ - Score on test : 0.742857142857
+
+
+ Classification took 0:00:03
+2016-09-02 15:21:55,623 INFO: Done: Result Analysis
+2016-09-02 15:21:59,525 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:21:59,525 DEBUG: Start: Training
+2016-09-02 15:22:02,830 DEBUG: Info: Time for Training: 10.4409120083[s]
+2016-09-02 15:22:02,830 DEBUG: Done: Training
+2016-09-02 15:22:02,830 DEBUG: Start: Predicting
+2016-09-02 15:22:03,765 DEBUG: Done: Predicting
+2016-09-02 15:22:03,765 DEBUG: Start: Getting Results
+2016-09-02 15:22:03,766 DEBUG: Done: Getting Results
+2016-09-02 15:22:03,766 INFO: Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.857142857143
+
+
+ Classification took 0:00:10
+2016-09-02 15:22:03,767 INFO: Done: Result Analysis
+2016-09-02 15:22:03,902 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:22:03,902 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:22:03,902 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:22:03,902 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:22:03,902 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:22:03,902 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:22:03,927 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:22:03,927 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:22:03,927 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:22:03,927 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:22:03,927 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:22:03,927 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:22:03,927 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:22:03,927 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:22:13,713 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:22:13,713 DEBUG: Start: Training
+2016-09-02 15:22:14,742 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:22:14,742 DEBUG: Start: Training
+2016-09-02 15:22:18,179 DEBUG: Info: Time for Training: 14.2957019806[s]
+2016-09-02 15:22:18,179 DEBUG: Done: Training
+2016-09-02 15:22:18,180 DEBUG: Start: Predicting
+2016-09-02 15:22:19,441 DEBUG: Done: Predicting
+2016-09-02 15:22:19,442 DEBUG: Start: Getting Results
+2016-09-02 15:22:19,443 DEBUG: Done: Getting Results
+2016-09-02 15:22:19,443 INFO: Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 257
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:14
+2016-09-02 15:22:19,443 INFO: Done: Result Analysis
+2016-09-02 15:22:19,609 DEBUG: Info: Time for Training: 15.7254669666[s]
+2016-09-02 15:22:19,609 DEBUG: Done: Training
+2016-09-02 15:22:19,609 DEBUG: Start: Predicting
+2016-09-02 15:22:20,715 DEBUG: Done: Predicting
+2016-09-02 15:22:20,715 DEBUG: Start: Getting Results
+2016-09-02 15:22:20,716 DEBUG: Done: Getting Results
+2016-09-02 15:22:20,716 INFO: Classification on MultiOmic database for Methyl with SVMPoly
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.760330578512
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:15
+2016-09-02 15:22:20,716 INFO: Done: Result Analysis
+2016-09-02 15:22:22,531 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:22:22,531 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:22:22,531 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:22:22,639 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:22:22,639 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:22:22,639 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:22:22,640 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:22:22,920 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:22:22,920 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:22:22,920 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:22:22,999 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:22:22,999 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:22:22,999 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:22:22,999 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:23:01,711 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:23:01,711 DEBUG: Start: Training
+2016-09-02 15:23:03,031 DEBUG: Info: Time for Training: 42.1560468674[s]
+2016-09-02 15:23:03,031 DEBUG: Done: Training
+2016-09-02 15:23:03,031 DEBUG: Start: Predicting
+2016-09-02 15:23:03,112 DEBUG: Done: Predicting
+2016-09-02 15:23:03,112 DEBUG: Start: Getting Results
+2016-09-02 15:23:03,126 DEBUG: Done: Getting Results
+2016-09-02 15:23:03,126 INFO: Classification on MultiOmic database for RANSeq with DecisionTree
+
+accuracy_score on train : 0.797520661157
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.797520661157
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:42
+2016-09-02 15:23:03,135 INFO: Done: Result Analysis
+2016-09-02 15:23:34,460 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:23:34,460 DEBUG: Start: Training
+2016-09-02 15:23:42,742 DEBUG: Info: Time for Training: 81.867525816[s]
+2016-09-02 15:23:42,742 DEBUG: Done: Training
+2016-09-02 15:23:42,743 DEBUG: Start: Predicting
+2016-09-02 15:23:42,787 DEBUG: Done: Predicting
+2016-09-02 15:23:42,787 DEBUG: Start: Getting Results
+2016-09-02 15:23:42,789 DEBUG: Done: Getting Results
+2016-09-02 15:23:42,789 INFO: Classification on MultiOmic database for RANSeq with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.571428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.571428571429
+
+
+ Classification took 0:01:21
+2016-09-02 15:23:42,789 INFO: Done: Result Analysis
+2016-09-02 15:23:44,120 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:23:44,120 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:23:44,120 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:23:44,157 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:23:44,157 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:23:44,158 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:23:44,158 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:23:44,631 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:23:44,631 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:23:44,631 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:23:44,697 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:23:44,697 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:23:44,697 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:23:44,697 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:23:48,305 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:23:48,305 DEBUG: Start: Training
+2016-09-02 15:23:48,659 DEBUG: Info: Time for Training: 5.68725204468[s]
+2016-09-02 15:23:48,659 DEBUG: Done: Training
+2016-09-02 15:23:48,659 DEBUG: Start: Predicting
+2016-09-02 15:23:48,761 DEBUG: Done: Predicting
+2016-09-02 15:23:48,761 DEBUG: Start: Getting Results
+2016-09-02 15:23:48,762 DEBUG: Done: Getting Results
+2016-09-02 15:23:48,762 INFO: Classification on MultiOmic database for RANSeq with RandomForest
+
+accuracy_score on train : 0.888429752066
+accuracy_score on test : 0.657142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.888429752066
+ - Score on test : 0.657142857143
+
+
+ Classification took 0:00:05
+2016-09-02 15:23:48,768 INFO: Done: Result Analysis
+2016-09-02 15:24:03,103 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:24:03,103 DEBUG: Start: Training
+2016-09-02 15:24:03,678 DEBUG: Info: Time for Training: 20.7172079086[s]
+2016-09-02 15:24:03,679 DEBUG: Done: Training
+2016-09-02 15:24:03,679 DEBUG: Start: Predicting
+2016-09-02 15:24:16,265 DEBUG: Done: Predicting
+2016-09-02 15:24:16,266 DEBUG: Start: Getting Results
+2016-09-02 15:24:16,266 DEBUG: Done: Getting Results
+2016-09-02 15:24:16,267 INFO: Classification on MultiOmic database for RANSeq with KNN
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.760330578512
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:20
+2016-09-02 15:24:16,275 INFO: Done: Result Analysis
+2016-09-02 15:24:17,745 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:24:17,746 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:24:17,746 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:24:17,783 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:24:17,783 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:24:17,783 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:24:17,783 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:24:17,867 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:24:17,868 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:24:17,868 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:24:17,938 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:24:17,938 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:24:17,938 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:24:17,939 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:24:27,521 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:24:27,521 DEBUG: Start: Training
+2016-09-02 15:24:27,835 DEBUG: Info: Time for Training: 11.4740538597[s]
+2016-09-02 15:24:27,835 DEBUG: Done: Training
+2016-09-02 15:24:27,835 DEBUG: Start: Predicting
+2016-09-02 15:24:28,027 DEBUG: Done: Predicting
+2016-09-02 15:24:28,027 DEBUG: Start: Getting Results
+2016-09-02 15:24:28,029 DEBUG: Done: Getting Results
+2016-09-02 15:24:28,029 INFO: Classification on MultiOmic database for RANSeq with SGD
+
+accuracy_score on train : 0.723140495868
+accuracy_score on test : 0.590476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.723140495868
+ - Score on test : 0.590476190476
+
+
+ Classification took 0:00:11
+2016-09-02 15:24:28,042 INFO: Done: Result Analysis
+2016-09-02 15:24:51,032 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:24:51,033 DEBUG: Start: Training
+2016-09-02 15:25:07,190 DEBUG: Info: Time for Training: 50.8405668736[s]
+2016-09-02 15:25:07,191 DEBUG: Done: Training
+2016-09-02 15:25:07,191 DEBUG: Start: Predicting
+2016-09-02 15:25:11,465 DEBUG: Done: Predicting
+2016-09-02 15:25:11,465 DEBUG: Start: Getting Results
+2016-09-02 15:25:11,466 DEBUG: Done: Getting Results
+2016-09-02 15:25:11,466 INFO: Classification on MultiOmic database for RANSeq with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.590476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.590476190476
+
+
+ Classification took 0:00:50
+2016-09-02 15:25:11,466 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152118Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152118Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..77b54ac5bf3ec17e31ce1a56c76238c30955924e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152118Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.771428571429
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152118Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152118Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..107f7e27e2e433583bc543c0fe2de6c9b1894890
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152118Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.742857142857
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152119Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152119Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..16e40d28df730144cf409babdf2c3a9dd6c97f25
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152119Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.768595041322
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.768595041322
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152119Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152119Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..263bb69d9663b0db1fb664248fdcfe89ea047eac
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152119Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 0.98347107438
+accuracy_score on test : 0.819047619048
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.98347107438
+ - Score on test : 0.819047619048
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152120Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152120Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..675118ba4b13b995264a6c6600c5da116cc468f4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152120Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.830578512397
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.830578512397
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152120Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152120Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f9d87ad3c402ed1e602d7b33256694798afc68b5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152120Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.681818181818
+accuracy_score on test : 0.657142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.681818181818
+ - Score on test : 0.657142857143
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152120Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152120Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4f846650df0493b30ed8fd52f602303acc40c2e5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152120Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.239669421488
+accuracy_score on test : 0.333333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.239669421488
+ - Score on test : 0.333333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152121Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152121Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fb75c74fca0d8586fcae8edba956eddea49ee408
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152121Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152122Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152122Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..18088f9f6d381e8b977c305bf010df31db93d520
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152122Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.742857142857
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152122Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152122Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..12a7e294d4401bd24a271d8e03e346d68b841576
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152122Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.742857142857
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152122Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152122Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1ccf90e5f3dceab16972076b6e4831f0ddd1e125
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152122Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.760330578512
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3a6ea3d4a13c688cf87c252954c062fa4a556e25
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, 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 : 0.733333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6f7eb0818c7b6a0eb87bcec5ff64397c5caf3553
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.603305785124
+accuracy_score on test : 0.628571428571
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.603305785124
+ - Score on test : 0.628571428571
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b50a810ea058f536fa7731a47fc63942c6eaf498
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.599173553719
+accuracy_score on test : 0.580952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.599173553719
+ - Score on test : 0.580952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a76d7f283d8f41b1dcca92b9a5b4439e43a96b1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMPoly
+
+accuracy_score on train : 0.239669421488
+accuracy_score on test : 0.333333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.239669421488
+ - Score on test : 0.333333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f4836944c19c64d5f2c0edf97b484d201218e6a4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152123Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152134Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152134Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9e38f2bd4f565c0f10a46b04a34eef15b22c7c95
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152134Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 0.880165289256
+accuracy_score on test : 0.895238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.880165289256
+ - Score on test : 0.895238095238
+
+
+ Classification took 0:00:10
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152142Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152142Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ee8d10a88f518a4df47fe3d600dc6ff956c8cd8a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152142Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.838095238095
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.838095238095
+
+
+ Classification took 0:00:18
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152145Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152145Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8856b0811b6880c7f1953d252e6f964f7da35754
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152145Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, 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 : 0.809523809524
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152152Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152152Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c1431862d60a18f58311f19556c63d9194f575d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152152Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.826446280992
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.826446280992
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:05
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152155Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152155Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0ccee84251fec1d06de56efd31d3d7ec7c167d72
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152155Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.904958677686
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.904958677686
+ - Score on test : 0.742857142857
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152203Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152203Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b79fe4d249f2ecb67c43099babe89a34a22bdf3a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152203Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.857142857143
+
+
+ Classification took 0:00:10
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152219Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152219Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fabacdfa13011753f81c350f779ada86dbe1f6ad
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152219Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 257
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:14
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152220Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152220Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b3a0e584d4c50cecbb836f610b6cb25bf4c7fe76
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152220Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMPoly
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.760330578512
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:15
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152303Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152303Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7e43614d4802b34341f6127b32a931c144e90a90
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152303Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with DecisionTree
+
+accuracy_score on train : 0.797520661157
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.797520661157
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:42
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152342Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152342Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6729fdfac3fe6f632d87182940ea0b37019db5cd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152342Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for RANSeq with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.571428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.571428571429
+
+
+ Classification took 0:01:21
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152348Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152348Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aaffddf0bd3c1473222df5b497cfd7b5703edba3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152348Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with RandomForest
+
+accuracy_score on train : 0.888429752066
+accuracy_score on test : 0.657142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, max_depth : 4
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.888429752066
+ - Score on test : 0.657142857143
+
+
+ Classification took 0:00:05
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152416Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152416Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..62f883a91167374e23276e668c3fca3ac09a637b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152416Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with KNN
+
+accuracy_score on train : 0.760330578512
+accuracy_score on test : 0.666666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.760330578512
+ - Score on test : 0.666666666667
+
+
+ Classification took 0:00:20
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152428Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152428Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..13bdc8086d087baa0a3bf60242137149885c4373
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152428Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with SGD
+
+accuracy_score on train : 0.723140495868
+accuracy_score on test : 0.590476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.723140495868
+ - Score on test : 0.590476190476
+
+
+ Classification took 0:00:11
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-152511Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-152511Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9f6e3b10d546aa46c5fe054a9748597bb2eabf30
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-152511Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.590476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 7968
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.590476190476
+
+
+ Classification took 0:00:50
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153851-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-153851-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..b0555048dc2302817841330c2b27177d1e4b551a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153851-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,247 @@
+2016-09-02 15:38:51,255 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:38:51,255 WARNING: This may use a lot of HDD storage space : 0 Gbytes
+2016-09-02 15:38:54,795 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:38:54,878 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:38:55,912 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:38:55,912 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:38:55,912 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:38:55,913 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:38:55,913 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:38:55,913 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:38:55,913 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:38:55,913 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:38:55,913 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:38:55,913 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:38:55,914 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:38:55,914 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:38:55,914 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:38:55,914 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:38:55,971 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:38:55,971 DEBUG: Start: Training
+2016-09-02 15:38:55,972 DEBUG: Info: Time for Training: 0.0607240200043[s]
+2016-09-02 15:38:55,972 DEBUG: Done: Training
+2016-09-02 15:38:55,972 DEBUG: Start: Predicting
+2016-09-02 15:38:55,975 DEBUG: Done: Predicting
+2016-09-02 15:38:55,975 DEBUG: Start: Getting Results
+2016-09-02 15:38:55,976 DEBUG: Done: Getting Results
+2016-09-02 15:38:55,976 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 0.756198347107
+accuracy_score on test : 0.67619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.756198347107
+ - Score on test : 0.67619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:38:55,977 INFO: Done: Result Analysis
+2016-09-02 15:38:56,010 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:38:56,011 DEBUG: Start: Training
+2016-09-02 15:38:56,016 DEBUG: Info: Time for Training: 0.104045152664[s]
+2016-09-02 15:38:56,016 DEBUG: Done: Training
+2016-09-02 15:38:56,016 DEBUG: Start: Predicting
+2016-09-02 15:38:56,019 DEBUG: Done: Predicting
+2016-09-02 15:38:56,019 DEBUG: Start: Getting Results
+2016-09-02 15:38:56,021 DEBUG: Done: Getting Results
+2016-09-02 15:38:56,021 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.704761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.704761904762
+
+
+ Classification took 0:00:00
+2016-09-02 15:38:56,022 INFO: Done: Result Analysis
+2016-09-02 15:38:56,166 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:38:56,166 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:38:56,167 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:38:56,167 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:38:56,167 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:38:56,167 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:38:56,168 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:38:56,168 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:38:56,168 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:38:56,168 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:38:56,168 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:38:56,168 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:38:56,169 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:38:56,169 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:38:56,236 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:38:56,236 DEBUG: Start: Training
+2016-09-02 15:38:56,237 DEBUG: Info: Time for Training: 0.0715620517731[s]
+2016-09-02 15:38:56,237 DEBUG: Done: Training
+2016-09-02 15:38:56,237 DEBUG: Start: Predicting
+2016-09-02 15:38:56,262 DEBUG: Done: Predicting
+2016-09-02 15:38:56,262 DEBUG: Start: Getting Results
+2016-09-02 15:38:56,263 DEBUG: Done: Getting Results
+2016-09-02 15:38:56,263 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.756198347107
+accuracy_score on test : 0.67619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.756198347107
+ - Score on test : 0.67619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:38:56,264 INFO: Done: Result Analysis
+2016-09-02 15:38:57,053 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:38:57,054 DEBUG: Start: Training
+2016-09-02 15:38:57,135 DEBUG: Info: Time for Training: 0.96910405159[s]
+2016-09-02 15:38:57,135 DEBUG: Done: Training
+2016-09-02 15:38:57,135 DEBUG: Start: Predicting
+2016-09-02 15:38:57,144 DEBUG: Done: Predicting
+2016-09-02 15:38:57,144 DEBUG: Start: Getting Results
+2016-09-02 15:38:57,146 DEBUG: Done: Getting Results
+2016-09-02 15:38:57,146 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.98347107438
+accuracy_score on test : 0.714285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.98347107438
+ - Score on test : 0.714285714286
+
+
+ Classification took 0:00:00
+2016-09-02 15:38:57,146 INFO: Done: Result Analysis
+2016-09-02 15:38:57,217 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:38:57,218 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:38:57,218 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:38:57,218 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:38:57,219 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:38:57,219 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:38:57,219 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:38:57,219 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:38:57,219 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:38:57,219 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:38:57,220 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:38:57,220 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:38:57,220 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:38:57,221 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:38:57,363 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:38:57,363 DEBUG: Start: Training
+2016-09-02 15:38:57,388 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:38:57,388 DEBUG: Start: Training
+2016-09-02 15:38:57,390 DEBUG: Info: Time for Training: 0.172912120819[s]
+2016-09-02 15:38:57,390 DEBUG: Done: Training
+2016-09-02 15:38:57,390 DEBUG: Start: Predicting
+2016-09-02 15:38:57,406 DEBUG: Done: Predicting
+2016-09-02 15:38:57,406 DEBUG: Start: Getting Results
+2016-09-02 15:38:57,407 DEBUG: Done: Getting Results
+2016-09-02 15:38:57,408 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.747933884298
+accuracy_score on test : 0.685714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.747933884298
+ - Score on test : 0.685714285714
+
+
+ Classification took 0:00:00
+2016-09-02 15:38:57,408 INFO: Done: Result Analysis
+2016-09-02 15:38:57,419 DEBUG: Info: Time for Training: 0.202293157578[s]
+2016-09-02 15:38:57,419 DEBUG: Done: Training
+2016-09-02 15:38:57,419 DEBUG: Start: Predicting
+2016-09-02 15:38:57,433 DEBUG: Done: Predicting
+2016-09-02 15:38:57,433 DEBUG: Start: Getting Results
+2016-09-02 15:38:57,434 DEBUG: Done: Getting Results
+2016-09-02 15:38:57,435 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.595041322314
+accuracy_score on test : 0.590476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 65
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.595041322314
+ - Score on test : 0.590476190476
+
+
+ Classification took 0:00:00
+2016-09-02 15:38:57,435 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153855Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153855Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6ad12c3982220326e2d6d0478e4f18223afd6326
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153855Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 0.756198347107
+accuracy_score on test : 0.67619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.756198347107
+ - Score on test : 0.67619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153856Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153856Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ae845f44f8ae24991d7f78defa36878387b6ded9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153856Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.704761904762
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.704761904762
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153856Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153856Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9870ce24d628ec5412afa31cb390c35f0f6e195d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153856Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.756198347107
+accuracy_score on test : 0.67619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.756198347107
+ - Score on test : 0.67619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153857Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153857Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57a32866772728c8a8759acfe5f92686188fa2c3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153857Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.98347107438
+accuracy_score on test : 0.714285714286
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.98347107438
+ - Score on test : 0.714285714286
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153857Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153857Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fac454f1dbb1ceeb40e81937aac730acd84acc58
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153857Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.747933884298
+accuracy_score on test : 0.685714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.747933884298
+ - Score on test : 0.685714285714
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153857Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153857Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..7766f05ef23c5e3209601dbab3873ba5951bfa70
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153857Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.595041322314
+accuracy_score on test : 0.590476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 65
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.595041322314
+ - Score on test : 0.590476190476
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153930-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-153930-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..5d0649b5365746ec8041cb7eeee3e010d2875588
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153930-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,247 @@
+2016-09-02 15:39:30,905 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:39:30,905 WARNING: This may use a lot of HDD storage space : 0.273145851562 Gbytes
+2016-09-02 15:39:32,586 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:39:32,587 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:39:32,637 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:39:32,637 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:39:32,637 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:39:32,637 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:39:32,637 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:39:32,637 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:39:32,638 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:39:32,638 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:39:32,638 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:39:32,638 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:39:32,638 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:39:32,638 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:39:32,638 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:39:32,638 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:39:32,694 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:39:32,694 DEBUG: Start: Training
+2016-09-02 15:39:32,696 DEBUG: Info: Time for Training: 0.0599081516266[s]
+2016-09-02 15:39:32,696 DEBUG: Done: Training
+2016-09-02 15:39:32,696 DEBUG: Start: Predicting
+2016-09-02 15:39:32,699 DEBUG: Done: Predicting
+2016-09-02 15:39:32,699 DEBUG: Start: Getting Results
+2016-09-02 15:39:32,700 DEBUG: Done: Getting Results
+2016-09-02 15:39:32,700 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.761904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:39:32,700 INFO: Done: Result Analysis
+2016-09-02 15:39:32,730 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:39:32,730 DEBUG: Start: Training
+2016-09-02 15:39:32,735 DEBUG: Info: Time for Training: 0.098865032196[s]
+2016-09-02 15:39:32,735 DEBUG: Done: Training
+2016-09-02 15:39:32,735 DEBUG: Start: Predicting
+2016-09-02 15:39:32,738 DEBUG: Done: Predicting
+2016-09-02 15:39:32,738 DEBUG: Start: Getting Results
+2016-09-02 15:39:32,739 DEBUG: Done: Getting Results
+2016-09-02 15:39:32,739 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.790476190476
+
+
+ Classification took 0:00:00
+2016-09-02 15:39:32,740 INFO: Done: Result Analysis
+2016-09-02 15:39:32,879 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:39:32,879 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:39:32,879 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:39:32,880 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:39:32,880 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:39:32,880 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:39:32,881 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:39:32,881 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:39:32,881 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:39:32,881 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:39:32,881 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:39:32,881 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:39:32,881 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:39:32,881 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:39:32,952 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:39:32,952 DEBUG: Start: Training
+2016-09-02 15:39:32,953 DEBUG: Info: Time for Training: 0.0751960277557[s]
+2016-09-02 15:39:32,954 DEBUG: Done: Training
+2016-09-02 15:39:32,954 DEBUG: Start: Predicting
+2016-09-02 15:39:32,975 DEBUG: Done: Predicting
+2016-09-02 15:39:32,975 DEBUG: Start: Getting Results
+2016-09-02 15:39:32,976 DEBUG: Done: Getting Results
+2016-09-02 15:39:32,976 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.731404958678
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 33
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.731404958678
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:39:32,976 INFO: Done: Result Analysis
+2016-09-02 15:39:33,335 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:39:33,336 DEBUG: Start: Training
+2016-09-02 15:39:33,361 DEBUG: Info: Time for Training: 0.482002973557[s]
+2016-09-02 15:39:33,361 DEBUG: Done: Training
+2016-09-02 15:39:33,361 DEBUG: Start: Predicting
+2016-09-02 15:39:33,365 DEBUG: Done: Predicting
+2016-09-02 15:39:33,365 DEBUG: Start: Getting Results
+2016-09-02 15:39:33,366 DEBUG: Done: Getting Results
+2016-09-02 15:39:33,366 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.979338842975
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 7, 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 : 0.979338842975
+ - Score on test : 0.857142857143
+
+
+ Classification took 0:00:00
+2016-09-02 15:39:33,366 INFO: Done: Result Analysis
+2016-09-02 15:39:33,429 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:39:33,429 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:39:33,430 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:39:33,430 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:39:33,430 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:39:33,430 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:39:33,431 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:39:33,431 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:39:33,431 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:39:33,431 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:39:33,431 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:39:33,431 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:39:33,431 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:39:33,431 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:39:33,562 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:39:33,562 DEBUG: Start: Training
+2016-09-02 15:39:33,565 DEBUG: Info: Time for Training: 0.136486053467[s]
+2016-09-02 15:39:33,565 DEBUG: Done: Training
+2016-09-02 15:39:33,565 DEBUG: Start: Predicting
+2016-09-02 15:39:33,578 DEBUG: Done: Predicting
+2016-09-02 15:39:33,578 DEBUG: Start: Getting Results
+2016-09-02 15:39:33,579 DEBUG: Done: Getting Results
+2016-09-02 15:39:33,579 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.702479338843
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.702479338843
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:39:33,580 INFO: Done: Result Analysis
+2016-09-02 15:39:33,598 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:39:33,598 DEBUG: Start: Training
+2016-09-02 15:39:33,646 DEBUG: Info: Time for Training: 0.217049121857[s]
+2016-09-02 15:39:33,646 DEBUG: Done: Training
+2016-09-02 15:39:33,646 DEBUG: Start: Predicting
+2016-09-02 15:39:33,656 DEBUG: Done: Predicting
+2016-09-02 15:39:33,656 DEBUG: Start: Getting Results
+2016-09-02 15:39:33,657 DEBUG: Done: Getting Results
+2016-09-02 15:39:33,657 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.566115702479
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9407
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566115702479
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:39:33,657 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153932Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153932Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2f956cb65daab76a2142e7a854d6c937b86f1683
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153932Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.790476190476
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153932Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153932Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..840eff49b0cd0acd2313fe2163d9f2805a642310
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153932Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.761904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.995867768595
+ - Score on test : 0.761904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153932Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153932Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2337f82b91843d6922bb33d9196ded54dc9ade09
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153932Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.731404958678
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 33
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.731404958678
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153933Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153933Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0de9d9dcfa1a2a5af4e9adbd089cc8e4c770fbdd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153933Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.979338842975
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 7, 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 : 0.979338842975
+ - Score on test : 0.857142857143
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153933Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153933Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..71032511e9a99f360321f2f15db3008531c441de
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153933Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.702479338843
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.702479338843
+ - Score on test : 0.72380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-153933Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-153933Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..07b8d3c62052333593c2c2a901880833a71326e8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-153933Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.566115702479
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 9407
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.566115702479
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154355-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-154355-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..6bb47bfb886d118edcf246323966ae6a81ec5347
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154355-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,247 @@
+2016-09-02 15:43:55,888 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:43:55,888 WARNING: This may use a lot of HDD storage space : 0.273145851562 Gbytes
+2016-09-02 15:43:57,520 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:43:57,521 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:43:57,566 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:43:57,566 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:43:57,567 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:43:57,567 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:43:57,567 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:43:57,567 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:43:57,568 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:43:57,568 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:43:57,568 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:43:57,568 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:43:57,568 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:43:57,568 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:43:57,568 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:43:57,568 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:43:57,627 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:43:57,628 DEBUG: Start: Training
+2016-09-02 15:43:57,630 DEBUG: Info: Time for Training: 0.0647261142731[s]
+2016-09-02 15:43:57,630 DEBUG: Done: Training
+2016-09-02 15:43:57,630 DEBUG: Start: Predicting
+2016-09-02 15:43:57,633 DEBUG: Done: Predicting
+2016-09-02 15:43:57,633 DEBUG: Start: Getting Results
+2016-09-02 15:43:57,634 DEBUG: Done: Getting Results
+2016-09-02 15:43:57,634 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.847619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:43:57,634 INFO: Done: Result Analysis
+2016-09-02 15:43:57,653 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:43:57,654 DEBUG: Start: Training
+2016-09-02 15:43:57,658 DEBUG: Info: Time for Training: 0.0926649570465[s]
+2016-09-02 15:43:57,658 DEBUG: Done: Training
+2016-09-02 15:43:57,658 DEBUG: Start: Predicting
+2016-09-02 15:43:57,661 DEBUG: Done: Predicting
+2016-09-02 15:43:57,661 DEBUG: Start: Getting Results
+2016-09-02 15:43:57,662 DEBUG: Done: Getting Results
+2016-09-02 15:43:57,662 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.857142857143
+
+
+ Classification took 0:00:00
+2016-09-02 15:43:57,663 INFO: Done: Result Analysis
+2016-09-02 15:43:57,815 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:43:57,815 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:43:57,816 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:43:57,816 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:43:57,816 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:43:57,816 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:43:57,816 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:43:57,816 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:43:57,817 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:43:57,817 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:43:57,817 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:43:57,817 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:43:57,817 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:43:57,817 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:43:57,883 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:43:57,883 DEBUG: Start: Training
+2016-09-02 15:43:57,884 DEBUG: Info: Time for Training: 0.06951212883[s]
+2016-09-02 15:43:57,884 DEBUG: Done: Training
+2016-09-02 15:43:57,884 DEBUG: Start: Predicting
+2016-09-02 15:43:57,904 DEBUG: Done: Predicting
+2016-09-02 15:43:57,904 DEBUG: Start: Getting Results
+2016-09-02 15:43:57,905 DEBUG: Done: Getting Results
+2016-09-02 15:43:57,905 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.731404958678
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.731404958678
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:43:57,906 INFO: Done: Result Analysis
+2016-09-02 15:43:58,277 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:43:58,277 DEBUG: Start: Training
+2016-09-02 15:43:58,332 DEBUG: Info: Time for Training: 0.517189979553[s]
+2016-09-02 15:43:58,332 DEBUG: Done: Training
+2016-09-02 15:43:58,332 DEBUG: Start: Predicting
+2016-09-02 15:43:58,340 DEBUG: Done: Predicting
+2016-09-02 15:43:58,340 DEBUG: Start: Getting Results
+2016-09-02 15:43:58,341 DEBUG: Done: Getting Results
+2016-09-02 15:43:58,341 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 22, 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 : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:43:58,341 INFO: Done: Result Analysis
+2016-09-02 15:43:58,469 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:43:58,469 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:43:58,470 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:43:58,470 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:43:58,470 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:43:58,470 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:43:58,472 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:43:58,472 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:43:58,472 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:43:58,472 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:43:58,473 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:43:58,473 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:43:58,473 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:43:58,473 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:43:58,568 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:43:58,568 DEBUG: Start: Training
+2016-09-02 15:43:58,569 DEBUG: Info: Time for Training: 0.101846933365[s]
+2016-09-02 15:43:58,569 DEBUG: Done: Training
+2016-09-02 15:43:58,570 DEBUG: Start: Predicting
+2016-09-02 15:43:58,573 DEBUG: Done: Predicting
+2016-09-02 15:43:58,573 DEBUG: Start: Getting Results
+2016-09-02 15:43:58,574 DEBUG: Done: Getting Results
+2016-09-02 15:43:58,574 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.731404958678
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.731404958678
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:43:58,574 INFO: Done: Result Analysis
+2016-09-02 15:43:58,609 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:43:58,610 DEBUG: Start: Training
+2016-09-02 15:43:58,656 DEBUG: Info: Time for Training: 0.188686847687[s]
+2016-09-02 15:43:58,656 DEBUG: Done: Training
+2016-09-02 15:43:58,656 DEBUG: Start: Predicting
+2016-09-02 15:43:58,666 DEBUG: Done: Predicting
+2016-09-02 15:43:58,666 DEBUG: Start: Getting Results
+2016-09-02 15:43:58,667 DEBUG: Done: Getting Results
+2016-09-02 15:43:58,667 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.54958677686
+accuracy_score on test : 0.590476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2494
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.54958677686
+ - Score on test : 0.590476190476
+
+
+ Classification took 0:00:00
+2016-09-02 15:43:58,667 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154357Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154357Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4f24e3822add60612d7bbb909e7b179ad003e403
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154357Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.857142857143
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154357Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154357Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c5045411b38f0c822359d6236c5c1fe8f4bc7e3a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154357Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.847619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154357Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154357Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3d3afc6666074255f6c0d6f8e5529b546da1bc23
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154357Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.731404958678
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.731404958678
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154358Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154358Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dbf7c82d5f26d728e1895880293623361d33e250
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154358Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 22, 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 : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154358Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154358Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a8434f6475310470be254ae08f324690313bcfeb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154358Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.731404958678
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.731404958678
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154358Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154358Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bd85cbccacac1750290957a2dcb74a49b006cf36
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154358Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.54958677686
+accuracy_score on test : 0.590476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2494
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.54958677686
+ - Score on test : 0.590476190476
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154432-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-154432-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..ff62cfde9afce61cad7a66382337ae143618bf51
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154432-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,247 @@
+2016-09-02 15:44:32,058 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:44:32,058 WARNING: This may use a lot of HDD storage space : 0.273145851562 Gbytes
+2016-09-02 15:44:34,008 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:44:34,009 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:44:34,056 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:44:34,056 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:44:34,056 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:44:34,056 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:44:34,056 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:44:34,056 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:44:34,057 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:44:34,057 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:44:34,057 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:44:34,057 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:44:34,057 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:44:34,057 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:44:34,057 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:44:34,057 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:44:34,122 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:44:34,122 DEBUG: Start: Training
+2016-09-02 15:44:34,125 DEBUG: Info: Time for Training: 0.0696170330048[s]
+2016-09-02 15:44:34,125 DEBUG: Done: Training
+2016-09-02 15:44:34,125 DEBUG: Start: Predicting
+2016-09-02 15:44:34,127 DEBUG: Done: Predicting
+2016-09-02 15:44:34,128 DEBUG: Start: Getting Results
+2016-09-02 15:44:34,128 DEBUG: Done: Getting Results
+2016-09-02 15:44:34,129 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.809523809524
+
+
+ Classification took 0:00:00
+2016-09-02 15:44:34,129 INFO: Done: Result Analysis
+2016-09-02 15:44:34,150 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:44:34,150 DEBUG: Start: Training
+2016-09-02 15:44:34,155 DEBUG: Info: Time for Training: 0.0996170043945[s]
+2016-09-02 15:44:34,155 DEBUG: Done: Training
+2016-09-02 15:44:34,155 DEBUG: Start: Predicting
+2016-09-02 15:44:34,158 DEBUG: Done: Predicting
+2016-09-02 15:44:34,158 DEBUG: Start: Getting Results
+2016-09-02 15:44:34,159 DEBUG: Done: Getting Results
+2016-09-02 15:44:34,159 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:44:34,160 INFO: Done: Result Analysis
+2016-09-02 15:44:34,307 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:44:34,307 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:44:34,307 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:44:34,307 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:44:34,307 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:44:34,307 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:44:34,309 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:44:34,309 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:44:34,309 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:44:34,309 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:44:34,309 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:44:34,309 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:44:34,310 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:44:34,310 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:44:34,421 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:44:34,421 DEBUG: Start: Training
+2016-09-02 15:44:34,423 DEBUG: Info: Time for Training: 0.117078065872[s]
+2016-09-02 15:44:34,423 DEBUG: Done: Training
+2016-09-02 15:44:34,423 DEBUG: Start: Predicting
+2016-09-02 15:44:34,452 DEBUG: Done: Predicting
+2016-09-02 15:44:34,452 DEBUG: Start: Getting Results
+2016-09-02 15:44:34,454 DEBUG: Done: Getting Results
+2016-09-02 15:44:34,454 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.739669421488
+accuracy_score on test : 0.695238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.739669421488
+ - Score on test : 0.695238095238
+
+
+ Classification took 0:00:00
+2016-09-02 15:44:34,454 INFO: Done: Result Analysis
+2016-09-02 15:44:34,683 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:44:34,683 DEBUG: Start: Training
+2016-09-02 15:44:34,721 DEBUG: Info: Time for Training: 0.415308952332[s]
+2016-09-02 15:44:34,721 DEBUG: Done: Training
+2016-09-02 15:44:34,721 DEBUG: Start: Predicting
+2016-09-02 15:44:34,726 DEBUG: Done: Predicting
+2016-09-02 15:44:34,726 DEBUG: Start: Getting Results
+2016-09-02 15:44:34,727 DEBUG: Done: Getting Results
+2016-09-02 15:44:34,727 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.979338842975
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, 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 : 0.979338842975
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:00
+2016-09-02 15:44:34,728 INFO: Done: Result Analysis
+2016-09-02 15:44:34,854 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:44:34,854 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:44:34,854 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:44:34,854 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:44:34,854 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:44:34,854 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:44:34,855 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:44:34,855 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:44:34,855 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:44:34,855 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:44:34,855 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:44:34,855 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:44:34,855 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:44:34,855 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:44:34,986 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:44:34,986 DEBUG: Start: Training
+2016-09-02 15:44:34,989 DEBUG: Info: Time for Training: 0.13588809967[s]
+2016-09-02 15:44:34,989 DEBUG: Done: Training
+2016-09-02 15:44:34,989 DEBUG: Start: Predicting
+2016-09-02 15:44:35,000 DEBUG: Done: Predicting
+2016-09-02 15:44:35,000 DEBUG: Start: Getting Results
+2016-09-02 15:44:35,001 DEBUG: Done: Getting Results
+2016-09-02 15:44:35,002 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.603305785124
+accuracy_score on test : 0.52380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.603305785124
+ - Score on test : 0.52380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:44:35,002 INFO: Done: Result Analysis
+2016-09-02 15:44:35,029 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:44:35,030 DEBUG: Start: Training
+2016-09-02 15:44:35,077 DEBUG: Info: Time for Training: 0.223808050156[s]
+2016-09-02 15:44:35,077 DEBUG: Done: Training
+2016-09-02 15:44:35,077 DEBUG: Start: Predicting
+2016-09-02 15:44:35,087 DEBUG: Done: Predicting
+2016-09-02 15:44:35,087 DEBUG: Start: Getting Results
+2016-09-02 15:44:35,088 DEBUG: Done: Getting Results
+2016-09-02 15:44:35,088 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.570247933884
+accuracy_score on test : 0.52380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3791
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.570247933884
+ - Score on test : 0.52380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:44:35,088 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b97920a149d0604e6b23d57bff834db7a7583c45
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d8f91c4940a80d4b87640c268e57176448e36994
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.809523809524
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8a58b9255c6b02133d6934c1524f7fdefbdabeba
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.739669421488
+accuracy_score on test : 0.695238095238
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.739669421488
+ - Score on test : 0.695238095238
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..742edc146dedaf216af319764b69e5352f785987
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.979338842975
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, 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 : 0.979338842975
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..37735ab1a54e0ccafbaa2337b4dcacb05896e655
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154434Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.603305785124
+accuracy_score on test : 0.52380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.603305785124
+ - Score on test : 0.52380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154435Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154435Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..667b5ea93baae96a1a2e741648d8653c7b80612a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154435Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.570247933884
+accuracy_score on test : 0.52380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3791
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.570247933884
+ - Score on test : 0.52380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154809-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-154809-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..e1c57e4eacce68966da19f627d21a5098c8b59b9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154809-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,247 @@
+2016-09-02 15:48:09,069 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:48:09,069 WARNING: This may use a lot of HDD storage space : 0.273145851562 Gbytes
+2016-09-02 15:48:10,358 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:48:10,359 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:48:10,413 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:48:10,413 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:48:10,413 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:48:10,413 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:48:10,414 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:48:10,414 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:48:10,415 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:48:10,415 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:48:10,415 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:48:10,415 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:48:10,415 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:48:10,415 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:48:10,415 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:48:10,415 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:48:10,505 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:48:10,505 DEBUG: Start: Training
+2016-09-02 15:48:10,508 DEBUG: Info: Time for Training: 0.0962390899658[s]
+2016-09-02 15:48:10,508 DEBUG: Done: Training
+2016-09-02 15:48:10,509 DEBUG: Start: Predicting
+2016-09-02 15:48:10,512 DEBUG: Done: Predicting
+2016-09-02 15:48:10,512 DEBUG: Start: Getting Results
+2016-09-02 15:48:10,513 DEBUG: Done: Getting Results
+2016-09-02 15:48:10,514 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:48:10,514 INFO: Done: Result Analysis
+2016-09-02 15:48:10,539 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:48:10,539 DEBUG: Start: Training
+2016-09-02 15:48:10,543 DEBUG: Info: Time for Training: 0.131463050842[s]
+2016-09-02 15:48:10,544 DEBUG: Done: Training
+2016-09-02 15:48:10,544 DEBUG: Start: Predicting
+2016-09-02 15:48:10,546 DEBUG: Done: Predicting
+2016-09-02 15:48:10,546 DEBUG: Start: Getting Results
+2016-09-02 15:48:10,548 DEBUG: Done: Getting Results
+2016-09-02 15:48:10,548 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:48:10,548 INFO: Done: Result Analysis
+2016-09-02 15:48:10,654 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:48:10,654 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:48:10,654 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:48:10,654 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:48:10,654 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:48:10,654 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:48:10,655 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:48:10,655 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:48:10,655 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:48:10,655 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:48:10,655 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:48:10,655 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:48:10,655 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:48:10,655 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:48:10,722 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:48:10,722 DEBUG: Start: Training
+2016-09-02 15:48:10,723 DEBUG: Info: Time for Training: 0.0694019794464[s]
+2016-09-02 15:48:10,723 DEBUG: Done: Training
+2016-09-02 15:48:10,723 DEBUG: Start: Predicting
+2016-09-02 15:48:10,740 DEBUG: Done: Predicting
+2016-09-02 15:48:10,741 DEBUG: Start: Getting Results
+2016-09-02 15:48:10,741 DEBUG: Done: Getting Results
+2016-09-02 15:48:10,742 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.727272727273
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.727272727273
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:48:10,742 INFO: Done: Result Analysis
+2016-09-02 15:48:10,970 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:48:10,970 DEBUG: Start: Training
+2016-09-02 15:48:11,011 DEBUG: Info: Time for Training: 0.357359886169[s]
+2016-09-02 15:48:11,011 DEBUG: Done: Training
+2016-09-02 15:48:11,011 DEBUG: Start: Predicting
+2016-09-02 15:48:11,017 DEBUG: Done: Predicting
+2016-09-02 15:48:11,017 DEBUG: Start: Getting Results
+2016-09-02 15:48:11,018 DEBUG: Done: Getting Results
+2016-09-02 15:48:11,018 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.987603305785
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 16, 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 : 0.987603305785
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
+2016-09-02 15:48:11,018 INFO: Done: Result Analysis
+2016-09-02 15:48:11,106 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:48:11,106 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:48:11,106 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:48:11,106 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:48:11,106 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:48:11,106 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:48:11,107 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:48:11,107 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:48:11,108 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:48:11,108 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:48:11,108 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:48:11,108 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:48:11,108 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:48:11,108 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:48:11,244 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:48:11,244 DEBUG: Start: Training
+2016-09-02 15:48:11,248 DEBUG: Info: Time for Training: 0.142592906952[s]
+2016-09-02 15:48:11,248 DEBUG: Done: Training
+2016-09-02 15:48:11,248 DEBUG: Start: Predicting
+2016-09-02 15:48:11,276 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:48:11,276 DEBUG: Start: Training
+2016-09-02 15:48:11,287 DEBUG: Done: Predicting
+2016-09-02 15:48:11,288 DEBUG: Start: Getting Results
+2016-09-02 15:48:11,289 DEBUG: Done: Getting Results
+2016-09-02 15:48:11,289 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.731404958678
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.731404958678
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:48:11,290 INFO: Done: Result Analysis
+2016-09-02 15:48:11,340 DEBUG: Info: Time for Training: 0.234586000443[s]
+2016-09-02 15:48:11,340 DEBUG: Done: Training
+2016-09-02 15:48:11,340 DEBUG: Start: Predicting
+2016-09-02 15:48:11,353 DEBUG: Done: Predicting
+2016-09-02 15:48:11,353 DEBUG: Start: Getting Results
+2016-09-02 15:48:11,354 DEBUG: Done: Getting Results
+2016-09-02 15:48:11,354 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.570247933884
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2001
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.570247933884
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:48:11,354 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154810Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154810Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b97920a149d0604e6b23d57bff834db7a7583c45
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154810Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154810Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154810Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..96c6ab5d9743e78a89307333ea7ce23e426de0f4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154810Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154810Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154810Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9574f1b0de1bbfe5b05234b39e3dd29f7ed4aaf1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154810Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.727272727273
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.727272727273
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154811Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154811Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aec567b9298b90f610226f67fbfa6c2dc0c2fe16
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154811Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.987603305785
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 16, 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 : 0.987603305785
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154811Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154811Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e807c34403ccf0939cc82d6c190dbedfd75bec3d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154811Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.731404958678
+accuracy_score on test : 0.733333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.731404958678
+ - Score on test : 0.733333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154811Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154811Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..50ca9ed364f9388f8c7812e19b813dddbc008848
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154811Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.570247933884
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 2001
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.570247933884
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154931-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-154931-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..21553ae8e50c0845c8866c4202ce61335e07882b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154931-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,247 @@
+2016-09-02 15:49:31,040 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:49:31,040 WARNING: This may use a lot of HDD storage space : 0.273145851562 Gbytes
+2016-09-02 15:49:32,566 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:49:32,568 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:49:32,613 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:49:32,613 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:49:32,614 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:49:32,614 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:49:32,614 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:49:32,614 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:49:32,614 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:49:32,614 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:49:32,615 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:49:32,615 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:49:32,615 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:49:32,615 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:49:32,615 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:49:32,615 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:49:32,677 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:49:32,677 DEBUG: Start: Training
+2016-09-02 15:49:32,680 DEBUG: Info: Time for Training: 0.0669529438019[s]
+2016-09-02 15:49:32,680 DEBUG: Done: Training
+2016-09-02 15:49:32,680 DEBUG: Start: Predicting
+2016-09-02 15:49:32,682 DEBUG: Done: Predicting
+2016-09-02 15:49:32,682 DEBUG: Start: Getting Results
+2016-09-02 15:49:32,683 DEBUG: Done: Getting Results
+2016-09-02 15:49:32,683 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.847619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:49:32,684 INFO: Done: Result Analysis
+2016-09-02 15:49:32,703 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:49:32,703 DEBUG: Start: Training
+2016-09-02 15:49:32,707 DEBUG: Info: Time for Training: 0.0948150157928[s]
+2016-09-02 15:49:32,708 DEBUG: Done: Training
+2016-09-02 15:49:32,708 DEBUG: Start: Predicting
+2016-09-02 15:49:32,710 DEBUG: Done: Predicting
+2016-09-02 15:49:32,711 DEBUG: Start: Getting Results
+2016-09-02 15:49:32,712 DEBUG: Done: Getting Results
+2016-09-02 15:49:32,712 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.847619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:49:32,713 INFO: Done: Result Analysis
+2016-09-02 15:49:32,861 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:49:32,861 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:49:32,861 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:49:32,861 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:49:32,861 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:49:32,861 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:49:32,862 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:49:32,862 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:49:32,862 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:49:32,862 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:49:32,862 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:49:32,862 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:49:32,862 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:49:32,862 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:49:32,933 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:49:32,933 DEBUG: Start: Training
+2016-09-02 15:49:32,934 DEBUG: Info: Time for Training: 0.0740809440613[s]
+2016-09-02 15:49:32,934 DEBUG: Done: Training
+2016-09-02 15:49:32,934 DEBUG: Start: Predicting
+2016-09-02 15:49:32,958 DEBUG: Done: Predicting
+2016-09-02 15:49:32,958 DEBUG: Start: Getting Results
+2016-09-02 15:49:32,959 DEBUG: Done: Getting Results
+2016-09-02 15:49:32,960 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.727272727273
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 33
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.727272727273
+ - Score on test : 0.742857142857
+
+
+ Classification took 0:00:00
+2016-09-02 15:49:32,960 INFO: Done: Result Analysis
+2016-09-02 15:49:33,188 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:49:33,188 DEBUG: Start: Training
+2016-09-02 15:49:33,232 DEBUG: Info: Time for Training: 0.371449947357[s]
+2016-09-02 15:49:33,232 DEBUG: Done: Training
+2016-09-02 15:49:33,232 DEBUG: Start: Predicting
+2016-09-02 15:49:33,238 DEBUG: Done: Predicting
+2016-09-02 15:49:33,238 DEBUG: Start: Getting Results
+2016-09-02 15:49:33,239 DEBUG: Done: Getting Results
+2016-09-02 15:49:33,239 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.828571428571
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, 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 : 0.828571428571
+
+
+ Classification took 0:00:00
+2016-09-02 15:49:33,239 INFO: Done: Result Analysis
+2016-09-02 15:49:33,315 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:49:33,315 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:49:33,315 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:49:33,315 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:49:33,316 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:49:33,316 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:49:33,316 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:49:33,316 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:49:33,316 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:49:33,316 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:49:33,317 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:49:33,317 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:49:33,317 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:49:33,317 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:49:33,421 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:49:33,421 DEBUG: Start: Training
+2016-09-02 15:49:33,423 DEBUG: Info: Time for Training: 0.108020067215[s]
+2016-09-02 15:49:33,423 DEBUG: Done: Training
+2016-09-02 15:49:33,423 DEBUG: Start: Predicting
+2016-09-02 15:49:33,426 DEBUG: Done: Predicting
+2016-09-02 15:49:33,426 DEBUG: Start: Getting Results
+2016-09-02 15:49:33,427 DEBUG: Done: Getting Results
+2016-09-02 15:49:33,427 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.648760330579
+accuracy_score on test : 0.67619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.648760330579
+ - Score on test : 0.67619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:49:33,428 INFO: Done: Result Analysis
+2016-09-02 15:49:33,443 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:49:33,443 DEBUG: Start: Training
+2016-09-02 15:49:33,493 DEBUG: Info: Time for Training: 0.17822098732[s]
+2016-09-02 15:49:33,493 DEBUG: Done: Training
+2016-09-02 15:49:33,493 DEBUG: Start: Predicting
+2016-09-02 15:49:33,503 DEBUG: Done: Predicting
+2016-09-02 15:49:33,503 DEBUG: Start: Getting Results
+2016-09-02 15:49:33,504 DEBUG: Done: Getting Results
+2016-09-02 15:49:33,505 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.590909090909
+accuracy_score on test : 0.609523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8929
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590909090909
+ - Score on test : 0.609523809524
+
+
+ Classification took 0:00:00
+2016-09-02 15:49:33,505 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154932Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154932Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cbbddda18b83ba99673453af9aa4d13bacbf7841
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154932Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.847619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154932Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154932Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f63ca2674eea7979d90b6927dd827cfff44f803c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154932Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.847619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154932Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154932Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..afb5029015bc703d127513d224e92229a6d9935c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154932Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.727272727273
+accuracy_score on test : 0.742857142857
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 33
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.727272727273
+ - Score on test : 0.742857142857
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154933Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154933Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6a3ec05c4f9949d433afe949557ddb2ec915555a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154933Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.828571428571
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, 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 : 0.828571428571
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154933Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154933Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bde34153a3e1f22372624b8387fc962a0aa6f558
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154933Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.648760330579
+accuracy_score on test : 0.67619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.648760330579
+ - Score on test : 0.67619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-154933Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-154933Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ed2a6e474f40fb58804be98fbe5fedc0423a691d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-154933Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.590909090909
+accuracy_score on test : 0.609523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 8929
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.590909090909
+ - Score on test : 0.609523809524
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155023-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-155023-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..608dc782068785f85d57010d1d64c462f4fa42fc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155023-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,247 @@
+2016-09-02 15:50:23,334 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:50:23,334 WARNING: This may use a lot of HDD storage space : 0.273145851562 Gbytes
+2016-09-02 15:50:25,334 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:50:25,337 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:50:25,386 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:50:25,386 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:50:25,386 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:50:25,386 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:50:25,386 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:50:25,386 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:50:25,387 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:50:25,387 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:50:25,388 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:50:25,388 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:50:25,388 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:50:25,388 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:50:25,388 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:50:25,388 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:50:25,444 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:50:25,444 DEBUG: Start: Training
+2016-09-02 15:50:25,446 DEBUG: Info: Time for Training: 0.0608510971069[s]
+2016-09-02 15:50:25,446 DEBUG: Done: Training
+2016-09-02 15:50:25,446 DEBUG: Start: Predicting
+2016-09-02 15:50:25,449 DEBUG: Done: Predicting
+2016-09-02 15:50:25,449 DEBUG: Start: Getting Results
+2016-09-02 15:50:25,450 DEBUG: Done: Getting Results
+2016-09-02 15:50:25,450 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 14
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
+2016-09-02 15:50:25,450 INFO: Done: Result Analysis
+2016-09-02 15:50:25,477 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:50:25,478 DEBUG: Start: Training
+2016-09-02 15:50:25,482 DEBUG: Info: Time for Training: 0.0966668128967[s]
+2016-09-02 15:50:25,482 DEBUG: Done: Training
+2016-09-02 15:50:25,482 DEBUG: Start: Predicting
+2016-09-02 15:50:25,485 DEBUG: Done: Predicting
+2016-09-02 15:50:25,485 DEBUG: Start: Getting Results
+2016-09-02 15:50:25,486 DEBUG: Done: Getting Results
+2016-09-02 15:50:25,486 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.771428571429
+
+
+ Classification took 0:00:00
+2016-09-02 15:50:25,487 INFO: Done: Result Analysis
+2016-09-02 15:50:25,631 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:50:25,631 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:50:25,632 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:50:25,632 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:50:25,632 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:50:25,632 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:50:25,634 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:50:25,634 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:50:25,635 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:50:25,635 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:50:25,635 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:50:25,635 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:50:25,635 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:50:25,635 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:50:25,740 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:50:25,741 DEBUG: Start: Training
+2016-09-02 15:50:25,742 DEBUG: Info: Time for Training: 0.112113952637[s]
+2016-09-02 15:50:25,742 DEBUG: Done: Training
+2016-09-02 15:50:25,742 DEBUG: Start: Predicting
+2016-09-02 15:50:25,781 DEBUG: Done: Predicting
+2016-09-02 15:50:25,781 DEBUG: Start: Getting Results
+2016-09-02 15:50:25,783 DEBUG: Done: Getting Results
+2016-09-02 15:50:25,783 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.723140495868
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 46
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.723140495868
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:50:25,783 INFO: Done: Result Analysis
+2016-09-02 15:50:25,952 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:50:25,952 DEBUG: Start: Training
+2016-09-02 15:50:25,962 DEBUG: Info: Time for Training: 0.332378864288[s]
+2016-09-02 15:50:25,962 DEBUG: Done: Training
+2016-09-02 15:50:25,962 DEBUG: Start: Predicting
+2016-09-02 15:50:25,966 DEBUG: Done: Predicting
+2016-09-02 15:50:25,966 DEBUG: Start: Getting Results
+2016-09-02 15:50:25,967 DEBUG: Done: Getting Results
+2016-09-02 15:50:25,967 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.946280991736
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 4, max_depth : 14
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.946280991736
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
+2016-09-02 15:50:25,967 INFO: Done: Result Analysis
+2016-09-02 15:50:26,074 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:50:26,074 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:50:26,074 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:50:26,074 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:50:26,075 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:50:26,075 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:50:26,075 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:50:26,075 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:50:26,075 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:50:26,075 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:50:26,076 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:50:26,076 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:50:26,076 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:50:26,076 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:50:26,204 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:50:26,204 DEBUG: Start: Training
+2016-09-02 15:50:26,207 DEBUG: Info: Time for Training: 0.133371114731[s]
+2016-09-02 15:50:26,207 DEBUG: Done: Training
+2016-09-02 15:50:26,207 DEBUG: Start: Predicting
+2016-09-02 15:50:26,219 DEBUG: Done: Predicting
+2016-09-02 15:50:26,220 DEBUG: Start: Getting Results
+2016-09-02 15:50:26,221 DEBUG: Done: Getting Results
+2016-09-02 15:50:26,221 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.681818181818
+accuracy_score on test : 0.657142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.681818181818
+ - Score on test : 0.657142857143
+
+
+ Classification took 0:00:00
+2016-09-02 15:50:26,221 INFO: Done: Result Analysis
+2016-09-02 15:50:26,236 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:50:26,236 DEBUG: Start: Training
+2016-09-02 15:50:26,284 DEBUG: Info: Time for Training: 0.210876941681[s]
+2016-09-02 15:50:26,285 DEBUG: Done: Training
+2016-09-02 15:50:26,285 DEBUG: Start: Predicting
+2016-09-02 15:50:26,294 DEBUG: Done: Predicting
+2016-09-02 15:50:26,295 DEBUG: Start: Getting Results
+2016-09-02 15:50:26,296 DEBUG: Done: Getting Results
+2016-09-02 15:50:26,296 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.636363636364
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3972
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.636363636364
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:00
+2016-09-02 15:50:26,296 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..895f2f1c0720f1d1f303f5e0af905fffe1c3f0a8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.771428571429
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2548317d11153f5533617f1bebde09836d4664df
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Decision Tree with max_depth : 14
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3f9adf417990ad7894d5b9842ac1e7e83c4f1a14
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.723140495868
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 46
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.723140495868
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..877e48a9bda563ecc493a52a8a6788a6a0dcfba8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155025Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.946280991736
+accuracy_score on test : 0.752380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 4, max_depth : 14
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.946280991736
+ - Score on test : 0.752380952381
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155026Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155026Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c2693ac8b6bce2977795ceffaf91e569da0ddcad
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155026Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.681818181818
+accuracy_score on test : 0.657142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.681818181818
+ - Score on test : 0.657142857143
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155026Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155026Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..704b75a5960cdb2bc0ae7dfa5e7dfb5ff7c1a681
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155026Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.636363636364
+accuracy_score on test : 0.561904761905
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3972
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.636363636364
+ - Score on test : 0.561904761905
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155057-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-155057-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..7f920d95cde2d58e911bb9ebbdbf6dff71c1e2e0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155057-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,247 @@
+2016-09-02 15:50:57,222 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:50:57,222 WARNING: This may use a lot of HDD storage space : 0.273145851562 Gbytes
+2016-09-02 15:50:59,058 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:50:59,059 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:50:59,106 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:50:59,106 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:50:59,107 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:50:59,107 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:50:59,107 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:50:59,107 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:50:59,108 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:50:59,108 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:50:59,108 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:50:59,108 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:50:59,108 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:50:59,108 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:50:59,108 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:50:59,108 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:50:59,201 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:50:59,201 DEBUG: Start: Training
+2016-09-02 15:50:59,205 DEBUG: Info: Time for Training: 0.0992250442505[s]
+2016-09-02 15:50:59,205 DEBUG: Done: Training
+2016-09-02 15:50:59,205 DEBUG: Start: Predicting
+2016-09-02 15:50:59,208 DEBUG: Done: Predicting
+2016-09-02 15:50:59,208 DEBUG: Start: Getting Results
+2016-09-02 15:50:59,210 DEBUG: Done: Getting Results
+2016-09-02 15:50:59,210 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:50:59,210 INFO: Done: Result Analysis
+2016-09-02 15:50:59,232 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:50:59,232 DEBUG: Start: Training
+2016-09-02 15:50:59,236 DEBUG: Info: Time for Training: 0.130702972412[s]
+2016-09-02 15:50:59,236 DEBUG: Done: Training
+2016-09-02 15:50:59,236 DEBUG: Start: Predicting
+2016-09-02 15:50:59,239 DEBUG: Done: Predicting
+2016-09-02 15:50:59,239 DEBUG: Start: Getting Results
+2016-09-02 15:50:59,240 DEBUG: Done: Getting Results
+2016-09-02 15:50:59,241 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:50:59,241 INFO: Done: Result Analysis
+2016-09-02 15:50:59,355 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:50:59,355 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:50:59,356 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:50:59,356 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:50:59,356 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:50:59,356 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:50:59,357 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:50:59,357 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:50:59,357 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:50:59,357 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:50:59,357 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:50:59,357 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:50:59,357 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:50:59,357 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:50:59,426 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:50:59,426 DEBUG: Start: Training
+2016-09-02 15:50:59,427 DEBUG: Info: Time for Training: 0.0725109577179[s]
+2016-09-02 15:50:59,427 DEBUG: Done: Training
+2016-09-02 15:50:59,427 DEBUG: Start: Predicting
+2016-09-02 15:50:59,451 DEBUG: Done: Predicting
+2016-09-02 15:50:59,452 DEBUG: Start: Getting Results
+2016-09-02 15:50:59,453 DEBUG: Done: Getting Results
+2016-09-02 15:50:59,453 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.714876033058
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.714876033058
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
+2016-09-02 15:50:59,453 INFO: Done: Result Analysis
+2016-09-02 15:50:59,933 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:50:59,934 DEBUG: Start: Training
+2016-09-02 15:50:59,994 DEBUG: Info: Time for Training: 0.638989925385[s]
+2016-09-02 15:50:59,994 DEBUG: Done: Training
+2016-09-02 15:50:59,994 DEBUG: Start: Predicting
+2016-09-02 15:51:00,001 DEBUG: Done: Predicting
+2016-09-02 15:51:00,002 DEBUG: Start: Getting Results
+2016-09-02 15:51:00,003 DEBUG: Done: Getting Results
+2016-09-02 15:51:00,003 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 24, 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 : 0.995867768595
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:00
+2016-09-02 15:51:00,003 INFO: Done: Result Analysis
+2016-09-02 15:51:00,112 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:51:00,112 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:51:00,112 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:51:00,112 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:51:00,113 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:51:00,113 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:51:00,114 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:51:00,114 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:51:00,114 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:51:00,114 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:51:00,114 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:51:00,114 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:51:00,114 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:51:00,114 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:51:00,225 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:51:00,225 DEBUG: Start: Training
+2016-09-02 15:51:00,227 DEBUG: Info: Time for Training: 0.116078853607[s]
+2016-09-02 15:51:00,227 DEBUG: Done: Training
+2016-09-02 15:51:00,227 DEBUG: Start: Predicting
+2016-09-02 15:51:00,232 DEBUG: Done: Predicting
+2016-09-02 15:51:00,233 DEBUG: Start: Getting Results
+2016-09-02 15:51:00,234 DEBUG: Done: Getting Results
+2016-09-02 15:51:00,234 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.714876033058
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.714876033058
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
+2016-09-02 15:51:00,234 INFO: Done: Result Analysis
+2016-09-02 15:51:00,254 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:51:00,254 DEBUG: Start: Training
+2016-09-02 15:51:00,304 DEBUG: Info: Time for Training: 0.193463087082[s]
+2016-09-02 15:51:00,304 DEBUG: Done: Training
+2016-09-02 15:51:00,304 DEBUG: Start: Predicting
+2016-09-02 15:51:00,314 DEBUG: Done: Predicting
+2016-09-02 15:51:00,315 DEBUG: Start: Getting Results
+2016-09-02 15:51:00,316 DEBUG: Done: Getting Results
+2016-09-02 15:51:00,316 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.623966942149
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3697
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.623966942149
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
+2016-09-02 15:51:00,316 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9e9ebfe1db8023122cf0bf0b96225e23da841e98
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..96c6ab5d9743e78a89307333ea7ce23e426de0f4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4b41d4270fb4dcc55b8bf413bd2a3cd1cbb7c948
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.714876033058
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.714876033058
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..20b5b028b47731646a16a83de448d186590abf38
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155059Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 0.995867768595
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 24, 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 : 0.995867768595
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155100Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155100Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..108a6fd544f971cde974b2953268fb3c88e39e64
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155100Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.714876033058
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.714876033058
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155100Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155100Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..086ce7ad180b9147d8bcdd9443244d07ebfe638e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155100Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.623966942149
+accuracy_score on test : 0.533333333333
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3697
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.623966942149
+ - Score on test : 0.533333333333
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155204-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-155204-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..ab0e61d6172ad260b97382be2d237eddef022ef5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155204-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,1592 @@
+2016-09-02 15:52:04,988 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 15:52:04,989 WARNING: This may use a lot of HDD storage space : 0.273145851562 Gbytes
+2016-09-02 15:52:06,464 DEBUG: Start: Creating datasets for multiprocessing
+2016-09-02 15:52:06,465 INFO: Start: Finding all available mono- & multiview algorithms
+2016-09-02 15:52:06,525 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:52:06,525 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:52:06,525 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:52:06,525 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:52:06,525 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:52:06,525 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:52:06,542 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:52:06,542 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:52:06,542 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:52:06,542 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:52:06,542 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:52:06,542 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:52:06,542 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:52:06,542 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:52:22,779 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:52:22,779 DEBUG: Start: Training
+2016-09-02 15:52:23,110 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:52:23,110 DEBUG: Start: Training
+2016-09-02 15:52:25,134 DEBUG: Info: Time for Training: 18.6244180202[s]
+2016-09-02 15:52:25,134 DEBUG: Done: Training
+2016-09-02 15:52:25,134 DEBUG: Start: Predicting
+2016-09-02 15:52:25,142 DEBUG: Done: Predicting
+2016-09-02 15:52:25,142 DEBUG: Start: Getting Results
+2016-09-02 15:52:25,143 DEBUG: Done: Getting Results
+2016-09-02 15:52:25,143 INFO: Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:18
+2016-09-02 15:52:25,143 INFO: Done: Result Analysis
+2016-09-02 15:52:25,450 DEBUG: Info: Time for Training: 18.9407770634[s]
+2016-09-02 15:52:25,450 DEBUG: Done: Training
+2016-09-02 15:52:25,450 DEBUG: Start: Predicting
+2016-09-02 15:52:25,464 DEBUG: Done: Predicting
+2016-09-02 15:52:25,464 DEBUG: Start: Getting Results
+2016-09-02 15:52:25,466 DEBUG: Done: Getting Results
+2016-09-02 15:52:25,466 INFO: Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.809523809524
+
+
+ Classification took 0:00:18
+2016-09-02 15:52:25,466 INFO: Done: Result Analysis
+2016-09-02 15:52:25,622 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:52:25,622 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:52:25,622 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:52:25,622 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:52:25,622 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:52:25,622 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:52:25,639 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:52:25,639 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:52:25,639 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:52:25,639 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:52:25,639 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:52:25,639 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:52:25,639 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:52:25,640 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:52:27,465 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:52:27,465 DEBUG: Start: Training
+2016-09-02 15:52:27,767 DEBUG: Info: Time for Training: 2.16040992737[s]
+2016-09-02 15:52:27,768 DEBUG: Done: Training
+2016-09-02 15:52:27,768 DEBUG: Start: Predicting
+2016-09-02 15:52:27,782 DEBUG: Done: Predicting
+2016-09-02 15:52:27,782 DEBUG: Start: Getting Results
+2016-09-02 15:52:27,783 DEBUG: Done: Getting Results
+2016-09-02 15:52:27,783 INFO: Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.87619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, 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 : 0.87619047619
+
+
+ Classification took 0:00:02
+2016-09-02 15:52:27,784 INFO: Done: Result Analysis
+2016-09-02 15:52:30,714 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:52:30,714 DEBUG: Start: Training
+2016-09-02 15:52:30,864 DEBUG: Info: Time for Training: 5.25712299347[s]
+2016-09-02 15:52:30,864 DEBUG: Done: Training
+2016-09-02 15:52:30,864 DEBUG: Start: Predicting
+2016-09-02 15:52:34,985 DEBUG: Done: Predicting
+2016-09-02 15:52:34,985 DEBUG: Start: Getting Results
+2016-09-02 15:52:34,986 DEBUG: Done: Getting Results
+2016-09-02 15:52:34,986 INFO: Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.900826446281
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.900826446281
+ - Score on test : 0.857142857143
+
+
+ Classification took 0:00:05
+2016-09-02 15:52:34,987 INFO: Done: Result Analysis
+2016-09-02 15:52:35,097 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:52:35,097 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:52:35,097 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:52:35,097 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:52:35,098 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:52:35,098 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:52:35,121 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:52:35,121 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:52:35,122 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:52:35,122 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:52:35,122 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:52:35,122 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:52:35,122 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:52:35,122 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:52:37,575 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:52:37,575 DEBUG: Start: Training
+2016-09-02 15:52:37,965 DEBUG: Info: Time for Training: 2.88462495804[s]
+2016-09-02 15:52:37,965 DEBUG: Done: Training
+2016-09-02 15:52:37,966 DEBUG: Start: Predicting
+2016-09-02 15:52:38,011 DEBUG: Done: Predicting
+2016-09-02 15:52:38,011 DEBUG: Start: Getting Results
+2016-09-02 15:52:38,013 DEBUG: Done: Getting Results
+2016-09-02 15:52:38,013 INFO: Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.933884297521
+accuracy_score on test : 0.885714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.933884297521
+ - Score on test : 0.885714285714
+
+
+ Classification took 0:00:02
+2016-09-02 15:52:38,030 INFO: Done: Result Analysis
+2016-09-02 15:52:43,430 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:52:43,431 DEBUG: Start: Training
+2016-09-02 15:52:47,160 DEBUG: Info: Time for Training: 12.0795547962[s]
+2016-09-02 15:52:47,160 DEBUG: Done: Training
+2016-09-02 15:52:47,160 DEBUG: Start: Predicting
+2016-09-02 15:52:48,217 DEBUG: Done: Predicting
+2016-09-02 15:52:48,217 DEBUG: Start: Getting Results
+2016-09-02 15:52:48,218 DEBUG: Done: Getting Results
+2016-09-02 15:52:48,218 INFO: Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:12
+2016-09-02 15:52:48,218 INFO: Done: Result Analysis
+2016-09-02 15:52:48,375 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:52:48,375 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:52:48,375 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:52:48,375 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:52:48,376 DEBUG: ### Classification - Database:MultiOmic Feature:Methyl train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:52:48,376 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:52:48,393 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:52:48,393 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
+2016-09-02 15:52:48,393 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:52:48,393 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
+2016-09-02 15:52:48,393 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:52:48,393 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:52:48,393 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:52:48,393 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:52:57,452 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:52:57,452 DEBUG: Start: Training
+2016-09-02 15:52:59,603 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:52:59,603 DEBUG: Start: Training
+2016-09-02 15:53:02,806 DEBUG: Info: Time for Training: 14.4469108582[s]
+2016-09-02 15:53:02,807 DEBUG: Done: Training
+2016-09-02 15:53:02,807 DEBUG: Start: Predicting
+2016-09-02 15:53:04,217 DEBUG: Done: Predicting
+2016-09-02 15:53:04,217 DEBUG: Start: Getting Results
+2016-09-02 15:53:04,218 DEBUG: Done: Getting Results
+2016-09-02 15:53:04,218 INFO: Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:14
+2016-09-02 15:53:04,218 INFO: Done: Result Analysis
+2016-09-02 15:53:05,238 DEBUG: Info: Time for Training: 16.8788430691[s]
+2016-09-02 15:53:05,238 DEBUG: Done: Training
+2016-09-02 15:53:05,238 DEBUG: Start: Predicting
+2016-09-02 15:53:06,571 DEBUG: Done: Predicting
+2016-09-02 15:53:06,571 DEBUG: Start: Getting Results
+2016-09-02 15:53:06,572 DEBUG: Done: Getting Results
+2016-09-02 15:53:06,573 INFO: Classification on MultiOmic database for Methyl with SVMPoly
+
+accuracy_score on train : 0.702479338843
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.702479338843
+ - Score on test : 0.8
+
+
+ Classification took 0:00:16
+2016-09-02 15:53:06,573 INFO: Done: Result Analysis
+2016-09-02 15:53:06,653 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:53:06,653 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:53:06,653 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:53:06,654 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:53:06,654 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:53:06,654 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:53:06,655 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:53:06,655 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:53:06,656 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:53:06,656 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:53:06,656 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:53:06,656 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:53:06,656 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:53:06,656 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:53:07,092 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:53:07,092 DEBUG: Start: Training
+2016-09-02 15:53:07,122 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:53:07,122 DEBUG: Start: Training
+2016-09-02 15:53:07,143 DEBUG: Info: Time for Training: 0.491369009018[s]
+2016-09-02 15:53:07,144 DEBUG: Done: Training
+2016-09-02 15:53:07,144 DEBUG: Start: Predicting
+2016-09-02 15:53:07,146 DEBUG: Done: Predicting
+2016-09-02 15:53:07,146 DEBUG: Start: Getting Results
+2016-09-02 15:53:07,147 DEBUG: Done: Getting Results
+2016-09-02 15:53:07,148 INFO: Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:53:07,148 INFO: Done: Result Analysis
+2016-09-02 15:53:07,176 DEBUG: Info: Time for Training: 0.524960041046[s]
+2016-09-02 15:53:07,177 DEBUG: Done: Training
+2016-09-02 15:53:07,177 DEBUG: Start: Predicting
+2016-09-02 15:53:07,180 DEBUG: Done: Predicting
+2016-09-02 15:53:07,180 DEBUG: Start: Getting Results
+2016-09-02 15:53:07,182 DEBUG: Done: Getting Results
+2016-09-02 15:53:07,182 INFO: Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:53:07,182 INFO: Done: Result Analysis
+2016-09-02 15:53:07,303 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:53:07,303 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:53:07,303 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:53:07,303 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:53:07,303 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:53:07,303 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:53:07,305 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:53:07,305 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:53:07,305 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:53:07,305 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:53:07,306 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:53:07,306 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:53:07,306 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:53:07,306 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:53:07,520 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:53:07,520 DEBUG: Start: Training
+2016-09-02 15:53:07,525 DEBUG: Info: Time for Training: 0.223237037659[s]
+2016-09-02 15:53:07,525 DEBUG: Done: Training
+2016-09-02 15:53:07,525 DEBUG: Start: Predicting
+2016-09-02 15:53:07,670 DEBUG: Done: Predicting
+2016-09-02 15:53:07,670 DEBUG: Start: Getting Results
+2016-09-02 15:53:07,671 DEBUG: Done: Getting Results
+2016-09-02 15:53:07,671 INFO: Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.743801652893
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.743801652893
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
+2016-09-02 15:53:07,671 INFO: Done: Result Analysis
+2016-09-02 15:53:07,940 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:53:07,940 DEBUG: Start: Training
+2016-09-02 15:53:08,027 DEBUG: Info: Time for Training: 0.72517991066[s]
+2016-09-02 15:53:08,027 DEBUG: Done: Training
+2016-09-02 15:53:08,027 DEBUG: Start: Predicting
+2016-09-02 15:53:08,035 DEBUG: Done: Predicting
+2016-09-02 15:53:08,035 DEBUG: Start: Getting Results
+2016-09-02 15:53:08,036 DEBUG: Done: Getting Results
+2016-09-02 15:53:08,036 INFO: Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, 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 : 0.847619047619
+
+
+ Classification took 0:00:00
+2016-09-02 15:53:08,036 INFO: Done: Result Analysis
+2016-09-02 15:53:08,154 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:53:08,154 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:53:08,155 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:53:08,155 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:53:08,155 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:53:08,155 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:53:08,158 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:53:08,158 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:53:08,158 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:53:08,158 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:53:08,158 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:53:08,158 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:53:08,158 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:53:08,158 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:53:08,355 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:53:08,356 DEBUG: Start: Training
+2016-09-02 15:53:08,361 DEBUG: Info: Time for Training: 0.207911968231[s]
+2016-09-02 15:53:08,361 DEBUG: Done: Training
+2016-09-02 15:53:08,361 DEBUG: Start: Predicting
+2016-09-02 15:53:08,365 DEBUG: Done: Predicting
+2016-09-02 15:53:08,365 DEBUG: Start: Getting Results
+2016-09-02 15:53:08,367 DEBUG: Done: Getting Results
+2016-09-02 15:53:08,367 INFO: Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.818181818182
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.818181818182
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:00
+2016-09-02 15:53:08,367 INFO: Done: Result Analysis
+2016-09-02 15:53:08,544 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:53:08,544 DEBUG: Start: Training
+2016-09-02 15:53:08,703 DEBUG: Info: Time for Training: 0.550046920776[s]
+2016-09-02 15:53:08,703 DEBUG: Done: Training
+2016-09-02 15:53:08,703 DEBUG: Start: Predicting
+2016-09-02 15:53:08,740 DEBUG: Done: Predicting
+2016-09-02 15:53:08,740 DEBUG: Start: Getting Results
+2016-09-02 15:53:08,741 DEBUG: Done: Getting Results
+2016-09-02 15:53:08,741 INFO: Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.876033057851
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.876033057851
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
+2016-09-02 15:53:08,741 INFO: Done: Result Analysis
+2016-09-02 15:53:08,894 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:53:08,894 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:53:08,894 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:53:08,894 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:53:08,895 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:53:08,895 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:53:08,898 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:53:08,898 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
+2016-09-02 15:53:08,898 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:53:08,898 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
+2016-09-02 15:53:08,898 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:53:08,898 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:53:08,898 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:53:08,898 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:53:08,974 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:53:08,975 DEBUG: Start: Training
+2016-09-02 15:53:08,980 DEBUG: Info: Time for Training: 0.0867691040039[s]
+2016-09-02 15:53:08,980 DEBUG: Done: Training
+2016-09-02 15:53:08,980 DEBUG: Start: Predicting
+2016-09-02 15:53:08,984 DEBUG: Done: Predicting
+2016-09-02 15:53:08,984 DEBUG: Start: Getting Results
+2016-09-02 15:53:08,985 DEBUG: Done: Getting Results
+2016-09-02 15:53:08,985 INFO: Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.297520661157
+accuracy_score on test : 0.2
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.297520661157
+ - Score on test : 0.2
+
+
+ Classification took 0:00:00
+2016-09-02 15:53:08,985 INFO: Done: Result Analysis
+2016-09-02 15:53:09,527 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:53:09,527 DEBUG: Start: Training
+2016-09-02 15:53:09,849 DEBUG: Info: Time for Training: 0.95553612709[s]
+2016-09-02 15:53:09,849 DEBUG: Done: Training
+2016-09-02 15:53:09,849 DEBUG: Start: Predicting
+2016-09-02 15:53:09,940 DEBUG: Done: Predicting
+2016-09-02 15:53:09,940 DEBUG: Start: Getting Results
+2016-09-02 15:53:09,941 DEBUG: Done: Getting Results
+2016-09-02 15:53:09,941 INFO: Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:53:09,941 INFO: Done: Result Analysis
+2016-09-02 15:53:10,089 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:53:10,089 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:53:10,089 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:53:10,089 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:53:10,089 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:53:10,089 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:53:10,182 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:53:10,193 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:53:10,830 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:53:10,830 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:53:10,830 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:53:10,830 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:53:10,830 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:53:10,830 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:54:17,241 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:54:17,241 DEBUG: Start: Training
+2016-09-02 15:54:19,501 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:54:19,501 DEBUG: Start: Training
+2016-09-02 15:54:25,855 DEBUG: Info: Time for Training: 75.8064689636[s]
+2016-09-02 15:54:25,855 DEBUG: Done: Training
+2016-09-02 15:54:25,855 DEBUG: Start: Predicting
+2016-09-02 15:54:25,938 DEBUG: Done: Predicting
+2016-09-02 15:54:25,938 DEBUG: Start: Getting Results
+2016-09-02 15:54:25,939 DEBUG: Done: Getting Results
+2016-09-02 15:54:25,939 INFO: Classification on MultiOmic database for RANSeq with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.72380952381
+
+
+ Classification took 0:01:15
+2016-09-02 15:54:25,944 INFO: Done: Result Analysis
+2016-09-02 15:54:28,021 DEBUG: Info: Time for Training: 77.9725730419[s]
+2016-09-02 15:54:28,021 DEBUG: Done: Training
+2016-09-02 15:54:28,021 DEBUG: Start: Predicting
+2016-09-02 15:54:28,054 DEBUG: Done: Predicting
+2016-09-02 15:54:28,055 DEBUG: Start: Getting Results
+2016-09-02 15:54:28,056 DEBUG: Done: Getting Results
+2016-09-02 15:54:28,056 INFO: Classification on MultiOmic database for RANSeq with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.72380952381
+
+
+ Classification took 0:01:17
+2016-09-02 15:54:28,056 INFO: Done: Result Analysis
+2016-09-02 15:54:28,381 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:54:28,381 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:54:28,381 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:54:28,404 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:54:28,405 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:54:28,405 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:54:28,425 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:54:28,425 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:54:28,425 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:54:28,426 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:54:28,457 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:54:28,457 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:54:28,457 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:54:28,457 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:54:31,462 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:54:31,462 DEBUG: Start: Training
+2016-09-02 15:54:31,508 DEBUG: Info: Time for Training: 3.31430196762[s]
+2016-09-02 15:54:31,508 DEBUG: Done: Training
+2016-09-02 15:54:31,508 DEBUG: Start: Predicting
+2016-09-02 15:54:31,529 DEBUG: Done: Predicting
+2016-09-02 15:54:31,529 DEBUG: Start: Getting Results
+2016-09-02 15:54:31,530 DEBUG: Done: Getting Results
+2016-09-02 15:54:31,530 INFO: Classification on MultiOmic database for RANSeq with RandomForest
+
+accuracy_score on train : 0.747933884298
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 6, 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 : 0.747933884298
+ - Score on test : 0.8
+
+
+ Classification took 0:00:03
+2016-09-02 15:54:31,531 INFO: Done: Result Analysis
+2016-09-02 15:54:44,395 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:54:44,395 DEBUG: Start: Training
+2016-09-02 15:54:44,995 DEBUG: Info: Time for Training: 16.7879331112[s]
+2016-09-02 15:54:44,996 DEBUG: Done: Training
+2016-09-02 15:54:44,996 DEBUG: Start: Predicting
+2016-09-02 15:54:56,615 DEBUG: Done: Predicting
+2016-09-02 15:54:56,615 DEBUG: Start: Getting Results
+2016-09-02 15:54:56,616 DEBUG: Done: Getting Results
+2016-09-02 15:54:56,616 INFO: Classification on MultiOmic database for RANSeq with KNN
+
+accuracy_score on train : 0.694214876033
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.694214876033
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:16
+2016-09-02 15:54:56,616 INFO: Done: Result Analysis
+2016-09-02 15:54:56,738 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:54:56,738 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:54:56,738 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:54:56,768 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:54:56,768 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:54:56,768 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:54:56,786 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:54:56,786 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:54:56,786 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:54:56,787 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:54:56,819 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:54:56,819 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:54:56,819 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:54:56,819 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:55:06,213 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:55:06,213 DEBUG: Start: Training
+2016-09-02 15:55:06,619 DEBUG: Info: Time for Training: 9.93699789047[s]
+2016-09-02 15:55:06,619 DEBUG: Done: Training
+2016-09-02 15:55:06,619 DEBUG: Start: Predicting
+2016-09-02 15:55:06,802 DEBUG: Done: Predicting
+2016-09-02 15:55:06,802 DEBUG: Start: Getting Results
+2016-09-02 15:55:06,804 DEBUG: Done: Getting Results
+2016-09-02 15:55:06,804 INFO: Classification on MultiOmic database for RANSeq with SGD
+
+accuracy_score on train : 0.735537190083
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.735537190083
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:09
+2016-09-02 15:55:06,819 INFO: Done: Result Analysis
+2016-09-02 15:55:34,060 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:55:34,060 DEBUG: Start: Training
+2016-09-02 15:55:53,121 DEBUG: Info: Time for Training: 56.438382864[s]
+2016-09-02 15:55:53,121 DEBUG: Done: Training
+2016-09-02 15:55:53,121 DEBUG: Start: Predicting
+2016-09-02 15:55:58,532 DEBUG: Done: Predicting
+2016-09-02 15:55:58,533 DEBUG: Start: Getting Results
+2016-09-02 15:55:58,534 DEBUG: Done: Getting Results
+2016-09-02 15:55:58,534 INFO: Classification on MultiOmic database for RANSeq with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.590476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.590476190476
+
+
+ Classification took 0:00:56
+2016-09-02 15:55:58,542 INFO: Done: Result Analysis
+2016-09-02 15:55:59,569 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:55:59,569 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2016-09-02 15:55:59,569 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:55:59,607 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:55:59,607 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:55:59,607 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:55:59,607 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:55:59,610 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:55:59,610 DEBUG: ### Classification - Database:MultiOmic Feature:RANSeq train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2016-09-02 15:55:59,610 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:55:59,668 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
+2016-09-02 15:55:59,668 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
+2016-09-02 15:55:59,668 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:55:59,668 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:56:01,855 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:56:01,855 DEBUG: Start: Training
+2016-09-02 15:56:02,335 DEBUG: Info: Time for Training: 3.65623283386[s]
+2016-09-02 15:56:02,335 DEBUG: Done: Training
+2016-09-02 15:56:02,336 DEBUG: Start: Predicting
+2016-09-02 15:56:02,544 DEBUG: Done: Predicting
+2016-09-02 15:56:02,544 DEBUG: Start: Getting Results
+2016-09-02 15:56:02,545 DEBUG: Done: Getting Results
+2016-09-02 15:56:02,545 INFO: Classification on MultiOmic database for RANSeq with SVMPoly
+
+accuracy_score on train : 0.297520661157
+accuracy_score on test : 0.2
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.297520661157
+ - Score on test : 0.2
+
+
+ Classification took 0:00:03
+2016-09-02 15:56:02,559 INFO: Done: Result Analysis
+2016-09-02 15:56:42,983 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:56:42,983 DEBUG: Start: Training
+2016-09-02 15:57:05,276 DEBUG: Info: Time for Training: 66.5965871811[s]
+2016-09-02 15:57:05,276 DEBUG: Done: Training
+2016-09-02 15:57:05,276 DEBUG: Start: Predicting
+2016-09-02 15:57:11,928 DEBUG: Done: Predicting
+2016-09-02 15:57:11,929 DEBUG: Start: Getting Results
+2016-09-02 15:57:11,929 DEBUG: Done: Getting Results
+2016-09-02 15:57:11,930 INFO: Classification on MultiOmic database for RANSeq with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.8
+
+
+ Classification took 0:01:06
+2016-09-02 15:57:11,930 INFO: Done: Result Analysis
+2016-09-02 15:57:12,018 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:57:12,018 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2016-09-02 15:57:12,019 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:57:12,020 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:57:12,020 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:57:12,020 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:57:12,021 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:57:12,029 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:57:12,029 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2016-09-02 15:57:12,029 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:57:12,030 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:57:12,030 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:57:12,030 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:57:12,030 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:57:12,088 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:57:12,088 DEBUG: Start: Training
+2016-09-02 15:57:12,090 DEBUG: Info: Time for Training: 0.0908818244934[s]
+2016-09-02 15:57:12,091 DEBUG: Done: Training
+2016-09-02 15:57:12,091 DEBUG: Start: Predicting
+2016-09-02 15:57:12,093 DEBUG: Done: Predicting
+2016-09-02 15:57:12,093 DEBUG: Start: Getting Results
+2016-09-02 15:57:12,095 DEBUG: Done: Getting Results
+2016-09-02 15:57:12,095 INFO: Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.857142857143
+
+
+ Classification took 0:00:00
+2016-09-02 15:57:12,106 INFO: Done: Result Analysis
+2016-09-02 15:57:12,119 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:57:12,119 DEBUG: Start: Training
+2016-09-02 15:57:12,124 DEBUG: Info: Time for Training: 0.124305009842[s]
+2016-09-02 15:57:12,124 DEBUG: Done: Training
+2016-09-02 15:57:12,124 DEBUG: Start: Predicting
+2016-09-02 15:57:12,127 DEBUG: Done: Predicting
+2016-09-02 15:57:12,127 DEBUG: Start: Getting Results
+2016-09-02 15:57:12,129 DEBUG: Done: Getting Results
+2016-09-02 15:57:12,129 INFO: Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.857142857143
+
+
+ Classification took 0:00:00
+2016-09-02 15:57:12,129 INFO: Done: Result Analysis
+2016-09-02 15:57:12,249 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:57:12,249 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:57:12,250 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2016-09-02 15:57:12,250 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2016-09-02 15:57:12,250 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:57:12,250 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:57:12,251 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:57:12,251 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:57:12,251 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:57:12,251 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:57:12,251 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:57:12,251 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:57:12,252 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:57:12,252 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:57:12,332 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:57:12,332 DEBUG: Start: Training
+2016-09-02 15:57:12,333 DEBUG: Info: Time for Training: 0.0839469432831[s]
+2016-09-02 15:57:12,333 DEBUG: Done: Training
+2016-09-02 15:57:12,333 DEBUG: Start: Predicting
+2016-09-02 15:57:12,353 DEBUG: Done: Predicting
+2016-09-02 15:57:12,353 DEBUG: Start: Getting Results
+2016-09-02 15:57:12,354 DEBUG: Done: Getting Results
+2016-09-02 15:57:12,355 INFO: Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.702479338843
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.702479338843
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:57:12,355 INFO: Done: Result Analysis
+2016-09-02 15:57:12,767 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:57:12,768 DEBUG: Start: Training
+2016-09-02 15:57:12,833 DEBUG: Info: Time for Training: 0.583716869354[s]
+2016-09-02 15:57:12,833 DEBUG: Done: Training
+2016-09-02 15:57:12,833 DEBUG: Start: Predicting
+2016-09-02 15:57:12,840 DEBUG: Done: Predicting
+2016-09-02 15:57:12,840 DEBUG: Start: Getting Results
+2016-09-02 15:57:12,841 DEBUG: Done: Getting Results
+2016-09-02 15:57:12,841 INFO: Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, 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 : 0.857142857143
+
+
+ Classification took 0:00:00
+2016-09-02 15:57:12,842 INFO: Done: Result Analysis
+2016-09-02 15:57:12,902 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:57:12,902 DEBUG: ### Main Programm for Classification MonoView
+2016-09-02 15:57:12,902 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2016-09-02 15:57:12,902 DEBUG: ### Classification - Database:MultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2016-09-02 15:57:12,903 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:57:12,903 DEBUG: Start: Determine Train/Test split
+2016-09-02 15:57:12,904 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:57:12,904 DEBUG: Info: Shape X_train:(242, 127), Length of y_train:242
+2016-09-02 15:57:12,904 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:57:12,904 DEBUG: Info: Shape X_test:(105, 127), Length of y_test:105
+2016-09-02 15:57:12,904 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:57:12,904 DEBUG: Done: Determine Train/Test split
+2016-09-02 15:57:12,904 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:57:12,905 DEBUG: Start: RandomSearch best settings with 2 iterations
+2016-09-02 15:57:13,007 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:57:13,007 DEBUG: Start: Training
+2016-09-02 15:57:13,008 DEBUG: Info: Time for Training: 0.10768198967[s]
+2016-09-02 15:57:13,008 DEBUG: Done: Training
+2016-09-02 15:57:13,008 DEBUG: Start: Predicting
+2016-09-02 15:57:13,021 DEBUG: Done: Predicting
+2016-09-02 15:57:13,021 DEBUG: Start: Getting Results
+2016-09-02 15:57:13,022 DEBUG: Done: Getting Results
+2016-09-02 15:57:13,022 INFO: Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.702479338843
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.702479338843
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
+2016-09-02 15:57:13,023 INFO: Done: Result Analysis
+2016-09-02 15:57:13,049 DEBUG: Done: RandomSearch best settings
+2016-09-02 15:57:13,049 DEBUG: Start: Training
+2016-09-02 15:57:13,118 DEBUG: Info: Time for Training: 0.217166185379[s]
+2016-09-02 15:57:13,118 DEBUG: Done: Training
+2016-09-02 15:57:13,118 DEBUG: Start: Predicting
+2016-09-02 15:57:13,132 DEBUG: Done: Predicting
+2016-09-02 15:57:13,132 DEBUG: Start: Getting Results
+2016-09-02 15:57:13,134 DEBUG: Done: Getting Results
+2016-09-02 15:57:13,134 INFO: Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.615702479339
+accuracy_score on test : 0.571428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.615702479339
+ - Score on test : 0.571428571429
+
+
+ Classification took 0:00:00
+2016-09-02 15:57:13,134 INFO: Done: Result Analysis
+2016-09-02 15:57:13,389 INFO: ### Main Programm for Multiview Classification
+2016-09-02 15:57:13,389 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Mumbo ; Cores : 1
+2016-09-02 15:57:13,389 INFO: Info: Shape of Methyl :(347, 25978)
+2016-09-02 15:57:13,389 INFO: ### Main Programm for Multiview Classification
+2016-09-02 15:57:13,390 INFO: ### Classification - Database : MultiOmic ; Views : Methyl, MiRNA_, RNASeq, Clinic ; Algorithm : Fusion ; Cores : 1
+2016-09-02 15:57:13,390 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-09-02 15:57:13,390 INFO: Info: Shape of Methyl :(347, 25978)
+2016-09-02 15:57:13,390 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-09-02 15:57:13,390 INFO: Info: Shape of MiRNA_ :(347, 1046)
+2016-09-02 15:57:13,391 INFO: Info: Shape of Clinic :(347, 127)
+2016-09-02 15:57:13,391 INFO: Done: Read Database Files
+2016-09-02 15:57:13,391 INFO: Start: Determine validation split for ratio 0.7
+2016-09-02 15:57:13,391 INFO: Info: Shape of RANSeq :(347, 73599)
+2016-09-02 15:57:13,391 INFO: Info: Shape of Clinic :(347, 127)
+2016-09-02 15:57:13,391 INFO: Done: Read Database Files
+2016-09-02 15:57:13,392 INFO: Start: Determine validation split for ratio 0.7
+2016-09-02 15:57:13,395 INFO: Done: Determine validation split
+2016-09-02 15:57:13,395 INFO: Start: Determine 5 folds
+2016-09-02 15:57:13,396 INFO: Done: Determine validation split
+2016-09-02 15:57:13,396 INFO: Start: Determine 5 folds
+2016-09-02 15:57:13,403 INFO: Info: Length of Learning Sets: 196
+2016-09-02 15:57:13,403 INFO: Info: Length of Testing Sets: 48
+2016-09-02 15:57:13,403 INFO: Info: Length of Validation Set: 103
+2016-09-02 15:57:13,404 INFO: Done: Determine folds
+2016-09-02 15:57:13,404 INFO: Start: Learning with Mumbo and 5 folds
+2016-09-02 15:57:13,404 INFO: Start: Classification
+2016-09-02 15:57:13,404 INFO: Start: Fold number 1
+2016-09-02 15:57:13,404 INFO: Info: Length of Learning Sets: 196
+2016-09-02 15:57:13,404 INFO: Info: Length of Testing Sets: 48
+2016-09-02 15:57:13,404 INFO: Info: Length of Validation Set: 103
+2016-09-02 15:57:13,404 INFO: Done: Determine folds
+2016-09-02 15:57:13,404 INFO: Start: Learning with Fusion and 5 folds
+2016-09-02 15:57:13,404 INFO: Start: Classification
+2016-09-02 15:57:13,405 INFO: Start: Fold number 1
+2016-09-02 15:57:14,371 INFO: Start: Classification
+2016-09-02 15:57:14,718 INFO: Done: Fold number 1
+2016-09-02 15:57:14,718 INFO: Start: Fold number 2
+2016-09-02 15:57:15,193 INFO: Start: Classification
+2016-09-02 15:57:15,382 INFO: Done: Fold number 2
+2016-09-02 15:57:15,383 INFO: Start: Fold number 3
+2016-09-02 15:57:15,722 DEBUG: Start: Iteration 1
+2016-09-02 15:57:15,843 INFO: Start: Classification
+2016-09-02 15:57:16,034 INFO: Done: Fold number 3
+2016-09-02 15:57:16,034 INFO: Start: Fold number 4
+2016-09-02 15:57:16,623 INFO: Start: Classification
+2016-09-02 15:57:16,814 INFO: Done: Fold number 4
+2016-09-02 15:57:16,814 INFO: Start: Fold number 5
+2016-09-02 15:57:17,246 DEBUG: View 0 : 0.634615384615
+2016-09-02 15:57:17,308 DEBUG: View 1 : 0.557692307692
+2016-09-02 15:57:17,381 INFO: Start: Classification
+2016-09-02 15:57:17,567 INFO: Done: Fold number 5
+2016-09-02 15:57:17,568 INFO: Done: Classification
+2016-09-02 15:57:17,568 INFO: Info: Time for Classification: 4[s]
+2016-09-02 15:57:17,568 INFO: Start: Result Analysis for Fusion
+2016-09-02 15:57:17,574 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy :
+ -On Train : 99.5173366428
+ -On Test : 89.1666666667
+ -On Validation : 97.6699029126
+
+Dataset info :
+ -Database name : MultiOmic
+ -Labels : No, Yes
+ -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
+ - Random Forest with num_esimators : 26, max_depth : 29
+ - Random Forest with num_esimators : 6, max_depth : 1
+ - 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')
+
+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 0:00:03 0:00:00
+ Total 0:00:12 0:00:01
+ So a total classification time of 0:00:04.
+
+
+2016-09-02 15:57:17,575 INFO: Done: Result Analysis
+2016-09-02 15:57:19,041 DEBUG: View 2 : 0.581730769231
+2016-09-02 15:57:19,050 DEBUG: View 3 : 0.591346153846
+2016-09-02 15:57:19,098 DEBUG: Best view : RANSeq
+2016-09-02 15:57:19,212 DEBUG: Start: Iteration 2
+2016-09-02 15:57:20,267 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:57:20,302 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:57:23,146 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:57:23,156 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:57:23,217 DEBUG: Best view : Methyl
+2016-09-02 15:57:23,414 DEBUG: Start: Iteration 3
+2016-09-02 15:57:24,471 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:57:24,506 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:57:27,357 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:57:27,365 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:57:27,427 DEBUG: Best view : Methyl
+2016-09-02 15:57:27,707 DEBUG: Start: Iteration 4
+2016-09-02 15:57:28,780 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:57:28,814 DEBUG: View 1 : 0.956730769231
+2016-09-02 15:57:31,630 DEBUG: View 2 : 0.870192307692
+2016-09-02 15:57:31,638 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:57:31,702 DEBUG: Best view : Methyl
+2016-09-02 15:57:32,070 DEBUG: Start: Iteration 5
+2016-09-02 15:57:33,147 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:57:33,181 DEBUG: View 1 : 0.956730769231
+2016-09-02 15:57:35,988 DEBUG: View 2 : 0.870192307692
+2016-09-02 15:57:35,997 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:57:36,064 DEBUG: Best view : Methyl
+2016-09-02 15:57:36,507 DEBUG: Start: Iteration 6
+2016-09-02 15:57:37,595 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:57:37,629 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:57:40,438 DEBUG: View 2 : 0.860576923077
+2016-09-02 15:57:40,446 DEBUG: View 3 : 0.735576923077
+2016-09-02 15:57:40,516 DEBUG: Best view : Methyl
+2016-09-02 15:57:41,040 DEBUG: Start: Iteration 7
+2016-09-02 15:57:42,105 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:57:42,140 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:57:44,945 DEBUG: View 2 : 0.903846153846
+2016-09-02 15:57:44,953 DEBUG: View 3 : 0.735576923077
+2016-09-02 15:57:45,024 DEBUG: Best view : Methyl
+2016-09-02 15:57:45,631 DEBUG: Start: Iteration 8
+2016-09-02 15:57:46,684 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:57:46,718 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:57:49,592 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:57:49,600 DEBUG: View 3 : 0.735576923077
+2016-09-02 15:57:49,673 DEBUG: Best view : Methyl
+2016-09-02 15:57:50,362 DEBUG: Start: Iteration 9
+2016-09-02 15:57:51,421 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:57:51,456 DEBUG: View 1 : 0.951923076923
+2016-09-02 15:57:54,269 DEBUG: View 2 : 0.884615384615
+2016-09-02 15:57:54,278 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:57:54,354 DEBUG: Best view : Methyl
+2016-09-02 15:57:55,129 DEBUG: Start: Iteration 10
+2016-09-02 15:57:56,193 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:57:56,228 DEBUG: View 1 : 0.951923076923
+2016-09-02 15:57:59,070 DEBUG: View 2 : 0.903846153846
+2016-09-02 15:57:59,078 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:57:59,156 DEBUG: Best view : Methyl
+2016-09-02 15:58:00,015 DEBUG: Start: Iteration 11
+2016-09-02 15:58:01,071 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:01,105 DEBUG: View 1 : 0.951923076923
+2016-09-02 15:58:03,944 DEBUG: View 2 : 0.846153846154
+2016-09-02 15:58:03,954 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:58:04,042 DEBUG: Best view : Methyl
+2016-09-02 15:58:04,979 DEBUG: Start: Iteration 12
+2016-09-02 15:58:06,034 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:06,068 DEBUG: View 1 : 0.951923076923
+2016-09-02 15:58:08,874 DEBUG: View 2 : 0.846153846154
+2016-09-02 15:58:08,882 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:58:08,964 DEBUG: Best view : Methyl
+2016-09-02 15:58:09,982 DEBUG: Start: Iteration 13
+2016-09-02 15:58:11,065 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:11,099 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:58:13,912 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:58:13,920 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:58:14,005 DEBUG: Best view : Methyl
+2016-09-02 15:58:15,106 DEBUG: Start: Iteration 14
+2016-09-02 15:58:16,165 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:16,200 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:58:19,010 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:58:19,018 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:58:19,106 DEBUG: Best view : Methyl
+2016-09-02 15:58:20,300 DEBUG: Start: Iteration 15
+2016-09-02 15:58:21,358 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:21,392 DEBUG: View 1 : 0.932692307692
+2016-09-02 15:58:24,249 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:58:24,257 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:58:24,351 DEBUG: Best view : Methyl
+2016-09-02 15:58:25,615 DEBUG: Start: Iteration 16
+2016-09-02 15:58:26,673 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:26,708 DEBUG: View 1 : 0.932692307692
+2016-09-02 15:58:29,527 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:58:29,535 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:58:29,627 DEBUG: Best view : Methyl
+2016-09-02 15:58:30,983 DEBUG: Start: Iteration 17
+2016-09-02 15:58:32,077 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:32,112 DEBUG: View 1 : 0.932692307692
+2016-09-02 15:58:35,013 DEBUG: View 2 : 0.870192307692
+2016-09-02 15:58:35,022 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:58:35,115 DEBUG: Best view : Methyl
+2016-09-02 15:58:36,569 DEBUG: Start: Iteration 18
+2016-09-02 15:58:37,705 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:37,741 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:58:40,659 DEBUG: View 2 : 0.870192307692
+2016-09-02 15:58:40,667 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:58:40,763 DEBUG: Best view : Methyl
+2016-09-02 15:58:42,285 DEBUG: Start: Iteration 19
+2016-09-02 15:58:43,357 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:43,392 DEBUG: View 1 : 0.951923076923
+2016-09-02 15:58:46,211 DEBUG: View 2 : 0.870192307692
+2016-09-02 15:58:46,219 DEBUG: View 3 : 0.735576923077
+2016-09-02 15:58:46,320 DEBUG: Best view : Methyl
+2016-09-02 15:58:47,928 DEBUG: Start: Iteration 20
+2016-09-02 15:58:48,991 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:49,025 DEBUG: View 1 : 0.951923076923
+2016-09-02 15:58:51,856 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:58:51,864 DEBUG: View 3 : 0.735576923077
+2016-09-02 15:58:51,965 DEBUG: Best view : Methyl
+2016-09-02 15:58:53,651 DEBUG: Start: Iteration 21
+2016-09-02 15:58:54,717 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:58:54,751 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:58:57,564 DEBUG: View 2 : 0.884615384615
+2016-09-02 15:58:57,573 DEBUG: View 3 : 0.735576923077
+2016-09-02 15:58:57,676 DEBUG: Best view : Methyl
+2016-09-02 15:58:59,435 DEBUG: Start: Iteration 22
+2016-09-02 15:59:00,519 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:59:00,554 DEBUG: View 1 : 0.932692307692
+2016-09-02 15:59:03,586 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:59:03,594 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:59:03,700 DEBUG: Best view : Methyl
+2016-09-02 15:59:05,580 DEBUG: Start: Iteration 23
+2016-09-02 15:59:06,649 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:59:06,686 DEBUG: View 1 : 0.932692307692
+2016-09-02 15:59:09,545 DEBUG: View 2 : 0.846153846154
+2016-09-02 15:59:09,554 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:59:09,672 DEBUG: Best view : Methyl
+2016-09-02 15:59:11,629 DEBUG: Start: Iteration 24
+2016-09-02 15:59:12,702 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:59:12,736 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:59:15,589 DEBUG: View 2 : 0.865384615385
+2016-09-02 15:59:15,597 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:59:15,707 DEBUG: Best view : Methyl
+2016-09-02 15:59:17,754 DEBUG: Start: Iteration 25
+2016-09-02 15:59:18,845 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:59:18,879 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:59:21,780 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:59:21,789 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:59:21,907 DEBUG: Best view : Methyl
+2016-09-02 15:59:24,023 DEBUG: Start: Iteration 26
+2016-09-02 15:59:25,084 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:59:25,118 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:59:27,967 DEBUG: View 2 : 0.903846153846
+2016-09-02 15:59:27,975 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:59:28,089 DEBUG: Best view : Methyl
+2016-09-02 15:59:30,357 DEBUG: Start: Iteration 27
+2016-09-02 15:59:31,477 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:59:31,513 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:59:34,387 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:59:34,396 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:59:34,515 DEBUG: Best view : Methyl
+2016-09-02 15:59:36,896 DEBUG: Start: Iteration 28
+2016-09-02 15:59:37,986 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:59:38,020 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:59:40,848 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:59:40,857 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:59:40,976 DEBUG: Best view : Methyl
+2016-09-02 15:59:43,312 DEBUG: Start: Iteration 29
+2016-09-02 15:59:44,405 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:59:44,440 DEBUG: View 1 : 0.942307692308
+2016-09-02 15:59:47,270 DEBUG: View 2 : 0.894230769231
+2016-09-02 15:59:47,278 DEBUG: View 3 : 0.730769230769
+2016-09-02 15:59:47,399 DEBUG: Best view : Methyl
+2016-09-02 15:59:49,823 DEBUG: Start: Iteration 30
+2016-09-02 15:59:50,887 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:59:50,922 DEBUG: View 1 : 0.951923076923
+2016-09-02 15:59:53,772 DEBUG: View 2 : 0.870192307692
+2016-09-02 15:59:53,781 DEBUG: View 3 : 0.745192307692
+2016-09-02 15:59:53,908 DEBUG: Best view : Methyl
+2016-09-02 15:59:56,412 DEBUG: Start: Iteration 31
+2016-09-02 15:59:57,485 DEBUG: View 0 : 0.975961538462
+2016-09-02 15:59:57,518 DEBUG: View 1 : 0.951923076923
+2016-09-02 16:00:00,337 DEBUG: View 2 : 0.870192307692
+2016-09-02 16:00:00,346 DEBUG: View 3 : 0.745192307692
+2016-09-02 16:00:00,471 DEBUG: Best view : Methyl
+2016-09-02 16:00:03,052 DEBUG: Start: Iteration 32
+2016-09-02 16:00:04,111 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:00:04,144 DEBUG: View 1 : 0.942307692308
+2016-09-02 16:00:06,961 DEBUG: View 2 : 0.846153846154
+2016-09-02 16:00:06,970 DEBUG: View 3 : 0.735576923077
+2016-09-02 16:00:07,097 DEBUG: Best view : Methyl
+2016-09-02 16:00:09,760 DEBUG: Start: Iteration 33
+2016-09-02 16:00:10,830 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:00:10,865 DEBUG: View 1 : 0.932692307692
+2016-09-02 16:00:13,737 DEBUG: View 2 : 0.846153846154
+2016-09-02 16:00:13,746 DEBUG: View 3 : 0.735576923077
+2016-09-02 16:00:13,874 DEBUG: Best view : Methyl
+2016-09-02 16:00:16,615 DEBUG: Start: Iteration 34
+2016-09-02 16:00:17,672 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:00:17,706 DEBUG: View 1 : 0.942307692308
+2016-09-02 16:00:20,589 DEBUG: View 2 : 0.884615384615
+2016-09-02 16:00:20,597 DEBUG: View 3 : 0.735576923077
+2016-09-02 16:00:20,727 DEBUG: Best view : Methyl
+2016-09-02 16:00:23,555 DEBUG: Start: Iteration 35
+2016-09-02 16:00:24,611 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:00:24,646 DEBUG: View 1 : 0.942307692308
+2016-09-02 16:00:27,451 DEBUG: View 2 : 0.884615384615
+2016-09-02 16:00:27,459 DEBUG: View 3 : 0.730769230769
+2016-09-02 16:00:27,594 DEBUG: Best view : Methyl
+2016-09-02 16:00:30,498 DEBUG: Start: Iteration 36
+2016-09-02 16:00:31,558 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:00:31,593 DEBUG: View 1 : 0.932692307692
+2016-09-02 16:00:34,446 DEBUG: View 2 : 0.894230769231
+2016-09-02 16:00:34,455 DEBUG: View 3 : 0.730769230769
+2016-09-02 16:00:34,589 DEBUG: Best view : Methyl
+2016-09-02 16:00:37,748 DEBUG: Start: Iteration 37
+2016-09-02 16:00:38,903 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:00:38,938 DEBUG: View 1 : 0.932692307692
+2016-09-02 16:00:41,784 DEBUG: View 2 : 0.894230769231
+2016-09-02 16:00:41,792 DEBUG: View 3 : 0.745192307692
+2016-09-02 16:00:41,945 DEBUG: Best view : Methyl
+2016-09-02 16:00:45,260 DEBUG: Start: Iteration 38
+2016-09-02 16:00:46,410 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:00:46,445 DEBUG: View 1 : 0.951923076923
+2016-09-02 16:00:49,356 DEBUG: View 2 : 0.846153846154
+2016-09-02 16:00:49,364 DEBUG: View 3 : 0.745192307692
+2016-09-02 16:00:49,509 DEBUG: Best view : Methyl
+2016-09-02 16:00:52,799 DEBUG: Start: Iteration 39
+2016-09-02 16:00:53,857 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:00:53,891 DEBUG: View 1 : 0.951923076923
+2016-09-02 16:00:56,755 DEBUG: View 2 : 0.870192307692
+2016-09-02 16:00:56,763 DEBUG: View 3 : 0.745192307692
+2016-09-02 16:00:56,909 DEBUG: Best view : Methyl
+2016-09-02 16:01:00,254 DEBUG: Start: Iteration 40
+2016-09-02 16:01:01,364 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:01:01,399 DEBUG: View 1 : 0.951923076923
+2016-09-02 16:01:04,437 DEBUG: View 2 : 0.903846153846
+2016-09-02 16:01:04,446 DEBUG: View 3 : 0.730769230769
+2016-09-02 16:01:04,597 DEBUG: Best view : Methyl
+2016-09-02 16:01:08,024 DEBUG: Start: Iteration 41
+2016-09-02 16:01:09,162 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:01:09,196 DEBUG: View 1 : 0.942307692308
+2016-09-02 16:01:12,079 DEBUG: View 2 : 0.903846153846
+2016-09-02 16:01:12,087 DEBUG: View 3 : 0.730769230769
+2016-09-02 16:01:12,240 DEBUG: Best view : Methyl
+2016-09-02 16:01:15,886 DEBUG: Start: Iteration 42
+2016-09-02 16:01:16,972 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:01:17,007 DEBUG: View 1 : 0.942307692308
+2016-09-02 16:01:19,855 DEBUG: View 2 : 0.894230769231
+2016-09-02 16:01:19,863 DEBUG: View 3 : 0.735576923077
+2016-09-02 16:01:20,012 DEBUG: Best view : Methyl
+2016-09-02 16:01:23,620 DEBUG: Start: Iteration 43
+2016-09-02 16:01:24,752 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:01:24,787 DEBUG: View 1 : 0.932692307692
+2016-09-02 16:01:27,712 DEBUG: View 2 : 0.894230769231
+2016-09-02 16:01:27,721 DEBUG: View 3 : 0.735576923077
+2016-09-02 16:01:27,872 DEBUG: Best view : Methyl
+2016-09-02 16:01:31,566 DEBUG: Start: Iteration 44
+2016-09-02 16:01:32,682 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:01:32,722 DEBUG: View 1 : 0.932692307692
+2016-09-02 16:01:35,658 DEBUG: View 2 : 0.894230769231
+2016-09-02 16:01:35,666 DEBUG: View 3 : 0.745192307692
+2016-09-02 16:01:35,818 DEBUG: Best view : Methyl
+2016-09-02 16:01:39,510 DEBUG: Start: Iteration 45
+2016-09-02 16:01:40,581 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:01:40,616 DEBUG: View 1 : 0.932692307692
+2016-09-02 16:01:43,689 DEBUG: View 2 : 0.894230769231
+2016-09-02 16:01:43,699 DEBUG: View 3 : 0.745192307692
+2016-09-02 16:01:43,879 DEBUG: Best view : Methyl
+2016-09-02 16:01:47,659 DEBUG: Start: Iteration 46
+2016-09-02 16:01:48,716 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:01:48,749 DEBUG: View 1 : 0.942307692308
+2016-09-02 16:01:51,689 DEBUG: View 2 : 0.870192307692
+2016-09-02 16:01:51,697 DEBUG: View 3 : 0.730769230769
+2016-09-02 16:01:51,879 DEBUG: Best view : Methyl
+2016-09-02 16:01:55,879 DEBUG: Start: Iteration 47
+2016-09-02 16:01:57,015 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:01:57,050 DEBUG: View 1 : 0.942307692308
+2016-09-02 16:02:00,044 DEBUG: View 2 : 0.870192307692
+2016-09-02 16:02:00,052 DEBUG: View 3 : 0.730769230769
+2016-09-02 16:02:00,211 DEBUG: Best view : Methyl
+2016-09-02 16:02:04,230 DEBUG: Start: Iteration 48
+2016-09-02 16:02:05,362 DEBUG: View 0 : 0.975961538462
+2016-09-02 16:02:05,398 DEBUG: View 1 : 0.951923076923
+2016-09-02 16:02:08,619 DEBUG: View 2 : 0.846153846154
+2016-09-02 16:02:08,630 DEBUG: View 3 : 0.730769230769
+2016-09-02 16:02:08,802 DEBUG: Best view : Methyl
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155225Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155225Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4dab1a0f03d7f9e95723009bf409939064fa4742
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155225Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Methyl with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.809523809524
+
+
+ Classification took 0:00:18
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155225Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155225Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d282798b9704a49412495dd41ef80e062e4a0eb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155225Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:18
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155227Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155227Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..47712ba81f702c4c916d207025370b9d9c670175
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155227Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.87619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, 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 : 0.87619047619
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155234Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155234Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9d9bd87debb16b36ced82544855b4710e2c0eab1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155234Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with KNN
+
+accuracy_score on train : 0.900826446281
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.900826446281
+ - Score on test : 0.857142857143
+
+
+ Classification took 0:00:05
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155238Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155238Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bd8d70e3ce6d31668631b6d7347cce243e5fc7b1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155238Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SGD
+
+accuracy_score on train : 0.933884297521
+accuracy_score on test : 0.885714285714
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - 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 : 0.933884297521
+ - Score on test : 0.885714285714
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155248Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155248Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4e17b59483cef7acbea64fe307bfc99e15733830
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155248Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:12
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155304Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155304Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3ffb7d24a956087f2b65ed6fb0c89147e10368fc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155304Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.866666666667
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.866666666667
+
+
+ Classification took 0:00:14
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155306Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155306Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c025e890f96fd9db25c061a39586b2da238f34c9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155306Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Methyl with SVMPoly
+
+accuracy_score on train : 0.702479338843
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Methyl View shape : (347, 25978)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.702479338843
+ - Score on test : 0.8
+
+
+ Classification took 0:00:16
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155307Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155307Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d77a4b0c15002957d4a70d874eba5c32f8e12cc4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155307Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for MiRNA_ with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155307Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155307Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..26e9c39f81a398208c551f0e5406e56d606fc77d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155307Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155307Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155307Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f7837cd2983583b8b5ddc667d5773fc60cfec04e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155307Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with KNN
+
+accuracy_score on train : 0.743801652893
+accuracy_score on test : 0.809523809524
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.743801652893
+ - Score on test : 0.809523809524
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..779bd572f240370036ad4af6d16a65b21f433162
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.847619047619
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, 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 : 0.847619047619
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e30eeb89d37becbef7b24d6c5685db26df575ae7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SGD
+
+accuracy_score on train : 0.818181818182
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - 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 : 0.818181818182
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f55622e9d9d481b6437f9c912184ee297d49f113
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMLinear
+
+accuracy_score on train : 0.876033057851
+accuracy_score on test : 0.780952380952
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.876033057851
+ - Score on test : 0.780952380952
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..fb1e71df1fed9de46cf32937f89ec7a031886cf9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155308Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMPoly
+
+accuracy_score on train : 0.297520661157
+accuracy_score on test : 0.2
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.297520661157
+ - Score on test : 0.2
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155309Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155309Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..90eb2ac6de4172b3e7c1b0980a3a5c002677c462
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155309Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for MiRNA_ with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : MiRNA_ View shape : (347, 1046)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155425Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155425Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..92aa0ef592f4024dc618142c48ca6427c4ab85ec
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155425Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.72380952381
+
+
+ Classification took 0:01:15
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155428Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155428Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..36316b74b5986d6257810d9bd7a5e69938db8221
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155428Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for RANSeq with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.72380952381
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.72380952381
+
+
+ Classification took 0:01:17
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155431Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155431Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..82a11ff93f1f08e2dacd189a26ddf0fa2f9447eb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155431Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with RandomForest
+
+accuracy_score on train : 0.747933884298
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 6, 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 : 0.747933884298
+ - Score on test : 0.8
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155456Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155456Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5221dfeae1a70c4fa72b9f8491c94398c6167f84
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155456Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with KNN
+
+accuracy_score on train : 0.694214876033
+accuracy_score on test : 0.771428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.694214876033
+ - Score on test : 0.771428571429
+
+
+ Classification took 0:00:16
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155506Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155506Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b71cdde6383a166216442e470facb81af591987d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155506Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with SGD
+
+accuracy_score on train : 0.735537190083
+accuracy_score on test : 0.790476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - 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 : 0.735537190083
+ - Score on test : 0.790476190476
+
+
+ Classification took 0:00:09
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155558Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155558Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0e05e9ce8188e03a0b55bf8748122d48b89bd8af
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155558Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with SVMLinear
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.590476190476
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.590476190476
+
+
+ Classification took 0:00:56
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155602Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155602Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..10376407256da145b44b7b475f2cd20c1c56b04a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155602Results-SVMPoly-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with SVMPoly
+
+accuracy_score on train : 0.297520661157
+accuracy_score on test : 0.2
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.297520661157
+ - Score on test : 0.2
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155711Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155711Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..48f827600f77360af641faac434b1407c6b56b29
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155711Results-SVMRBF-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for RANSeq with SVMRBF
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : RANSeq View shape : (347, 73599)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.8
+
+
+ Classification took 0:01:06
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..162f5cd9d19a0d1300c68bd3ef2e28535b3f12f9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-Adaboost-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,27 @@
+Classification on MultiOmic database for Clinic with Adaboost
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.857142857143
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f082a7d26b6a425feab608716df615902fc58909
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-DecisionTree-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with DecisionTree
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.857142857143
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..11ff4444a28dba3201ac4accc3e79bb43b208cf8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-KNN-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with KNN
+
+accuracy_score on train : 0.702479338843
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.702479338843
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..04a75901a5e8bad80bdcd2a29aaf81952db240a2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155712Results-RandomForest-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with RandomForest
+
+accuracy_score on train : 1.0
+accuracy_score on test : 0.857142857143
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 26, 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 : 0.857142857143
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155713Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155713Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a119e9610e02843074d43d16adb1a73fee7e8d69
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155713Results-SGD-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SGD
+
+accuracy_score on train : 0.702479338843
+accuracy_score on test : 0.8
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - 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 : 0.702479338843
+ - Score on test : 0.8
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155713Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155713Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a082aaac737d07a413312a7fd27ffc1f6024d215
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155713Results-SVMLinear-Non-Oui-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,24 @@
+Classification on MultiOmic database for Clinic with SVMLinear
+
+accuracy_score on train : 0.615702479339
+accuracy_score on test : 0.571428571429
+
+Database configuration :
+ - Database name : MultiOmic
+ - View name : Clinic View shape : (347, 127)
+ - Learning Rate : 0.7
+ - Labels used : Non, Oui
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 4893
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.615702479339
+ - Score on test : 0.571428571429
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-155717Results-Fusion-LateFusion-BayesianInference-SGD-RandomForest-RandomForest-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-Yes-No-learnRate0.7-MultiOmic.txt b/Code/MonoMutliViewClassifiers/Results/20160902-155717Results-Fusion-LateFusion-BayesianInference-SGD-RandomForest-RandomForest-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-Yes-No-learnRate0.7-MultiOmic.txt
new file mode 100644
index 0000000000000000000000000000000000000000..080e7de2b3f37fdff4930840de6c68f3e4d37c43
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-155717Results-Fusion-LateFusion-BayesianInference-SGD-RandomForest-RandomForest-Adaboost-Methyl-MiRNA_-RNASeq-Clinic-Yes-No-learnRate0.7-MultiOmic.txt
@@ -0,0 +1,35 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy :
+ -On Train : 99.5173366428
+ -On Test : 89.1666666667
+ -On Validation : 97.6699029126
+
+Dataset info :
+ -Database name : MultiOmic
+ -Labels : No, Yes
+ -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
+ - Random Forest with num_esimators : 26, max_depth : 29
+ - Random Forest with num_esimators : 6, max_depth : 1
+ - 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')
+
+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 0:00:03 0:00:00
+ Total 0:00:12 0:00:01
+ So a total classification time of 0:00:04.
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-160216-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-160216-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..8fdff837185ed9606f39daf47b92ac7e4b9a7f55
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-160216-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,2 @@
+2016-09-02 16:02:16,533 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 16:02:16,534 WARNING: /!\ This may use a lot of HDD storage space : 0.273145851562 Gbytes /!\
diff --git a/Code/MonoMutliViewClassifiers/Results/20160902-160238-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log b/Code/MonoMutliViewClassifiers/Results/20160902-160238-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
new file mode 100644
index 0000000000000000000000000000000000000000..9185f1274a1fc4046838d4ccaf137d790e315766
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20160902-160238-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log
@@ -0,0 +1,2 @@
+2016-09-02 16:02:38,073 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2016-09-02 16:02:38,073 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.273145851562 Gbytes /!\