From 788b3384049a9e7f5ef7e8c52d5d6db11bcc7576 Mon Sep 17 00:00:00 2001
From: bbauvin <baptiste.bauvin@centrale-marseille.fr>
Date: Fri, 22 Sep 2017 15:50:21 -0400
Subject: [PATCH] Modified result analysis and updated classic databse fetching
---
Code/MonoMutliViewClassifiers/ExecClassif.py | 82 +-
.../Monoview/ExecClassifMonoView.py | 5 +-
.../MonoviewClassifiers/SVMPoly.py | 4 +-
.../MonoviewClassifiers/SVMRBF.py | 4 +-
.../Multiview/GetMultiviewDb.py | 21 +-
.../ResultAnalysis.py | 70 +-
.../Results/20170920-164255error_analysis.png | Bin 0 -> 31043 bytes
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.../Results/20170921-135252error_analysis.png | Bin 0 -> 155127 bytes
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 0
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 0
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 0
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 0
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 0
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 0
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 0
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 12 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 0
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 10 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 10 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 10 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 10 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 10 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 10 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 10 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 10 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 10 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 10 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 0
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 1110 +++++++++
...isionTree--learnRate0.7-awaexp-cq-hist.txt | 51 +
...-Adaboost--learnRate0.7-awaexp-cq-hist.txt | 55 +
...domForest--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-KNN--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-SGD--learnRate0.7-awaexp-cq-hist.txt | 51 +
...SVMLinear--learnRate0.7-awaexp-cq-hist.txt | 51 +
...ts-SVMRBF--learnRate0.7-awaexp-cq-hist.txt | 51 +
...s-SVMPoly--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sionTree--learnRate0.7-awaexp-lss-hist.txt | 51 +
...Adaboost--learnRate0.7-awaexp-lss-hist.txt | 55 +
...omForest--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-KNN--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-SGD--learnRate0.7-awaexp-lss-hist.txt | 51 +
...VMLinear--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-SVMPoly--learnRate0.7-awaexp-lss-hist.txt | 51 +
...s-SVMRBF--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 1110 +++++++++
...-Adaboost--learnRate0.7-awaexp-cq-hist.txt | 55 +
...isionTree--learnRate0.7-awaexp-cq-hist.txt | 51 +
...domForest--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-KNN--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-SGD--learnRate0.7-awaexp-cq-hist.txt | 51 +
...SVMLinear--learnRate0.7-awaexp-cq-hist.txt | 51 +
...ts-SVMRBF--learnRate0.7-awaexp-cq-hist.txt | 51 +
...s-SVMPoly--learnRate0.7-awaexp-cq-hist.txt | 51 +
...Adaboost--learnRate0.7-awaexp-lss-hist.txt | 55 +
...sionTree--learnRate0.7-awaexp-lss-hist.txt | 51 +
...omForest--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-KNN--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-SGD--learnRate0.7-awaexp-lss-hist.txt | 51 +
...VMLinear--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-SVMPoly--learnRate0.7-awaexp-lss-hist.txt | 51 +
...s-SVMRBF--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 1110 +++++++++
...isionTree--learnRate0.7-awaexp-cq-hist.txt | 51 +
...-Adaboost--learnRate0.7-awaexp-cq-hist.txt | 55 +
...domForest--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-KNN--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-SGD--learnRate0.7-awaexp-cq-hist.txt | 51 +
...SVMLinear--learnRate0.7-awaexp-cq-hist.txt | 51 +
...ts-SVMRBF--learnRate0.7-awaexp-cq-hist.txt | 51 +
...s-SVMPoly--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sionTree--learnRate0.7-awaexp-lss-hist.txt | 51 +
...Adaboost--learnRate0.7-awaexp-lss-hist.txt | 55 +
...omForest--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-KNN--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-SGD--learnRate0.7-awaexp-lss-hist.txt | 51 +
...VMLinear--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-SVMPoly--learnRate0.7-awaexp-lss-hist.txt | 51 +
...s-SVMRBF--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 1110 +++++++++
...-Adaboost--learnRate0.7-awaexp-cq-hist.txt | 55 +
...isionTree--learnRate0.7-awaexp-cq-hist.txt | 51 +
...domForest--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-KNN--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-SGD--learnRate0.7-awaexp-cq-hist.txt | 51 +
...SVMLinear--learnRate0.7-awaexp-cq-hist.txt | 51 +
...ts-SVMRBF--learnRate0.7-awaexp-cq-hist.txt | 51 +
...s-SVMPoly--learnRate0.7-awaexp-cq-hist.txt | 51 +
...Adaboost--learnRate0.7-awaexp-lss-hist.txt | 55 +
...sionTree--learnRate0.7-awaexp-lss-hist.txt | 51 +
...omForest--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-KNN--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-SGD--learnRate0.7-awaexp-lss-hist.txt | 51 +
...VMLinear--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-SVMPoly--learnRate0.7-awaexp-lss-hist.txt | 51 +
...s-SVMRBF--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 0
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 626 +++++
...isionTree--learnRate0.7-awaexp-cq-hist.txt | 51 +
...-Adaboost--learnRate0.7-awaexp-cq-hist.txt | 55 +
...domForest--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-KNN--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-SGD--learnRate0.7-awaexp-cq-hist.txt | 51 +
...SVMLinear--learnRate0.7-awaexp-cq-hist.txt | 51 +
...ts-SVMRBF--learnRate0.7-awaexp-cq-hist.txt | 51 +
...s-SVMPoly--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sionTree--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 2095 +++++++++++++++++
...isionTree--learnRate0.7-awaexp-cq-hist.txt | 51 +
...-Adaboost--learnRate0.7-awaexp-cq-hist.txt | 55 +
...domForest--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-KNN--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-SGD--learnRate0.7-awaexp-cq-hist.txt | 51 +
...SVMLinear--learnRate0.7-awaexp-cq-hist.txt | 51 +
...ts-SVMRBF--learnRate0.7-awaexp-cq-hist.txt | 51 +
...s-SVMPoly--learnRate0.7-awaexp-cq-hist.txt | 51 +
...Adaboost--learnRate0.7-awaexp-lss-hist.txt | 55 +
...sionTree--learnRate0.7-awaexp-lss-hist.txt | 51 +
...omForest--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-KNN--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-SGD--learnRate0.7-awaexp-lss-hist.txt | 51 +
...VMLinear--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-SVMPoly--learnRate0.7-awaexp-lss-hist.txt | 51 +
...s-SVMRBF--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 58 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 58 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 58 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 58 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 58 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
.../Results/20170922-151331error_analysis.png | Bin 0 -> 161159 bytes
.../20170922-151333-awaexp-accuracy_score.png | Bin 0 -> 232211 bytes
.../20170922-151336-awaexp-f1_score.png | Bin 0 -> 237774 bytes
.../20170922-151339-awaexp-fbeta_score.png | Bin 0 -> 238605 bytes
.../20170922-151342-awaexp-hamming_loss.png | Bin 0 -> 231817 bytes
...151344-awaexp-jaccard_similarity_score.png | Bin 0 -> 232798 bytes
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.../20170922-151356-awaexp-roc_auc_score.png | Bin 0 -> 235019 bytes
.../20170922-151359-awaexp-zero_one_loss.png | Bin 0 -> 232883 bytes
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 2 +
...-Benchmark-cq-hist_lss-hist-awaexp-LOG.log | 1921 +++++++++++++++
...domForest--learnRate0.7-awaexp-cq-hist.txt | 51 +
...isionTree--learnRate0.7-awaexp-cq-hist.txt | 51 +
...-Adaboost--learnRate0.7-awaexp-cq-hist.txt | 55 +
...sults-KNN--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-SGD--learnRate0.7-awaexp-cq-hist.txt | 51 +
...SVMLinear--learnRate0.7-awaexp-cq-hist.txt | 51 +
...ts-SVMRBF--learnRate0.7-awaexp-cq-hist.txt | 51 +
...s-SVMPoly--learnRate0.7-awaexp-cq-hist.txt | 51 +
...omForest--learnRate0.7-awaexp-lss-hist.txt | 51 +
...sionTree--learnRate0.7-awaexp-lss-hist.txt | 51 +
...Adaboost--learnRate0.7-awaexp-lss-hist.txt | 55 +
...ults-KNN--learnRate0.7-awaexp-lss-hist.txt | 51 +
...ults-SGD--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-SVMPoly--learnRate0.7-awaexp-lss-hist.txt | 51 +
...VMLinear--learnRate0.7-awaexp-lss-hist.txt | 51 +
...s-SVMRBF--learnRate0.7-awaexp-lss-hist.txt | 51 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 58 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 58 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 58 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 58 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 58 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist-lss-hist--learnRate0.7-awaexp.txt | 56 +
...-cq-hist_lss-hist_phog-hist-awaexp-LOG.log | 0
...-cq-hist_lss-hist_phog-hist-awaexp-LOG.log | 708 ++++++
...isionTree--learnRate0.7-awaexp-cq-hist.txt | 51 +
...-Adaboost--learnRate0.7-awaexp-cq-hist.txt | 55 +
...domForest--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-KNN--learnRate0.7-awaexp-cq-hist.txt | 51 +
...sults-SGD--learnRate0.7-awaexp-cq-hist.txt | 51 +
...SVMLinear--learnRate0.7-awaexp-cq-hist.txt | 51 +
...ts-SVMRBF--learnRate0.7-awaexp-cq-hist.txt | 51 +
...s-SVMPoly--learnRate0.7-awaexp-cq-hist.txt | 51 +
Code/MonoMutliViewClassifiers/temp_scm_fusion | Bin 0 -> 2240 bytes
Code/MonoMutliViewClassifiers/test.png | Bin 0 -> 31292 bytes
223 files changed, 17261 insertions(+), 68 deletions(-)
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diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index 72edbcbb..f16a1e46 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -5,6 +5,8 @@ import os
import time # for time calculations
import operator
import itertools
+import sys
+import select
# Import 3rd party modules
from joblib import Parallel, delayed
@@ -21,7 +23,7 @@ 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
+from ResultAnalysis import resultAnalysis, analyzeLabels
from Versions import testVersions
import MonoviewClassifiers
@@ -57,7 +59,31 @@ def initLogFile(args):
logging.getLogger().addHandler(logging.StreamHandler())
+def input(timeout=15):
+ print "You have " + str(timeout) + " seconds to stop the script by typing n"
+
+ i, o, e = select.select( [sys.stdin], [], [], timeout)
+
+ if (i):
+ return sys.stdin.readline().strip()
+ else:
+ return "y"
+
+
+def confirm(prompt=None, resp=True, timeout = 15):
+ ans = input(timeout)
+ if not ans:
+ return resp
+ if ans not in ['y', 'Y', 'n', 'N']:
+ print 'please enter y or n.'
+ if ans == 'y' or ans == 'Y':
+ return True
+ if ans == 'n' or ans == 'N':
+ return False
+
def initMultipleDatasets(args, nbCores):
+ """Used to create copies of the dataset if multicore computation is used
+ Needs arg.pathF and arg.name"""
if nbCores>1:
if DB.datasetsAlreadyExist(args.pathF, args.name, nbCores):
logging.debug("Info:\t Enough copies of the dataset are already available")
@@ -66,13 +92,18 @@ def initMultipleDatasets(args, nbCores):
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")
- return datasetFiles
+ confirmation = confirm()
+ if not confirmation:
+ sys.exit(0)
+ else:
+ datasetFiles = DB.copyHDF5(args.pathF, args.name, nbCores)
+ logging.debug("Start:\t Creating datasets for multiprocessing")
+ return datasetFiles
def initViews(DATASET, args):
+ """Used to return the views names that will be used by the algos, their indices and all the views names
+ Needs args.views"""
NB_VIEW = DATASET.get("Metadata").attrs["nbView"]
if args.views!="":
allowedViews = args.views.split(":")
@@ -88,6 +119,9 @@ def initViews(DATASET, args):
def initBenchmark(args):
+ """Used to create a list of all the algorithm packages names used for the benchmark
+ Needs args.CL_type, args.CL_algos_multiview, args.MU_types, args.FU_types, args.FU_late_methods,
+ args.FU_early_methods, args.CL_algos_monoview"""
benchmark = {"Monoview":{}, "Multiview":[]}
if args.CL_type.split(":")==["Benchmark"]:
# if args.CL_algorithm=='':
@@ -109,7 +143,7 @@ def initBenchmark(args):
allMumboAlgos = [name for _, name, isPackage in
pkgutil.iter_modules(['Multiview/Mumbo/Classifiers'])
if not isPackage and not name in ["SubSampling", "ModifiedMulticlass", "Kover"]]
- allMultiviewAlgos = {"Fusion": allFusionAlgos}#, "Mumbo": allMumboAlgos}
+ allMultiviewAlgos = {"Fusion": allFusionAlgos}#, "Mumbo": allMumboAlgos
benchmark = {"Monoview": allMonoviewAlgos, "Multiview": allMultiviewAlgos}
if "Multiview" in args.CL_type.strip(":"):
@@ -296,21 +330,21 @@ def initMultiviewArguments(args, benchmark, views, viewsIndices, accuracies, cla
return argumentDictionaries
-def analyzeLabels(labelsArrays, realLabels, classifiersNames):
- nbClassifiers = len(classifiersNames)
- nbExamples = realLabels.shape[0]
- nbIter = nbExamples/nbClassifiers
- data = np.zeros((nbExamples, nbClassifiers*nbIter))
- tempData = np.array([labelsArray == realLabels for labelsArray in labelsArrays]).astype(int)
- for classifierIndex in range(nbClassifiers):
- for iterIndex in range(nbIter):
- data[:,classifierIndex*nbIter+iterIndex] = tempData[:,classifierIndex]
- fig, ax = plt.subplots()
- cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
- ax.set_title('Gaussian noise with vertical colorbar')
- cbar = fig.colorbar(cax, ticks=[0, 1])
- cbar.ax.set_yticklabels(['Wrong', ' Right'])
- fig.savefig("test.png")
+# def analyzeLabels(labelsArrays, realLabels, classifiersNames):
+# nbClassifiers = len(classifiersNames)
+# nbExamples = realLabels.shape[0]
+# nbIter = nbExamples/nbClassifiers
+# data = np.zeros((nbExamples, nbClassifiers*nbIter))
+# tempData = np.array([labelsArray == realLabels for labelsArray in np.transpose(labelsArrays)]).astype(int)
+# for classifierIndex in range(nbClassifiers):
+# for iterIndex in range(nbIter):
+# data[:,classifierIndex*nbIter+iterIndex] = tempData[classifierIndex,:]
+# fig, ax = plt.subplots()
+# cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
+# ax.set_title('Error on examples depending on the classifier')
+# cbar = fig.colorbar(cax, ticks=[0, 1])
+# cbar.ax.set_yticklabels(['Wrong', ' Right'])
+# fig.savefig("Results/"+time.strftime("%Y%m%d-%H%M%S")+"error_analysis.png")
parser = argparse.ArgumentParser(
@@ -351,7 +385,7 @@ groupClass.add_argument('--CL_algos_monoview', metavar='STRING', action='store',
groupClass.add_argument('--CL_algos_multiview', metavar='STRING', action='store',
help='Determine which multiview classifier to use, separate with : if multiple, if empty, considering all', default='')
groupClass.add_argument('--CL_cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
- default=1)
+ default=2)
groupClass.add_argument('--CL_statsiter', metavar='INT', action='store', help='Number of iteration for each algorithm to mean results', type=int,
default=2)
groupClass.add_argument('--CL_metrics', metavar='STRING', action='store', nargs="+",
@@ -456,7 +490,7 @@ except:
initLogFile(args)
-DATASET, LABELS_DICTIONARY = getDatabase(args.views.split(":"), args.pathF, args.name, len(args.CL_classes), args.CL_classes)
+DATASET, LABELS_DICTIONARY = getDatabase(args.views.split(":"), args.pathF, args.name, args.CL_nb_class, args.CL_classes)
datasetFiles = initMultipleDatasets(args, nbCores)
@@ -539,10 +573,10 @@ if nbCores>1:
logging.debug("Start:\t Deleting datasets for multiprocessing")
labels = np.array([resultMonoview[1][3] for resultMonoview in resultsMonoview]+[resultMultiview[3] for resultMultiview in resultsMultiview]).transpose()
trueLabels = DATASET.get("Labels").value
-analyzeLabels(labels, trueLabels, ["" in range(labels.shape[1])])
times = [dataBaseTime, monoviewTime, multiviewTime]
# times=[]
results = (resultsMonoview, resultsMultiview)
+analyzeLabels(labels, trueLabels, results)
logging.debug("Start:\t Analyze Global Results")
resultAnalysis(benchmark, results, args.name, times, metrics)
logging.debug("Done:\t Analyze Global Results")
diff --git a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
index 5c9c9ece..327974a8 100644
--- a/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -68,10 +68,11 @@ def ExecMonoview(X, Y, name, labelsNames, learningRate, nbFolds, nbCores, databa
# Calculate Train/Test data
logging.debug("Start:\t Determine Train/Test split"+" for iteration "+str(iterationStat+1))
testIndices = ClassifMonoView.splitDataset(Y, nbClass, learningRate, datasetLength)
+ print "fromage"
trainIndices = [i for i in range(datasetLength) if i not in testIndices]
-
+ print "jqmbon"
X_train = extractSubset(X,trainIndices)
-
+ print "poulet"
X_test = extractSubset(X,testIndices)
y_train = Y[trainIndices]
y_test = Y[testIndices]
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
index 7f48d225..2beb8295 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMPoly.py
@@ -39,6 +39,6 @@ def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score",
def getConfig(config):
try:
- return "\n\t\t- SVM Linear with C : "+str(config[0])
+ return "\n\t\t- SVM Poly with C : "+str(config[0])
except:
- return "\n\t\t- SVM Linear with C : "+str(config["0"])
\ No newline at end of file
+ return "\n\t\t- SVM Poly with C : "+str(config["0"])
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
index 9234a3a5..760395b9 100644
--- a/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
+++ b/Code/MonoMutliViewClassifiers/MonoviewClassifiers/SVMRBF.py
@@ -38,6 +38,6 @@ def gridSearch(X_train, y_train, nbFolds=4, nbCores=1, metric=["accuracy_score",
def getConfig(config):
try:
- return "\n\t\t- SVM Linear with C : "+str(config[0])
+ return "\n\t\t- SVM RBF with C : "+str(config[0])
except:
- return "\n\t\t- SVM Linear with C : "+str(config["0"])
\ No newline at end of file
+ return "\n\t\t- SVM RBF with C : "+str(config["0"])
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
index ce16fd5e..34b8a394 100644
--- a/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
+++ b/Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
@@ -13,7 +13,7 @@ __author__ = "Baptiste Bauvin"
__status__ = "Prototype" # Production, Development, Prototype
-def makeMeNoisy(viewData, percentage=25):
+def makeMeNoisy(viewData, percentage=15):
viewData = viewData.astype(bool)
nbNoisyCoord = int(percentage/100.0*viewData.shape[0]*viewData.shape[1])
rows = range(viewData.shape[0])
@@ -33,8 +33,8 @@ def getPlausibleDBhdf5(features, pathF, name , NB_CLASS, LABELS_NAME, nbView=3,
for viewIndex in range(nbView):
# if viewIndex== 0 :
viewData = np.array([np.zeros(nbFeatures) for i in range(datasetLength/2)]+[np.ones(nbFeatures) for i in range(datasetLength/2)])
- fakeTrueIndices = np.random.randint(0, datasetLength/2-1, datasetLength/10)
- fakeFalseIndices = np.random.randint(datasetLength/2, datasetLength-1, datasetLength/10)
+ fakeTrueIndices = np.random.randint(0, datasetLength/2-1, datasetLength/5)
+ fakeFalseIndices = np.random.randint(datasetLength/2, datasetLength-1, datasetLength/5)
viewData[fakeTrueIndices] = np.ones((len(fakeTrueIndices), nbFeatures))
viewData[fakeFalseIndices] = np.zeros((len(fakeFalseIndices), nbFeatures))
@@ -175,32 +175,35 @@ def getPositions(labelsUsed, fullLabels):
def getClassicDBcsv(views, pathF, nameDB, NB_CLASS, LABELS_NAMES):
- datasetFile = h5py.File(pathF+nameDB+".hdf5", "w")
labelsNamesFile = open(pathF+nameDB+'-ClassLabels-Description.csv')
+ datasetFile = h5py.File(pathF+nameDB+".hdf5", "w")
if len(LABELS_NAMES)!=NB_CLASS:
nbLabelsAvailable = 0
for l in labelsNamesFile:
nbLabelsAvailable+=1
LABELS_NAMES = [line.strip().split(";")[1] for lineIdx, line in enumerate(labelsNamesFile) if lineIdx in np.random.randint(nbLabelsAvailable, size=NB_CLASS)]
- fullLabels = np.genfromtxt(pathF + nameDB + '-ClassLabels.csv', delimiter=';').astype(int)
+ fullLabels = np.genfromtxt(pathF + nameDB + '-ClassLabels.csv', delimiter=',').astype(int)
labelsDictionary = dict((classIndice, labelName) for (classIndice, labelName) in
- [(int(line.strip().split(";")[0]),line.strip().split(";")[1])for lineIndex, line in labelsNamesFile if line.strip().split(";")[0] in LABELS_NAMES])
+ [(int(line.strip().split(";")[0]),line.strip().split(";")[1])for lineIndex, line in enumerate(labelsNamesFile) if line.strip().split(";")[0] in LABELS_NAMES])
if len(set(fullLabels))>NB_CLASS:
usedIndices = getPositions(labelsDictionary.keys(), fullLabels)
else:
usedIndices = range(len(fullLabels))
for viewIndex, view in enumerate(views):
viewFile = pathF + nameDB + "-" + view + '.csv'
- viewMatrix = np.array(np.genfromtxt(viewFile, delimiter=';'))[usedIndices, :]
+ viewMatrix = np.array(np.genfromtxt(viewFile, delimiter=','))[usedIndices, :]
viewDset = datasetFile.create_dataset("View"+str(viewIndex), viewMatrix.shape, data=viewMatrix)
viewDset.attrs["name"] = view
+ viewDset.attrs["sparse"] = False
+ viewDset.attrs["binary"] = False
labelsDset = datasetFile.create_dataset("Labels", fullLabels[usedIndices].shape, data=fullLabels[usedIndices])
- labelsDset.attrs["labelsDictionary"] = labelsDictionary
+ #labelsDset.attrs["labelsDictionary"] = labelsDictionary
metaDataGrp = datasetFile.create_group("Metadata")
metaDataGrp.attrs["nbView"] = len(views)
metaDataGrp.attrs["nbClass"] = NB_CLASS
+ print NB_CLASS
metaDataGrp.attrs["datasetLength"] = len(fullLabels[usedIndices])
datasetFile.close()
datasetFile = h5py.File(pathF+nameDB+".hdf5", "r")
@@ -456,6 +459,7 @@ def makeSortedBinsMatrix(nbBins, lenBins, overlapping, arrayLen, path):
np.savetxt(path+"sortedBinsMatrix--t-"+str(lenBins)+"--n-"+str(nbBins)+"--c-"+str(overlapping)+".csv", sortedBinsMatrix, delimiter=",")
return sortedBinsMatrix
+
def makeSparseTotalMatrix(sortedRNASeq):
nbPatients, nbGenes = sortedRNASeq.shape
params = findParams(nbGenes, nbPatients)
@@ -895,6 +899,7 @@ def copyHDF5(pathF, name, nbCores):
datasetFile.copy("/"+dataset, newDataSet["/"])
newDataSet.close()
+
def datasetsAlreadyExist(pathF, name, nbCores):
allDatasetExist = True
for coreIndex in range(nbCores):
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index 22fee825..694a6333 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -9,6 +9,7 @@ matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
+import matplotlib as mpl
#Import own Modules
import Metrics
@@ -27,24 +28,27 @@ def autolabel(rects, ax):
ha='center', va='bottom')
+def genNamesFromRes(mono, multi):
+ names = [res[1][0]+"-"+res[1][1][-1] for res in mono]
+ names+=[type_ for type_, a, b, c in multi if type_ != "Fusion"]
+ names+=[ "Late-"+str(a["fusionMethod"]) for type_, a, b, c in multi if type_ == "Fusion" and a["fusionType"] != "EarlyFusion"]
+ names+=[ "Early-"+a["fusionMethod"]+"-"+a["classifiersNames"][0] for type_, a, b, c in multi if type_ == "Fusion" and a["fusionType"] != "LateFusion"]
+ return names
+
+
def resultAnalysis(benchmark, results, name, times, metrics):
mono, multi = results
- labelsByView = np.array([res[0][3] for res in mono]+[res[3] for res in multi])
- makeColorMap(labelsByView, name)
for metric in metrics:
- names = [res[1][0]+"-"+res[1][1][-1] for res in mono]
- names+=[type_ for type_, a, b in multi if type_ != "Fusion"]
- names+=[ "Late-"+str(a["fusionMethod"]) for type_, a, b in multi if type_ == "Fusion" and a["fusionType"] != "EarlyFusion"]
- names+=[ "Early-"+a["fusionMethod"]+"-"+a["classifiersNames"][0] for type_, a, b in multi if type_ == "Fusion" and a["fusionType"] != "LateFusion"]
+ names = genNamesFromRes(mono, multi)
nbResults = len(mono)+len(multi)
- validationScores = [float(res[1][2][metric[0]][1]) for res in mono]
- validationSTD = [float(res[1][2][metric[0]][3]) for res in mono]
- validationScores += [float(scores[metric[0]][1]) for a, b, scores in multi]
- validationSTD += [float(scores[metric[0]][3]) for a, b, scores in multi]
- trainScores = [float(res[1][2][metric[0]][0]) for res in mono]
- trainScores += [float(scores[metric[0]][0]) for a, b, scores in multi]
- trainSTD = [float(res[1][2][metric[0]][2]) for res in mono]
- trainSTD += [float(scores[metric[0]][2]) for a, b, scores in multi]
+ validationScores = [float(res[1][2][metric[0]][0]) for res in mono]
+ validationScores += [float(scores[metric[0]][0]) for a, b, scores, c in multi]
+ validationSTD = [float(res[1][2][metric[0]][2]) for res in mono]
+ validationSTD += [float(scores[metric[0]][2]) for a, b, scores, c in multi]
+ trainScores = [float(res[1][2][metric[0]][1]) for res in mono]
+ trainScores += [float(scores[metric[0]][1]) for a, b, scores, c in multi]
+ trainSTD = [float(res[1][2][metric[0]][3]) for res in mono]
+ trainSTD += [float(scores[metric[0]][3]) for a, b, scores, c in multi]
f = pylab.figure(figsize=(40, 30))
width = 0.35 # the width of the bars
fig = plt.gcf()
@@ -67,21 +71,27 @@ def resultAnalysis(benchmark, results, name, times, metrics):
logging.info("Extraction time : "+str(times[0])+"s, Monoview time : "+str(times[1])+"s, Multiview Time : "+str(times[2])+"s")
-def makeColorMap(labelsByView, name):
- nb_view = labelsByView.shape[1]
- nbExamples = labelsByView.shape[0]
- # Make plot with vertical (default) colorbar
- fig, ax = plt.subplots()
- data = np.zeros((nbExamples,nbExamples), dtype=int)
- datap = np.array([signLabels(labels) for labels in labelsByView])
- nbRepet = nbExamples/nb_view
- for j in range(nb_view):
- for i in range(nbRepet):
- data[:, j*50+i] = datap[:, j]
+def analyzeLabels(labelsArrays, realLabels, results):
+ mono, multi = results
+ classifiersNames = genNamesFromRes(mono, multi)
+ nbClassifiers = len(classifiersNames)
+ nbExamples = realLabels.shape[0]
+ nbIter = 20
+ data = np.zeros((nbExamples, nbClassifiers*nbIter))
+ tempData = np.array([labelsArray == realLabels for labelsArray in np.transpose(labelsArrays)]).astype(int)
+ for classifierIndex in range(nbClassifiers):
+ for iterIndex in range(nbIter):
+ data[:,classifierIndex*nbIter+iterIndex] = tempData[classifierIndex,:]
+ fig = pylab.figure(figsize=(30,20))
+ cmap = mpl.colors.ListedColormap(['red','green'])
+ bounds=[-0.5,0.5,1.5]
+ norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
- cax = ax.imshow(data, interpolation='nearest', cmap=cm.coolwarm)
- ax.set_title('Labels per view')
+ cax = plt.imshow(data, interpolation='nearest', cmap=cmap, norm=norm)
+ plt.title('Error on examples depending on the classifier')
+ ticks = np.arange(0, nbClassifiers*nbIter, nbIter)
+ labels = classifiersNames
+ plt.xticks(ticks, labels, rotation="vertical")
cbar = fig.colorbar(cax, ticks=[0, 1])
- cbar.ax.set_yticklabels(['-1', ' 1']) # vertically oriented colorbar
- plt.show()
- fig.savefig("Results/"+time.strftime("%Y%m%d-%H%M%S")+"-"+name+"-labels.png")
+ cbar.ax.set_yticklabels(['Wrong', ' Right'])
+ fig.savefig("Results/"+time.strftime("%Y%m%d-%H%M%S")+"error_analysis.png")
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170920-164255error_analysis.png b/Code/MonoMutliViewClassifiers/Results/20170920-164255error_analysis.png
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diff --git a/Code/MonoMutliViewClassifiers/Results/20170921-103136error_analysis.png b/Code/MonoMutliViewClassifiers/Results/20170921-103136error_analysis.png
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diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-142442-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-142442-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-142458-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-142458-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-142511-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-142511-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-142614-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-142614-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-142635-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-142635-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-142814-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-142814-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-142841-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-142841-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-142951-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-142951-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..ebdc774e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-142951-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,12 @@
+2017-09-22 14:29:58,384 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2017-09-22 14:29:58,385 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.08002225 Gbytes /!\
+2017-09-22 14:30:02,552 DEBUG: Start: Creating datasets for multiprocessing
+2017-09-22 14:30:02,554 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:30:02,870 DEBUG: Start: Loading data
+2017-09-22 14:30:02,870 DEBUG: Start: Loading data
+2017-09-22 14:30:02,882 DEBUG: Done: Loading data
+2017-09-22 14:30:02,882 DEBUG: Done: Loading data
+2017-09-22 14:30:02,882 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:30:02,882 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:30:02,882 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:30:02,882 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-143334-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-143334-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-143350-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-143350-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..aa0ed894
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-143350-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,10 @@
+2017-09-22 14:33:56,670 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:33:56,673 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:33:56,746 DEBUG: Start: Loading data
+2017-09-22 14:33:56,746 DEBUG: Start: Loading data
+2017-09-22 14:33:56,757 DEBUG: Done: Loading data
+2017-09-22 14:33:56,757 DEBUG: Done: Loading data
+2017-09-22 14:33:56,758 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:33:56,758 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:33:56,758 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:33:56,758 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-143429-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-143429-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..8b4ad09d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-143429-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,10 @@
+2017-09-22 14:34:35,133 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:34:35,136 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:34:35,207 DEBUG: Start: Loading data
+2017-09-22 14:34:35,208 DEBUG: Start: Loading data
+2017-09-22 14:34:35,218 DEBUG: Done: Loading data
+2017-09-22 14:34:35,219 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:34:35,219 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:34:35,219 DEBUG: Done: Loading data
+2017-09-22 14:34:35,219 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:34:35,220 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-143518-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-143518-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..eb50ff09
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-143518-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,10 @@
+2017-09-22 14:35:24,223 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:35:24,225 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:35:24,302 DEBUG: Start: Loading data
+2017-09-22 14:35:24,303 DEBUG: Start: Loading data
+2017-09-22 14:35:24,316 DEBUG: Done: Loading data
+2017-09-22 14:35:24,316 DEBUG: Done: Loading data
+2017-09-22 14:35:24,316 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:35:24,316 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:35:24,316 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:35:24,316 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-143626-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-143626-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..88ced8d1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-143626-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,10 @@
+2017-09-22 14:36:32,769 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:36:32,772 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:36:32,843 DEBUG: Start: Loading data
+2017-09-22 14:36:32,843 DEBUG: Start: Loading data
+2017-09-22 14:36:32,857 DEBUG: Done: Loading data
+2017-09-22 14:36:32,857 DEBUG: Done: Loading data
+2017-09-22 14:36:32,857 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:36:32,857 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:36:32,858 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:36:32,858 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-143718-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-143718-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..30acdc74
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-143718-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,10 @@
+2017-09-22 14:37:24,159 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:37:24,162 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:37:24,232 DEBUG: Start: Loading data
+2017-09-22 14:37:24,232 DEBUG: Start: Loading data
+2017-09-22 14:37:24,246 DEBUG: Done: Loading data
+2017-09-22 14:37:24,246 DEBUG: Done: Loading data
+2017-09-22 14:37:24,247 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:37:24,247 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:37:24,247 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:37:24,247 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-143818-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-143818-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..a7327f60
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-143818-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,10 @@
+2017-09-22 14:38:23,950 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:38:23,953 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:38:24,026 DEBUG: Start: Loading data
+2017-09-22 14:38:24,026 DEBUG: Start: Loading data
+2017-09-22 14:38:24,039 DEBUG: Done: Loading data
+2017-09-22 14:38:24,039 DEBUG: Done: Loading data
+2017-09-22 14:38:24,039 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:38:24,040 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:38:24,040 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:38:24,040 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-143842-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-143842-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..37edfd4e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-143842-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,10 @@
+2017-09-22 14:38:48,037 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:38:48,039 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:38:48,110 DEBUG: Start: Loading data
+2017-09-22 14:38:48,110 DEBUG: Start: Loading data
+2017-09-22 14:38:48,124 DEBUG: Done: Loading data
+2017-09-22 14:38:48,124 DEBUG: Done: Loading data
+2017-09-22 14:38:48,125 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:38:48,125 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:38:48,125 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:38:48,125 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144055-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-144055-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..19ca60e1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144055-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,10 @@
+2017-09-22 14:41:01,415 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:41:01,417 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:41:01,490 DEBUG: Start: Loading data
+2017-09-22 14:41:01,490 DEBUG: Start: Loading data
+2017-09-22 14:41:01,504 DEBUG: Done: Loading data
+2017-09-22 14:41:01,504 DEBUG: Done: Loading data
+2017-09-22 14:41:01,504 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:41:01,504 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:41:01,504 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:41:01,504 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144134-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-144134-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..4ab39e60
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144134-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,10 @@
+2017-09-22 14:41:40,004 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:41:40,006 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:41:40,077 DEBUG: Start: Loading data
+2017-09-22 14:41:40,077 DEBUG: Start: Loading data
+2017-09-22 14:41:40,088 DEBUG: Done: Loading data
+2017-09-22 14:41:40,089 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:41:40,089 DEBUG: Done: Loading data
+2017-09-22 14:41:40,089 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:41:40,089 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:41:40,089 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144153-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-144153-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..7590aec7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144153-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,10 @@
+2017-09-22 14:41:59,904 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:41:59,906 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:41:59,979 DEBUG: Start: Loading data
+2017-09-22 14:41:59,979 DEBUG: Start: Loading data
+2017-09-22 14:41:59,990 DEBUG: Done: Loading data
+2017-09-22 14:41:59,990 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:41:59,990 DEBUG: Done: Loading data
+2017-09-22 14:41:59,990 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:41:59,990 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:41:59,990 DEBUG: Start: Determine Train/Test split for iteration 1
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144355-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-144355-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144358-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-144358-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..66463c67
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144358-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,1110 @@
+2017-09-22 14:44:04,106 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:44:04,108 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:44:04,179 DEBUG: Start: Loading data
+2017-09-22 14:44:04,179 DEBUG: Start: Loading data
+2017-09-22 14:44:04,193 DEBUG: Done: Loading data
+2017-09-22 14:44:04,193 DEBUG: Done: Loading data
+2017-09-22 14:44:04,193 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:44:04,193 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:44:04,194 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:04,194 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:04,240 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:44:04,240 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:44:04,240 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:04,241 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 14:44:04,244 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:44:04,244 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:44:04,244 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:04,244 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 14:44:06,280 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:06,281 DEBUG: Start: Training
+2017-09-22 14:44:06,785 DEBUG: Done: Training
+2017-09-22 14:44:06,785 DEBUG: Start: Predicting
+2017-09-22 14:44:06,797 DEBUG: Done: Predicting
+2017-09-22 14:44:06,797 DEBUG: Info: Time for training and predicting: 2.61771392822[s]
+2017-09-22 14:44:06,797 DEBUG: Start: Getting Results
+2017-09-22 14:44:06,826 DEBUG: Done: Getting Results
+2017-09-22 14:44:06,827 INFO: Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.879895561358, with STD : 0.0
+accuracy_score on test : 0.711656441718, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.879895561358
+ - Score on test : 0.711656441718
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.877005347594
+ - Score on test : 0.694805194805
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.877005347594
+ - Score on test : 0.694805194805
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.120104438642
+ - Score on test : 0.288343558282
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.879895561358
+ - Score on test : 0.711656441718
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.760631611356
+ - Score on test : 0.425917811428
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.898630136986
+ - Score on test : 0.737931034483
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.856396866841
+ - Score on test : 0.656441717791
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.879895561358
+ - Score on test : 0.711656441718
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.120104438642
+ - Score on test : 0.288343558282
+
+
+ Classification took 0:00:02
+2017-09-22 14:44:06,827 INFO: Done: Result Analysis
+2017-09-22 14:44:06,921 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:06,921 DEBUG: Start: Training
+2017-09-22 14:44:07,795 DEBUG: Done: Training
+2017-09-22 14:44:07,795 DEBUG: Start: Predicting
+2017-09-22 14:44:07,811 DEBUG: Done: Predicting
+2017-09-22 14:44:07,811 DEBUG: Info: Time for training and predicting: 3.63157486916[s]
+2017-09-22 14:44:07,811 DEBUG: Start: Getting Results
+2017-09-22 14:44:07,839 DEBUG: Done: Getting Results
+2017-09-22 14:44:07,839 INFO: Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Adaboost with num_esimators : 5, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None,
+ min_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.65671641791
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.65671641791
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.294928439892
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.639534883721
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.674846625767
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:03
+2017-09-22 14:44:07,861 INFO: Done: Result Analysis
+2017-09-22 14:44:07,944 DEBUG: Start: Loading data
+2017-09-22 14:44:07,944 DEBUG: Start: Loading data
+2017-09-22 14:44:07,957 DEBUG: Done: Loading data
+2017-09-22 14:44:07,957 DEBUG: Done: Loading data
+2017-09-22 14:44:07,957 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 14:44:07,957 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 14:44:07,958 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:07,958 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:07,993 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:44:07,994 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:44:07,994 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:07,994 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 14:44:08,001 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:44:08,001 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:44:08,001 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:08,001 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 14:44:08,585 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:08,585 DEBUG: Start: Training
+2017-09-22 14:44:08,765 DEBUG: Done: Training
+2017-09-22 14:44:08,765 DEBUG: Start: Predicting
+2017-09-22 14:44:08,820 DEBUG: Done: Predicting
+2017-09-22 14:44:08,820 DEBUG: Info: Time for training and predicting: 0.8758289814[s]
+2017-09-22 14:44:08,820 DEBUG: Start: Getting Results
+2017-09-22 14:44:08,849 DEBUG: Done: Getting Results
+2017-09-22 14:44:08,849 INFO: Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.88772845953, with STD : 0.0
+accuracy_score on test : 0.699386503067, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.88772845953
+ - Score on test : 0.699386503067
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.883783783784
+ - Score on test : 0.675496688742
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.883783783784
+ - Score on test : 0.675496688742
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.11227154047
+ - Score on test : 0.300613496933
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.88772845953
+ - Score on test : 0.699386503067
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.777249922224
+ - Score on test : 0.403167163571
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.915966386555
+ - Score on test : 0.73381294964
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.853785900783
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.88772845953
+ - Score on test : 0.699386503067
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.11227154047
+ - Score on test : 0.300613496933
+
+
+ Classification took 0:00:00
+2017-09-22 14:44:08,849 INFO: Done: Result Analysis
+2017-09-22 14:44:10,585 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:10,585 DEBUG: Start: Training
+2017-09-22 14:44:10,632 DEBUG: Done: Training
+2017-09-22 14:44:10,632 DEBUG: Start: Predicting
+2017-09-22 14:44:17,801 DEBUG: Done: Predicting
+2017-09-22 14:44:17,801 DEBUG: Info: Time for training and predicting: 9.85622215271[s]
+2017-09-22 14:44:17,801 DEBUG: Start: Getting Results
+2017-09-22 14:44:17,828 DEBUG: Done: Getting Results
+2017-09-22 14:44:17,828 INFO: Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.707571801567, with STD : 0.0
+accuracy_score on test : 0.634969325153, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.707571801567
+ - Score on test : 0.634969325153
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.742528735632
+ - Score on test : 0.687664041995
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.742528735632
+ - Score on test : 0.687664041995
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.292428198433
+ - Score on test : 0.365030674847
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.707571801567
+ - Score on test : 0.634969325153
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.431350734688
+ - Score on test : 0.286756025235
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.663244353183
+ - Score on test : 0.600917431193
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.843342036554
+ - Score on test : 0.803680981595
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.707571801567
+ - Score on test : 0.634969325153
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.292428198433
+ - Score on test : 0.365030674847
+
+
+ Classification took 0:00:09
+2017-09-22 14:44:17,828 INFO: Done: Result Analysis
+2017-09-22 14:44:17,931 DEBUG: Start: Loading data
+2017-09-22 14:44:17,931 DEBUG: Start: Loading data
+2017-09-22 14:44:17,945 DEBUG: Done: Loading data
+2017-09-22 14:44:17,945 DEBUG: Done: Loading data
+2017-09-22 14:44:17,945 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 14:44:17,945 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 14:44:17,946 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:17,946 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:17,975 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:44:17,975 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:44:17,975 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:44:17,975 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:44:17,975 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:17,975 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:17,975 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 14:44:17,975 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 14:44:18,645 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:18,645 DEBUG: Start: Training
+2017-09-22 14:44:18,706 DEBUG: Done: Training
+2017-09-22 14:44:18,706 DEBUG: Start: Predicting
+2017-09-22 14:44:18,718 DEBUG: Done: Predicting
+2017-09-22 14:44:18,718 DEBUG: Info: Time for training and predicting: 0.786246061325[s]
+2017-09-22 14:44:18,718 DEBUG: Start: Getting Results
+2017-09-22 14:44:18,760 DEBUG: Done: Getting Results
+2017-09-22 14:44:18,760 INFO: Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.664490861619, with STD : 0.0
+accuracy_score on test : 0.70245398773, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.664490861619
+ - Score on test : 0.70245398773
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.655957161981
+ - Score on test : 0.701538461538
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.655957161981
+ - Score on test : 0.701538461538
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.335509138381
+ - Score on test : 0.29754601227
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.664490861619
+ - Score on test : 0.70245398773
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.329387282132
+ - Score on test : 0.404915595608
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.673076923077
+ - Score on test : 0.703703703704
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.639686684073
+ - Score on test : 0.699386503067
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.664490861619
+ - Score on test : 0.70245398773
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.335509138381
+ - Score on test : 0.29754601227
+
+
+ Classification took 0:00:00
+2017-09-22 14:44:18,761 INFO: Done: Result Analysis
+2017-09-22 14:44:21,563 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:21,563 DEBUG: Start: Training
+2017-09-22 14:44:27,074 DEBUG: Done: Training
+2017-09-22 14:44:27,074 DEBUG: Start: Predicting
+2017-09-22 14:44:29,936 DEBUG: Done: Predicting
+2017-09-22 14:44:29,936 DEBUG: Info: Time for training and predicting: 12.0045089722[s]
+2017-09-22 14:44:29,936 DEBUG: Start: Getting Results
+2017-09-22 14:44:29,963 DEBUG: Done: Getting Results
+2017-09-22 14:44:29,963 INFO: Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.653374233129, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.676217765043
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.676217765043
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.346625766871
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.30984858301
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.634408602151
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.723926380368
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.346625766871
+
+
+ Classification took 0:00:12
+2017-09-22 14:44:29,964 INFO: Done: Result Analysis
+2017-09-22 14:44:30,120 DEBUG: Start: Loading data
+2017-09-22 14:44:30,120 DEBUG: Start: Loading data
+2017-09-22 14:44:30,134 DEBUG: Done: Loading data
+2017-09-22 14:44:30,134 DEBUG: Done: Loading data
+2017-09-22 14:44:30,134 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 14:44:30,134 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 14:44:30,135 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:30,135 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:30,164 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:44:30,164 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:44:30,164 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:44:30,164 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:44:30,164 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:30,164 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:30,165 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 14:44:30,165 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 14:44:35,098 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:35,098 DEBUG: Start: Training
+2017-09-22 14:44:36,476 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:36,476 DEBUG: Start: Training
+2017-09-22 14:44:42,311 DEBUG: Done: Training
+2017-09-22 14:44:42,311 DEBUG: Start: Predicting
+2017-09-22 14:44:46,225 DEBUG: Done: Training
+2017-09-22 14:44:46,225 DEBUG: Start: Predicting
+2017-09-22 14:44:46,423 DEBUG: Done: Predicting
+2017-09-22 14:44:46,423 DEBUG: Info: Time for training and predicting: 16.3026628494[s]
+2017-09-22 14:44:46,423 DEBUG: Start: Getting Results
+2017-09-22 14:44:46,468 DEBUG: Done: Getting Results
+2017-09-22 14:44:46,468 INFO: Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.929503916449, with STD : 0.0
+accuracy_score on test : 0.708588957055, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.708588957055
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.9296875
+ - Score on test : 0.727793696275
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.9296875
+ - Score on test : 0.727793696275
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0704960835509
+ - Score on test : 0.291411042945
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.708588957055
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.859019545097
+ - Score on test : 0.421394072893
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.927272727273
+ - Score on test : 0.682795698925
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.932114882507
+ - Score on test : 0.779141104294
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.708588957055
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0704960835509
+ - Score on test : 0.291411042945
+
+
+ Classification took 0:00:16
+2017-09-22 14:44:46,469 INFO: Done: Result Analysis
+2017-09-22 14:44:51,890 DEBUG: Done: Predicting
+2017-09-22 14:44:51,890 DEBUG: Info: Time for training and predicting: 21.7691369057[s]
+2017-09-22 14:44:51,890 DEBUG: Start: Getting Results
+2017-09-22 14:44:51,917 DEBUG: Done: Getting Results
+2017-09-22 14:44:51,917 INFO: Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.936031331593, with STD : 0.0
+accuracy_score on test : 0.564417177914, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.936031331593
+ - Score on test : 0.564417177914
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.932784636488
+ - Score on test : 0.403361344538
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.932784636488
+ - Score on test : 0.403361344538
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0639686684073
+ - Score on test : 0.435582822086
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.936031331593
+ - Score on test : 0.564417177914
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.8761607063
+ - Score on test : 0.153056508587
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.982658959538
+ - Score on test : 0.64
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.88772845953
+ - Score on test : 0.294478527607
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.936031331593
+ - Score on test : 0.564417177914
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0639686684073
+ - Score on test : 0.435582822086
+
+
+ Classification took 0:00:21
+2017-09-22 14:44:51,917 INFO: Done: Result Analysis
+2017-09-22 14:44:52,016 DEBUG: Start: Loading data
+2017-09-22 14:44:52,017 DEBUG: Start: Loading data
+2017-09-22 14:44:52,028 DEBUG: Done: Loading data
+2017-09-22 14:44:52,028 DEBUG: Done: Loading data
+2017-09-22 14:44:52,028 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:44:52,028 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:44:52,029 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:52,029 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:52,057 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:44:52,057 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:44:52,057 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:44:52,057 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:52,057 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:44:52,058 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:52,058 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 14:44:52,058 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 14:44:53,296 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:53,296 DEBUG: Start: Training
+2017-09-22 14:44:53,647 DEBUG: Done: Training
+2017-09-22 14:44:53,647 DEBUG: Start: Predicting
+2017-09-22 14:44:53,659 DEBUG: Done: Predicting
+2017-09-22 14:44:53,660 DEBUG: Info: Time for training and predicting: 1.64258408546[s]
+2017-09-22 14:44:53,660 DEBUG: Start: Getting Results
+2017-09-22 14:44:53,701 DEBUG: Done: Getting Results
+2017-09-22 14:44:53,701 INFO: Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.8772845953, with STD : 0.0
+accuracy_score on test : 0.684049079755, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.8772845953
+ - Score on test : 0.684049079755
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.880407124682
+ - Score on test : 0.692537313433
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.880407124682
+ - Score on test : 0.692537313433
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.1227154047
+ - Score on test : 0.315950920245
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.8772845953
+ - Score on test : 0.684049079755
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.755600100768
+ - Score on test : 0.368660549865
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.858560794045
+ - Score on test : 0.674418604651
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.903394255875
+ - Score on test : 0.711656441718
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.8772845953
+ - Score on test : 0.684049079755
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.1227154047
+ - Score on test : 0.315950920245
+
+
+ Classification took 0:00:01
+2017-09-22 14:44:53,701 INFO: Done: Result Analysis
+2017-09-22 14:44:53,730 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:53,730 DEBUG: Start: Training
+2017-09-22 14:44:54,116 DEBUG: Done: Training
+2017-09-22 14:44:54,116 DEBUG: Start: Predicting
+2017-09-22 14:44:54,129 DEBUG: Done: Predicting
+2017-09-22 14:44:54,129 DEBUG: Info: Time for training and predicting: 2.11217498779[s]
+2017-09-22 14:44:54,129 DEBUG: Start: Getting Results
+2017-09-22 14:44:54,156 DEBUG: Done: Getting Results
+2017-09-22 14:44:54,156 INFO: Classification on awaexp database for lss-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.664634146341
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.664634146341
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.325177853144
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.660606060606
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:02
+2017-09-22 14:44:54,156 INFO: Done: Result Analysis
+2017-09-22 14:44:54,277 DEBUG: Start: Loading data
+2017-09-22 14:44:54,277 DEBUG: Start: Loading data
+2017-09-22 14:44:54,288 DEBUG: Done: Loading data
+2017-09-22 14:44:54,288 DEBUG: Done: Loading data
+2017-09-22 14:44:54,288 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 14:44:54,288 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 14:44:54,289 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:54,289 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:44:54,317 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:44:54,317 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:44:54,317 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:44:54,317 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:44:54,317 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:54,317 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:44:54,317 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 14:44:54,317 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 14:44:54,801 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:54,801 DEBUG: Start: Training
+2017-09-22 14:44:54,945 DEBUG: Done: Training
+2017-09-22 14:44:54,945 DEBUG: Start: Predicting
+2017-09-22 14:44:54,997 DEBUG: Done: Predicting
+2017-09-22 14:44:54,998 DEBUG: Info: Time for training and predicting: 0.719907999039[s]
+2017-09-22 14:44:54,998 DEBUG: Start: Getting Results
+2017-09-22 14:44:55,026 DEBUG: Done: Getting Results
+2017-09-22 14:44:55,026 INFO: Classification on awaexp database for lss-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.89817232376, with STD : 0.0
+accuracy_score on test : 0.726993865031, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.89817232376
+ - Score on test : 0.726993865031
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.896551724138
+ - Score on test : 0.702341137124
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.896551724138
+ - Score on test : 0.702341137124
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.10182767624
+ - Score on test : 0.273006134969
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.89817232376
+ - Score on test : 0.726993865031
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.796735808844
+ - Score on test : 0.460347156659
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.911051212938
+ - Score on test : 0.772058823529
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.882506527415
+ - Score on test : 0.644171779141
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.89817232376
+ - Score on test : 0.726993865031
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.10182767624
+ - Score on test : 0.273006134969
+
+
+ Classification took 0:00:00
+2017-09-22 14:44:55,027 INFO: Done: Result Analysis
+2017-09-22 14:44:56,175 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:44:56,175 DEBUG: Start: Training
+2017-09-22 14:44:56,204 DEBUG: Done: Training
+2017-09-22 14:44:56,205 DEBUG: Start: Predicting
+2017-09-22 14:45:01,385 DEBUG: Done: Predicting
+2017-09-22 14:45:01,385 DEBUG: Info: Time for training and predicting: 7.10769701004[s]
+2017-09-22 14:45:01,385 DEBUG: Start: Getting Results
+2017-09-22 14:45:01,423 DEBUG: Done: Getting Results
+2017-09-22 14:45:01,423 INFO: Classification on awaexp database for lss-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.725848563969, with STD : 0.0
+accuracy_score on test : 0.628834355828, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.725848563969
+ - Score on test : 0.628834355828
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.6875
+ - Score on test : 0.563176895307
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.6875
+ - Score on test : 0.563176895307
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.274151436031
+ - Score on test : 0.371165644172
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.725848563969
+ - Score on test : 0.628834355828
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.465948579669
+ - Score on test : 0.270164906057
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.799307958478
+ - Score on test : 0.684210526316
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.603133159269
+ - Score on test : 0.478527607362
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.725848563969
+ - Score on test : 0.628834355828
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.274151436031
+ - Score on test : 0.371165644172
+
+
+ Classification took 0:00:07
+2017-09-22 14:45:01,423 INFO: Done: Result Analysis
+2017-09-22 14:45:01,546 DEBUG: Start: Loading data
+2017-09-22 14:45:01,547 DEBUG: Start: Loading data
+2017-09-22 14:45:01,554 DEBUG: Done: Loading data
+2017-09-22 14:45:01,554 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 14:45:01,554 DEBUG: Done: Loading data
+2017-09-22 14:45:01,555 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:45:01,555 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 14:45:01,555 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:45:01,572 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:45:01,572 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:45:01,572 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:45:01,572 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 14:45:01,573 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:45:01,573 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:45:01,573 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:45:01,573 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 14:45:01,797 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:45:01,797 DEBUG: Start: Training
+2017-09-22 14:45:01,821 DEBUG: Done: Training
+2017-09-22 14:45:01,822 DEBUG: Start: Predicting
+2017-09-22 14:45:01,830 DEBUG: Done: Predicting
+2017-09-22 14:45:01,831 DEBUG: Info: Time for training and predicting: 0.283898830414[s]
+2017-09-22 14:45:01,831 DEBUG: Start: Getting Results
+2017-09-22 14:45:01,871 DEBUG: Done: Getting Results
+2017-09-22 14:45:01,871 INFO: Classification on awaexp database for lss-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.869451697128, with STD : 0.0
+accuracy_score on test : 0.739263803681, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.869451697128
+ - Score on test : 0.739263803681
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.850299401198
+ - Score on test : 0.681647940075
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.850299401198
+ - Score on test : 0.681647940075
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.130548302872
+ - Score on test : 0.260736196319
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.869451697128
+ - Score on test : 0.739263803681
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.764348587476
+ - Score on test : 0.51333567333
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.99649122807
+ - Score on test : 0.875
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.741514360313
+ - Score on test : 0.558282208589
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.869451697128
+ - Score on test : 0.739263803681
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.130548302872
+ - Score on test : 0.260736196319
+
+
+ Classification took 0:00:00
+2017-09-22 14:45:01,872 INFO: Done: Result Analysis
+2017-09-22 14:45:03,966 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:45:03,967 DEBUG: Start: Training
+2017-09-22 14:45:07,715 DEBUG: Done: Training
+2017-09-22 14:45:07,715 DEBUG: Start: Predicting
+2017-09-22 14:45:09,521 DEBUG: Done: Predicting
+2017-09-22 14:45:09,522 DEBUG: Info: Time for training and predicting: 7.97445011139[s]
+2017-09-22 14:45:09,522 DEBUG: Start: Getting Results
+2017-09-22 14:45:09,549 DEBUG: Done: Getting Results
+2017-09-22 14:45:09,549 INFO: Classification on awaexp database for lss-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.782208588957, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.782208588957
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.781538461538
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.781538461538
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.217791411043
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.782208588957
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.564427799938
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.783950617284
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.782208588957
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.217791411043
+
+
+ Classification took 0:00:07
+2017-09-22 14:45:09,549 INFO: Done: Result Analysis
+2017-09-22 14:45:09,620 DEBUG: Start: Loading data
+2017-09-22 14:45:09,620 DEBUG: Start: Loading data
+2017-09-22 14:45:09,628 DEBUG: Done: Loading data
+2017-09-22 14:45:09,628 DEBUG: Done: Loading data
+2017-09-22 14:45:09,628 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 14:45:09,628 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 14:45:09,629 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:45:09,629 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:45:09,647 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:45:09,647 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:45:09,647 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:45:09,647 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:45:09,647 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:45:09,647 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:45:09,647 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 14:45:09,647 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 14:45:11,911 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:45:11,912 DEBUG: Start: Training
+2017-09-22 14:45:13,964 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:45:13,964 DEBUG: Start: Training
+2017-09-22 14:45:15,835 DEBUG: Done: Training
+2017-09-22 14:45:15,835 DEBUG: Start: Predicting
+2017-09-22 14:45:17,760 DEBUG: Done: Predicting
+2017-09-22 14:45:17,760 DEBUG: Info: Time for training and predicting: 8.14053297043[s]
+2017-09-22 14:45:17,761 DEBUG: Start: Getting Results
+2017-09-22 14:45:17,789 DEBUG: Done: Getting Results
+2017-09-22 14:45:17,790 INFO: Classification on awaexp database for lss-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.530026109661, with STD : 0.0
+accuracy_score on test : 0.457055214724, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.530026109661
+ - Score on test : 0.457055214724
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.669724770642
+ - Score on test : 0.598639455782
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.669724770642
+ - Score on test : 0.598639455782
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.469973890339
+ - Score on test : 0.542944785276
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.530026109661
+ - Score on test : 0.457055214724
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.112613932219
+ - Score on test : -0.121195088186
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.516265912306
+ - Score on test : 0.474820143885
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.953002610966
+ - Score on test : 0.80981595092
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.530026109661
+ - Score on test : 0.457055214724
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.469973890339
+ - Score on test : 0.542944785276
+
+
+ Classification took 0:00:08
+2017-09-22 14:45:17,790 INFO: Done: Result Analysis
+2017-09-22 14:45:20,806 DEBUG: Done: Training
+2017-09-22 14:45:20,806 DEBUG: Start: Predicting
+2017-09-22 14:45:24,334 DEBUG: Done: Predicting
+2017-09-22 14:45:24,335 DEBUG: Info: Time for training and predicting: 14.7147290707[s]
+2017-09-22 14:45:24,335 DEBUG: Start: Getting Results
+2017-09-22 14:45:24,361 DEBUG: Done: Getting Results
+2017-09-22 14:45:24,361 INFO: Classification on awaexp database for lss-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.524539877301, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.524539877301
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670912951168
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670912951168
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.475460122699
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.524539877301
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.10744306187
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.512987012987
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.969325153374
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.524539877301
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.475460122699
+
+
+ Classification took 0:00:14
+2017-09-22 14:45:24,361 INFO: Done: Result Analysis
+2017-09-22 14:45:24,514 INFO: ### Main Programm for Multiview Classification
+2017-09-22 14:45:24,514 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 14:45:24,515 INFO: ### Main Programm for Multiview Classification
+2017-09-22 14:45:24,515 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 14:45:24,515 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 14:45:24,516 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 14:45:24,516 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 14:45:24,516 INFO: Done: Read Database Files
+2017-09-22 14:45:24,516 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 14:45:24,517 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 14:45:24,517 INFO: Done: Read Database Files
+2017-09-22 14:45:24,517 INFO: Start: Determine validation split for ratio 0.7
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144406Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144406Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..4014d5d7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144406Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.879895561358, with STD : 0.0
+accuracy_score on test : 0.711656441718, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.879895561358
+ - Score on test : 0.711656441718
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.877005347594
+ - Score on test : 0.694805194805
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.877005347594
+ - Score on test : 0.694805194805
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.120104438642
+ - Score on test : 0.288343558282
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.879895561358
+ - Score on test : 0.711656441718
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.760631611356
+ - Score on test : 0.425917811428
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.898630136986
+ - Score on test : 0.737931034483
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.856396866841
+ - Score on test : 0.656441717791
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.879895561358
+ - Score on test : 0.711656441718
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.120104438642
+ - Score on test : 0.288343558282
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144407Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144407Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..941338b7
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144407Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Adaboost with num_esimators : 5, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None,
+ min_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.65671641791
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.65671641791
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.294928439892
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.639534883721
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.674846625767
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144408Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144408Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..9994c27e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144408Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.88772845953, with STD : 0.0
+accuracy_score on test : 0.699386503067, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.88772845953
+ - Score on test : 0.699386503067
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.883783783784
+ - Score on test : 0.675496688742
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.883783783784
+ - Score on test : 0.675496688742
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.11227154047
+ - Score on test : 0.300613496933
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.88772845953
+ - Score on test : 0.699386503067
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.777249922224
+ - Score on test : 0.403167163571
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.915966386555
+ - Score on test : 0.73381294964
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.853785900783
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.88772845953
+ - Score on test : 0.699386503067
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.11227154047
+ - Score on test : 0.300613496933
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144417Results-KNN--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144417Results-KNN--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..1220eb92
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144417Results-KNN--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.707571801567, with STD : 0.0
+accuracy_score on test : 0.634969325153, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.707571801567
+ - Score on test : 0.634969325153
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.742528735632
+ - Score on test : 0.687664041995
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.742528735632
+ - Score on test : 0.687664041995
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.292428198433
+ - Score on test : 0.365030674847
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.707571801567
+ - Score on test : 0.634969325153
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.431350734688
+ - Score on test : 0.286756025235
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.663244353183
+ - Score on test : 0.600917431193
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.843342036554
+ - Score on test : 0.803680981595
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.707571801567
+ - Score on test : 0.634969325153
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.292428198433
+ - Score on test : 0.365030674847
+
+
+ Classification took 0:00:09
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144418Results-SGD--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144418Results-SGD--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..9e3ed886
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144418Results-SGD--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.664490861619, with STD : 0.0
+accuracy_score on test : 0.70245398773, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.664490861619
+ - Score on test : 0.70245398773
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.655957161981
+ - Score on test : 0.701538461538
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.655957161981
+ - Score on test : 0.701538461538
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.335509138381
+ - Score on test : 0.29754601227
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.664490861619
+ - Score on test : 0.70245398773
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.329387282132
+ - Score on test : 0.404915595608
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.673076923077
+ - Score on test : 0.703703703704
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.639686684073
+ - Score on test : 0.699386503067
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.664490861619
+ - Score on test : 0.70245398773
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.335509138381
+ - Score on test : 0.29754601227
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144429Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144429Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..fdea905a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144429Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.653374233129, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.676217765043
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.676217765043
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.346625766871
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.30984858301
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.634408602151
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.723926380368
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.346625766871
+
+
+ Classification took 0:00:12
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144446Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144446Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..7603a96f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144446Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.929503916449, with STD : 0.0
+accuracy_score on test : 0.708588957055, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.708588957055
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.9296875
+ - Score on test : 0.727793696275
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.9296875
+ - Score on test : 0.727793696275
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0704960835509
+ - Score on test : 0.291411042945
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.708588957055
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.859019545097
+ - Score on test : 0.421394072893
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.927272727273
+ - Score on test : 0.682795698925
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.932114882507
+ - Score on test : 0.779141104294
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.708588957055
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0704960835509
+ - Score on test : 0.291411042945
+
+
+ Classification took 0:00:16
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144451Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144451Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..8cf56278
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144451Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.936031331593, with STD : 0.0
+accuracy_score on test : 0.564417177914, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.936031331593
+ - Score on test : 0.564417177914
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.932784636488
+ - Score on test : 0.403361344538
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.932784636488
+ - Score on test : 0.403361344538
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0639686684073
+ - Score on test : 0.435582822086
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.936031331593
+ - Score on test : 0.564417177914
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.8761607063
+ - Score on test : 0.153056508587
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.982658959538
+ - Score on test : 0.64
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.88772845953
+ - Score on test : 0.294478527607
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.936031331593
+ - Score on test : 0.564417177914
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0639686684073
+ - Score on test : 0.435582822086
+
+
+ Classification took 0:00:21
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144453Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144453Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..2b537539
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144453Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.8772845953, with STD : 0.0
+accuracy_score on test : 0.684049079755, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.8772845953
+ - Score on test : 0.684049079755
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.880407124682
+ - Score on test : 0.692537313433
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.880407124682
+ - Score on test : 0.692537313433
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.1227154047
+ - Score on test : 0.315950920245
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.8772845953
+ - Score on test : 0.684049079755
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.755600100768
+ - Score on test : 0.368660549865
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.858560794045
+ - Score on test : 0.674418604651
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.903394255875
+ - Score on test : 0.711656441718
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.8772845953
+ - Score on test : 0.684049079755
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.1227154047
+ - Score on test : 0.315950920245
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144454Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144454Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..e69ff308
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144454Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for lss-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.664634146341
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.664634146341
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.325177853144
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.660606060606
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144455Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144455Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..a8ff8c7d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144455Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.89817232376, with STD : 0.0
+accuracy_score on test : 0.726993865031, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 17, max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.89817232376
+ - Score on test : 0.726993865031
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.896551724138
+ - Score on test : 0.702341137124
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.896551724138
+ - Score on test : 0.702341137124
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.10182767624
+ - Score on test : 0.273006134969
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.89817232376
+ - Score on test : 0.726993865031
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.796735808844
+ - Score on test : 0.460347156659
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.911051212938
+ - Score on test : 0.772058823529
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.882506527415
+ - Score on test : 0.644171779141
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.89817232376
+ - Score on test : 0.726993865031
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.10182767624
+ - Score on test : 0.273006134969
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144501Results-KNN--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144501Results-KNN--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..4de72ebd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144501Results-KNN--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.725848563969, with STD : 0.0
+accuracy_score on test : 0.628834355828, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.725848563969
+ - Score on test : 0.628834355828
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.6875
+ - Score on test : 0.563176895307
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.6875
+ - Score on test : 0.563176895307
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.274151436031
+ - Score on test : 0.371165644172
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.725848563969
+ - Score on test : 0.628834355828
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.465948579669
+ - Score on test : 0.270164906057
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.799307958478
+ - Score on test : 0.684210526316
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.603133159269
+ - Score on test : 0.478527607362
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.725848563969
+ - Score on test : 0.628834355828
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.274151436031
+ - Score on test : 0.371165644172
+
+
+ Classification took 0:00:07
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144501Results-SGD--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144501Results-SGD--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..4473af4f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144501Results-SGD--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.869451697128, with STD : 0.0
+accuracy_score on test : 0.739263803681, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.869451697128
+ - Score on test : 0.739263803681
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.850299401198
+ - Score on test : 0.681647940075
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.850299401198
+ - Score on test : 0.681647940075
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.130548302872
+ - Score on test : 0.260736196319
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.869451697128
+ - Score on test : 0.739263803681
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.764348587476
+ - Score on test : 0.51333567333
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.99649122807
+ - Score on test : 0.875
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.741514360313
+ - Score on test : 0.558282208589
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.869451697128
+ - Score on test : 0.739263803681
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.130548302872
+ - Score on test : 0.260736196319
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144509Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144509Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..063ea1d6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144509Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.782208588957, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.782208588957
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.781538461538
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.781538461538
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.217791411043
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.782208588957
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.564427799938
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.783950617284
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.782208588957
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.217791411043
+
+
+ Classification took 0:00:07
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144517Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144517Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..a5fd967d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144517Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.530026109661, with STD : 0.0
+accuracy_score on test : 0.457055214724, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.530026109661
+ - Score on test : 0.457055214724
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.669724770642
+ - Score on test : 0.598639455782
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.669724770642
+ - Score on test : 0.598639455782
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.469973890339
+ - Score on test : 0.542944785276
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.530026109661
+ - Score on test : 0.457055214724
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.112613932219
+ - Score on test : -0.121195088186
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.516265912306
+ - Score on test : 0.474820143885
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.953002610966
+ - Score on test : 0.80981595092
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.530026109661
+ - Score on test : 0.457055214724
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.469973890339
+ - Score on test : 0.542944785276
+
+
+ Classification took 0:00:08
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-144524Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-144524Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..1adc63a6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-144524Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.524539877301, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 3089
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.524539877301
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670912951168
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670912951168
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.475460122699
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.524539877301
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.10744306187
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.512987012987
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.969325153374
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.524539877301
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.475460122699
+
+
+ Classification took 0:00:14
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145401-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-145401-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..7ebb6036
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145401-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,1110 @@
+2017-09-22 14:54:08,064 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:54:08,067 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:54:08,142 DEBUG: Start: Loading data
+2017-09-22 14:54:08,142 DEBUG: Start: Loading data
+2017-09-22 14:54:08,155 DEBUG: Done: Loading data
+2017-09-22 14:54:08,155 DEBUG: Done: Loading data
+2017-09-22 14:54:08,155 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:54:08,155 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:54:08,156 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:08,156 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:08,186 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:54:08,186 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:54:08,186 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:54:08,186 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:54:08,187 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:08,187 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:08,187 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 14:54:08,187 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 14:54:11,193 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:11,193 DEBUG: Start: Training
+2017-09-22 14:54:11,406 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:11,406 DEBUG: Start: Training
+2017-09-22 14:54:12,138 DEBUG: Done: Training
+2017-09-22 14:54:12,138 DEBUG: Start: Predicting
+2017-09-22 14:54:12,155 DEBUG: Done: Predicting
+2017-09-22 14:54:12,155 DEBUG: Info: Time for training and predicting: 4.01276493073[s]
+2017-09-22 14:54:12,155 DEBUG: Start: Getting Results
+2017-09-22 14:54:12,185 DEBUG: Done: Getting Results
+2017-09-22 14:54:12,185 INFO: Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.677914110429, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.677914110429
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.672897196262
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.672897196262
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.322085889571
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.677914110429
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.355995746702
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.683544303797
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.677914110429
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.322085889571
+
+
+ Classification took 0:00:04
+2017-09-22 14:54:12,185 INFO: Done: Result Analysis
+2017-09-22 14:54:12,288 DEBUG: Done: Training
+2017-09-22 14:54:12,289 DEBUG: Start: Predicting
+2017-09-22 14:54:12,308 DEBUG: Done: Predicting
+2017-09-22 14:54:12,308 DEBUG: Info: Time for training and predicting: 4.16530394554[s]
+2017-09-22 14:54:12,308 DEBUG: Start: Getting Results
+2017-09-22 14:54:12,335 DEBUG: Done: Getting Results
+2017-09-22 14:54:12,335 INFO: Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.628834355828, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Adaboost with num_esimators : 6, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None,
+ min_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.628834355828
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.618296529968
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.618296529968
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.371165644172
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.628834355828
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.258062384906
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.636363636364
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.601226993865
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.628834355828
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.371165644172
+
+
+ Classification took 0:00:04
+2017-09-22 14:54:12,336 INFO: Done: Result Analysis
+2017-09-22 14:54:12,406 DEBUG: Start: Loading data
+2017-09-22 14:54:12,406 DEBUG: Start: Loading data
+2017-09-22 14:54:12,419 DEBUG: Done: Loading data
+2017-09-22 14:54:12,419 DEBUG: Done: Loading data
+2017-09-22 14:54:12,420 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 14:54:12,420 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 14:54:12,420 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:12,420 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:12,451 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:54:12,451 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:54:12,451 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:54:12,451 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:54:12,451 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:12,451 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:12,451 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 14:54:12,451 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 14:54:12,672 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:12,672 DEBUG: Start: Training
+2017-09-22 14:54:12,696 DEBUG: Done: Training
+2017-09-22 14:54:12,696 DEBUG: Start: Predicting
+2017-09-22 14:54:12,719 DEBUG: Done: Predicting
+2017-09-22 14:54:12,719 DEBUG: Info: Time for training and predicting: 0.312060832977[s]
+2017-09-22 14:54:12,719 DEBUG: Start: Getting Results
+2017-09-22 14:54:12,762 DEBUG: Done: Getting Results
+2017-09-22 14:54:12,762 INFO: Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.791122715405, with STD : 0.0
+accuracy_score on test : 0.687116564417, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 1, max_depth : 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 : 0.791122715405
+ - Score on test : 0.687116564417
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.780821917808
+ - Score on test : 0.677215189873
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.780821917808
+ - Score on test : 0.677215189873
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.208877284595
+ - Score on test : 0.312883435583
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.791122715405
+ - Score on test : 0.687116564417
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.584834675032
+ - Score on test : 0.374939389614
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.821325648415
+ - Score on test : 0.699346405229
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.744125326371
+ - Score on test : 0.656441717791
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.791122715405
+ - Score on test : 0.687116564417
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.208877284595
+ - Score on test : 0.312883435583
+
+
+ Classification took 0:00:00
+2017-09-22 14:54:12,763 INFO: Done: Result Analysis
+2017-09-22 14:54:15,117 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:15,117 DEBUG: Start: Training
+2017-09-22 14:54:15,167 DEBUG: Done: Training
+2017-09-22 14:54:15,167 DEBUG: Start: Predicting
+2017-09-22 14:54:18,032 DEBUG: Done: Predicting
+2017-09-22 14:54:18,032 DEBUG: Info: Time for training and predicting: 5.62530589104[s]
+2017-09-22 14:54:18,033 DEBUG: Start: Getting Results
+2017-09-22 14:54:18,067 DEBUG: Done: Getting Results
+2017-09-22 14:54:18,067 INFO: Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.610429447853, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 1.0
+ - Score on test : 0.610429447853
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.609230769231
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.609230769231
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.389570552147
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.610429447853
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.22086305215
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.611111111111
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.60736196319
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.610429447853
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.389570552147
+
+
+ Classification took 0:00:05
+2017-09-22 14:54:18,067 INFO: Done: Result Analysis
+2017-09-22 14:54:18,182 DEBUG: Start: Loading data
+2017-09-22 14:54:18,182 DEBUG: Start: Loading data
+2017-09-22 14:54:18,196 DEBUG: Done: Loading data
+2017-09-22 14:54:18,196 DEBUG: Done: Loading data
+2017-09-22 14:54:18,196 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 14:54:18,196 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 14:54:18,196 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:18,196 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:18,226 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:54:18,226 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:54:18,226 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:54:18,226 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:54:18,226 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:18,226 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:18,226 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 14:54:18,226 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 14:54:18,808 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:18,808 DEBUG: Start: Training
+2017-09-22 14:54:18,950 DEBUG: Done: Training
+2017-09-22 14:54:18,951 DEBUG: Start: Predicting
+2017-09-22 14:54:18,961 DEBUG: Done: Predicting
+2017-09-22 14:54:18,961 DEBUG: Info: Time for training and predicting: 0.778836965561[s]
+2017-09-22 14:54:18,961 DEBUG: Start: Getting Results
+2017-09-22 14:54:18,993 DEBUG: Done: Getting Results
+2017-09-22 14:54:18,993 INFO: Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.569190600522, with STD : 0.0
+accuracy_score on test : 0.552147239264, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.569190600522
+ - Score on test : 0.552147239264
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.663265306122
+ - Score on test : 0.657276995305
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.663265306122
+ - Score on test : 0.657276995305
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.430809399478
+ - Score on test : 0.447852760736
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.569190600522
+ - Score on test : 0.552147239264
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.166857353772
+ - Score on test : 0.132068964403
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.544388609715
+ - Score on test : 0.532319391635
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.848563968668
+ - Score on test : 0.858895705521
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.569190600522
+ - Score on test : 0.552147239264
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.430809399478
+ - Score on test : 0.447852760736
+
+
+ Classification took 0:00:00
+2017-09-22 14:54:18,994 INFO: Done: Result Analysis
+2017-09-22 14:54:21,791 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:21,791 DEBUG: Start: Training
+2017-09-22 14:54:27,205 DEBUG: Done: Training
+2017-09-22 14:54:27,205 DEBUG: Start: Predicting
+2017-09-22 14:54:29,979 DEBUG: Done: Predicting
+2017-09-22 14:54:29,979 DEBUG: Info: Time for training and predicting: 11.7960519791[s]
+2017-09-22 14:54:29,979 DEBUG: Start: Getting Results
+2017-09-22 14:54:30,006 DEBUG: Done: Getting Results
+2017-09-22 14:54:30,006 INFO: Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.601226993865, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 8934
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.601226993865
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.606060606061
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.606060606061
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.398773006135
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.601226993865
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.202514974737
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.59880239521
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.613496932515
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.601226993865
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.398773006135
+
+
+ Classification took 0:00:11
+2017-09-22 14:54:30,006 INFO: Done: Result Analysis
+2017-09-22 14:54:30,168 DEBUG: Start: Loading data
+2017-09-22 14:54:30,168 DEBUG: Start: Loading data
+2017-09-22 14:54:30,181 DEBUG: Done: Loading data
+2017-09-22 14:54:30,181 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 14:54:30,181 DEBUG: Done: Loading data
+2017-09-22 14:54:30,181 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:30,181 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 14:54:30,181 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:30,212 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:54:30,212 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:54:30,212 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:54:30,212 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:54:30,212 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:30,212 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:30,212 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 14:54:30,212 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 14:54:34,657 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:34,657 DEBUG: Start: Training
+2017-09-22 14:54:35,136 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:35,136 DEBUG: Start: Training
+2017-09-22 14:54:41,935 DEBUG: Done: Training
+2017-09-22 14:54:41,935 DEBUG: Start: Predicting
+2017-09-22 14:54:42,389 DEBUG: Done: Training
+2017-09-22 14:54:42,389 DEBUG: Start: Predicting
+2017-09-22 14:54:45,803 DEBUG: Done: Predicting
+2017-09-22 14:54:45,803 DEBUG: Info: Time for training and predicting: 15.6344819069[s]
+2017-09-22 14:54:45,803 DEBUG: Start: Getting Results
+2017-09-22 14:54:45,834 DEBUG: Done: Getting Results
+2017-09-22 14:54:45,834 INFO: Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.966057441253, with STD : 0.0
+accuracy_score on test : 0.696319018405, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 7097
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.966057441253
+ - Score on test : 0.696319018405
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.966145833333
+ - Score on test : 0.702702702703
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.966145833333
+ - Score on test : 0.702702702703
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0339425587467
+ - Score on test : 0.303680981595
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.966057441253
+ - Score on test : 0.696319018405
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.932127591489
+ - Score on test : 0.393000600631
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.963636363636
+ - Score on test : 0.688235294118
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.968668407311
+ - Score on test : 0.717791411043
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.966057441253
+ - Score on test : 0.696319018405
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0339425587467
+ - Score on test : 0.303680981595
+
+
+ Classification took 0:00:15
+2017-09-22 14:54:45,834 INFO: Done: Result Analysis
+2017-09-22 14:54:46,169 DEBUG: Done: Predicting
+2017-09-22 14:54:46,170 DEBUG: Info: Time for training and predicting: 16.0016198158[s]
+2017-09-22 14:54:46,170 DEBUG: Start: Getting Results
+2017-09-22 14:54:46,197 DEBUG: Done: Getting Results
+2017-09-22 14:54:46,198 INFO: Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.946475195822, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 8934
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.94626474443
+ - Score on test : 0.668674698795
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.94626474443
+ - Score on test : 0.668674698795
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.892977786076
+ - Score on test : 0.325373883668
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.95
+ - Score on test : 0.656804733728
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.942558746736
+ - Score on test : 0.680981595092
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:16
+2017-09-22 14:54:46,198 INFO: Done: Result Analysis
+2017-09-22 14:54:46,350 DEBUG: Start: Loading data
+2017-09-22 14:54:46,350 DEBUG: Start: Loading data
+2017-09-22 14:54:46,366 DEBUG: Done: Loading data
+2017-09-22 14:54:46,366 DEBUG: Done: Loading data
+2017-09-22 14:54:46,366 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:54:46,366 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:54:46,366 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:46,366 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:46,393 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:54:46,393 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:54:46,393 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:54:46,393 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:54:46,393 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:46,393 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:46,394 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 14:54:46,394 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 14:54:47,827 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:47,828 DEBUG: Start: Training
+2017-09-22 14:54:48,105 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:48,105 DEBUG: Start: Training
+2017-09-22 14:54:48,166 DEBUG: Done: Training
+2017-09-22 14:54:48,166 DEBUG: Start: Predicting
+2017-09-22 14:54:48,177 DEBUG: Done: Predicting
+2017-09-22 14:54:48,177 DEBUG: Info: Time for training and predicting: 1.82623195648[s]
+2017-09-22 14:54:48,177 DEBUG: Start: Getting Results
+2017-09-22 14:54:48,207 DEBUG: Done: Getting Results
+2017-09-22 14:54:48,207 INFO: Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.929503916449, with STD : 0.0
+accuracy_score on test : 0.70245398773, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 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 : 0.929503916449
+ - Score on test : 0.70245398773
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.926430517711
+ - Score on test : 0.681967213115
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.926430517711
+ - Score on test : 0.681967213115
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0704960835509
+ - Score on test : 0.29754601227
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.70245398773
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.862021884075
+ - Score on test : 0.408310785419
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.968660968661
+ - Score on test : 0.732394366197
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.88772845953
+ - Score on test : 0.638036809816
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.70245398773
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0704960835509
+ - Score on test : 0.29754601227
+
+
+ Classification took 0:00:01
+2017-09-22 14:54:48,207 INFO: Done: Result Analysis
+2017-09-22 14:54:48,586 DEBUG: Done: Training
+2017-09-22 14:54:48,586 DEBUG: Start: Predicting
+2017-09-22 14:54:48,600 DEBUG: Done: Predicting
+2017-09-22 14:54:48,600 DEBUG: Info: Time for training and predicting: 2.2490208149[s]
+2017-09-22 14:54:48,600 DEBUG: Start: Getting Results
+2017-09-22 14:54:48,628 DEBUG: Done: Getting Results
+2017-09-22 14:54:48,628 INFO: Classification on awaexp database for lss-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.645569620253
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.645569620253
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.313473915907
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:00:02
+2017-09-22 14:54:48,628 INFO: Done: Result Analysis
+2017-09-22 14:54:48,714 DEBUG: Start: Loading data
+2017-09-22 14:54:48,715 DEBUG: Start: Loading data
+2017-09-22 14:54:48,726 DEBUG: Done: Loading data
+2017-09-22 14:54:48,726 DEBUG: Done: Loading data
+2017-09-22 14:54:48,726 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 14:54:48,726 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 14:54:48,726 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:48,726 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:48,752 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:54:48,752 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:54:48,752 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:48,752 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:54:48,752 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 14:54:48,752 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:54:48,753 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:48,753 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 14:54:48,896 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:48,896 DEBUG: Start: Training
+2017-09-22 14:54:48,923 DEBUG: Done: Training
+2017-09-22 14:54:48,923 DEBUG: Start: Predicting
+2017-09-22 14:54:48,941 DEBUG: Done: Predicting
+2017-09-22 14:54:48,941 DEBUG: Info: Time for training and predicting: 0.225959062576[s]
+2017-09-22 14:54:48,941 DEBUG: Start: Getting Results
+2017-09-22 14:54:48,970 DEBUG: Done: Getting Results
+2017-09-22 14:54:48,970 INFO: Classification on awaexp database for lss-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.860313315927, with STD : 0.0
+accuracy_score on test : 0.60736196319, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 2, max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.860313315927
+ - Score on test : 0.60736196319
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.841949778434
+ - Score on test : 0.511450381679
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.841949778434
+ - Score on test : 0.511450381679
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.139686684073
+ - Score on test : 0.39263803681
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.860313315927
+ - Score on test : 0.60736196319
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.740908227603
+ - Score on test : 0.233473459459
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.969387755102
+ - Score on test : 0.676767676768
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.744125326371
+ - Score on test : 0.411042944785
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.860313315927
+ - Score on test : 0.60736196319
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.139686684073
+ - Score on test : 0.39263803681
+
+
+ Classification took 0:00:00
+2017-09-22 14:54:48,970 INFO: Done: Result Analysis
+2017-09-22 14:54:50,572 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:50,572 DEBUG: Start: Training
+2017-09-22 14:54:50,604 DEBUG: Done: Training
+2017-09-22 14:54:50,605 DEBUG: Start: Predicting
+2017-09-22 14:54:55,867 DEBUG: Done: Predicting
+2017-09-22 14:54:55,867 DEBUG: Info: Time for training and predicting: 7.15247702599[s]
+2017-09-22 14:54:55,868 DEBUG: Start: Getting Results
+2017-09-22 14:54:55,895 DEBUG: Done: Getting Results
+2017-09-22 14:54:55,896 INFO: Classification on awaexp database for lss-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.656657963446, with STD : 0.0
+accuracy_score on test : 0.680981595092, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 38
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.656657963446
+ - Score on test : 0.680981595092
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.594761171032
+ - Score on test : 0.631205673759
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.594761171032
+ - Score on test : 0.631205673759
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.343342036554
+ - Score on test : 0.319018404908
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.656657963446
+ - Score on test : 0.680981595092
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.329045098264
+ - Score on test : 0.375918204951
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.725563909774
+ - Score on test : 0.747899159664
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.503916449086
+ - Score on test : 0.546012269939
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.656657963446
+ - Score on test : 0.680981595092
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.343342036554
+ - Score on test : 0.319018404908
+
+
+ Classification took 0:00:07
+2017-09-22 14:54:55,896 INFO: Done: Result Analysis
+2017-09-22 14:54:55,990 DEBUG: Start: Loading data
+2017-09-22 14:54:55,991 DEBUG: Start: Loading data
+2017-09-22 14:54:56,000 DEBUG: Done: Loading data
+2017-09-22 14:54:56,000 DEBUG: Done: Loading data
+2017-09-22 14:54:56,001 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 14:54:56,001 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 14:54:56,001 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:56,001 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:54:56,020 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:54:56,021 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:54:56,021 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:54:56,021 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:56,021 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:54:56,021 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 14:54:56,021 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:54:56,021 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 14:54:56,478 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:56,478 DEBUG: Start: Training
+2017-09-22 14:54:56,579 DEBUG: Done: Training
+2017-09-22 14:54:56,579 DEBUG: Start: Predicting
+2017-09-22 14:54:56,587 DEBUG: Done: Predicting
+2017-09-22 14:54:56,587 DEBUG: Info: Time for training and predicting: 0.596174955368[s]
+2017-09-22 14:54:56,587 DEBUG: Start: Getting Results
+2017-09-22 14:54:56,622 DEBUG: Done: Getting Results
+2017-09-22 14:54:56,622 INFO: Classification on awaexp database for lss-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.930809399478, with STD : 0.0
+accuracy_score on test : 0.751533742331, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.930809399478
+ - Score on test : 0.751533742331
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.926490984743
+ - Score on test : 0.744479495268
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.926490984743
+ - Score on test : 0.744479495268
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0691906005222
+ - Score on test : 0.248466257669
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.930809399478
+ - Score on test : 0.751533742331
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.867628291993
+ - Score on test : 0.503836084816
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.988165680473
+ - Score on test : 0.766233766234
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.872062663185
+ - Score on test : 0.723926380368
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.930809399478
+ - Score on test : 0.751533742331
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0691906005222
+ - Score on test : 0.248466257669
+
+
+ Classification took 0:00:00
+2017-09-22 14:54:56,623 INFO: Done: Result Analysis
+2017-09-22 14:54:58,556 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:54:58,557 DEBUG: Start: Training
+2017-09-22 14:55:02,569 DEBUG: Done: Training
+2017-09-22 14:55:02,569 DEBUG: Start: Predicting
+2017-09-22 14:55:04,416 DEBUG: Done: Predicting
+2017-09-22 14:55:04,417 DEBUG: Info: Time for training and predicting: 8.42555618286[s]
+2017-09-22 14:55:04,417 DEBUG: Start: Getting Results
+2017-09-22 14:55:04,444 DEBUG: Done: Getting Results
+2017-09-22 14:55:04,444 INFO: Classification on awaexp database for lss-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.834355828221, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 8934
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.834355828221
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.835365853659
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.835365853659
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.165644171779
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.834355828221
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.668761999862
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.830303030303
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.840490797546
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.834355828221
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.165644171779
+
+
+ Classification took 0:00:08
+2017-09-22 14:55:04,444 INFO: Done: Result Analysis
+2017-09-22 14:55:04,565 DEBUG: Start: Loading data
+2017-09-22 14:55:04,566 DEBUG: Start: Loading data
+2017-09-22 14:55:04,576 DEBUG: Done: Loading data
+2017-09-22 14:55:04,576 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 14:55:04,576 DEBUG: Done: Loading data
+2017-09-22 14:55:04,576 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 14:55:04,576 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:55:04,577 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:55:04,604 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:55:04,604 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:55:04,604 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:55:04,604 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:55:04,604 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:55:04,604 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:55:04,604 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 14:55:04,604 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 14:55:07,929 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:55:07,929 DEBUG: Start: Training
+2017-09-22 14:55:09,576 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:55:09,576 DEBUG: Start: Training
+2017-09-22 14:55:12,730 DEBUG: Done: Training
+2017-09-22 14:55:12,731 DEBUG: Start: Predicting
+2017-09-22 14:55:15,040 DEBUG: Done: Predicting
+2017-09-22 14:55:15,040 DEBUG: Info: Time for training and predicting: 10.4746248722[s]
+2017-09-22 14:55:15,040 DEBUG: Start: Getting Results
+2017-09-22 14:55:15,070 DEBUG: Done: Getting Results
+2017-09-22 14:55:15,070 INFO: Classification on awaexp database for lss-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.800613496933, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 8934
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.800613496933
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.798761609907
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.798761609907
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.199386503067
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.800613496933
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.60132884975
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.80625
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.791411042945
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.800613496933
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.199386503067
+
+
+ Classification took 0:00:10
+2017-09-22 14:55:15,071 INFO: Done: Result Analysis
+2017-09-22 14:55:16,852 DEBUG: Done: Training
+2017-09-22 14:55:16,852 DEBUG: Start: Predicting
+2017-09-22 14:55:20,363 DEBUG: Done: Predicting
+2017-09-22 14:55:20,364 DEBUG: Info: Time for training and predicting: 15.797577858[s]
+2017-09-22 14:55:20,364 DEBUG: Start: Getting Results
+2017-09-22 14:55:20,390 DEBUG: Done: Getting Results
+2017-09-22 14:55:20,391 INFO: Classification on awaexp database for lss-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.527607361963, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 8934
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.527607361963
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.669527896996
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.669527896996
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.472392638037
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.527607361963
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.107809560896
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.514851485149
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.957055214724
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.527607361963
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.472392638037
+
+
+ Classification took 0:00:15
+2017-09-22 14:55:20,391 INFO: Done: Result Analysis
+2017-09-22 14:55:20,560 INFO: ### Main Programm for Multiview Classification
+2017-09-22 14:55:20,560 INFO: ### Main Programm for Multiview Classification
+2017-09-22 14:55:20,560 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 14:55:20,561 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 14:55:20,563 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 14:55:20,563 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 14:55:20,565 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 14:55:20,565 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 14:55:20,565 INFO: Done: Read Database Files
+2017-09-22 14:55:20,565 INFO: Done: Read Database Files
+2017-09-22 14:55:20,566 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 14:55:20,566 INFO: Start: Determine validation split for ratio 0.7
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145412Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145412Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..cea8682b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145412Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.628834355828, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Adaboost with num_esimators : 6, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None,
+ min_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.628834355828
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.618296529968
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.618296529968
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.371165644172
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.628834355828
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.258062384906
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.636363636364
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.601226993865
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.628834355828
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.371165644172
+
+
+ Classification took 0:00:04
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145412Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145412Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..ad4e7d8f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145412Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.677914110429, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.677914110429
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.672897196262
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.672897196262
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.322085889571
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.677914110429
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.355995746702
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.683544303797
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.677914110429
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.322085889571
+
+
+ Classification took 0:00:04
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145412Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145412Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..22b59d7c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145412Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.791122715405, with STD : 0.0
+accuracy_score on test : 0.687116564417, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 1, max_depth : 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 : 0.791122715405
+ - Score on test : 0.687116564417
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.780821917808
+ - Score on test : 0.677215189873
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.780821917808
+ - Score on test : 0.677215189873
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.208877284595
+ - Score on test : 0.312883435583
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.791122715405
+ - Score on test : 0.687116564417
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.584834675032
+ - Score on test : 0.374939389614
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.821325648415
+ - Score on test : 0.699346405229
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.744125326371
+ - Score on test : 0.656441717791
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.791122715405
+ - Score on test : 0.687116564417
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.208877284595
+ - Score on test : 0.312883435583
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145418Results-KNN--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145418Results-KNN--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..2691e9a2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145418Results-KNN--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.610429447853, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 1.0
+ - Score on test : 0.610429447853
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.609230769231
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.609230769231
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.389570552147
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.610429447853
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.22086305215
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.611111111111
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.60736196319
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.610429447853
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.389570552147
+
+
+ Classification took 0:00:05
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145418Results-SGD--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145418Results-SGD--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..fda8b618
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145418Results-SGD--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.569190600522, with STD : 0.0
+accuracy_score on test : 0.552147239264, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.569190600522
+ - Score on test : 0.552147239264
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.663265306122
+ - Score on test : 0.657276995305
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.663265306122
+ - Score on test : 0.657276995305
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.430809399478
+ - Score on test : 0.447852760736
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.569190600522
+ - Score on test : 0.552147239264
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.166857353772
+ - Score on test : 0.132068964403
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.544388609715
+ - Score on test : 0.532319391635
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.848563968668
+ - Score on test : 0.858895705521
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.569190600522
+ - Score on test : 0.552147239264
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.430809399478
+ - Score on test : 0.447852760736
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145430Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145430Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..163070a3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145430Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.601226993865, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 8934
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.601226993865
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.606060606061
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.606060606061
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.398773006135
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.601226993865
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.202514974737
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.59880239521
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.613496932515
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.601226993865
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.398773006135
+
+
+ Classification took 0:00:11
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145445Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145445Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..2ddea1ed
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145445Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.966057441253, with STD : 0.0
+accuracy_score on test : 0.696319018405, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 7097
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.966057441253
+ - Score on test : 0.696319018405
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.966145833333
+ - Score on test : 0.702702702703
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.966145833333
+ - Score on test : 0.702702702703
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0339425587467
+ - Score on test : 0.303680981595
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.966057441253
+ - Score on test : 0.696319018405
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.932127591489
+ - Score on test : 0.393000600631
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.963636363636
+ - Score on test : 0.688235294118
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.968668407311
+ - Score on test : 0.717791411043
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.966057441253
+ - Score on test : 0.696319018405
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0339425587467
+ - Score on test : 0.303680981595
+
+
+ Classification took 0:00:15
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145446Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145446Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..2bc72322
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145446Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.946475195822, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 8934
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.94626474443
+ - Score on test : 0.668674698795
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.94626474443
+ - Score on test : 0.668674698795
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.892977786076
+ - Score on test : 0.325373883668
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.95
+ - Score on test : 0.656804733728
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.942558746736
+ - Score on test : 0.680981595092
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:16
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145448Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145448Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..e1367106
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145448Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for lss-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.645569620253
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.645569620253
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.313473915907
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145448Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145448Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..85e7156f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145448Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.929503916449, with STD : 0.0
+accuracy_score on test : 0.70245398773, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 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 : 0.929503916449
+ - Score on test : 0.70245398773
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.926430517711
+ - Score on test : 0.681967213115
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.926430517711
+ - Score on test : 0.681967213115
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0704960835509
+ - Score on test : 0.29754601227
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.70245398773
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.862021884075
+ - Score on test : 0.408310785419
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.968660968661
+ - Score on test : 0.732394366197
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.88772845953
+ - Score on test : 0.638036809816
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.929503916449
+ - Score on test : 0.70245398773
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0704960835509
+ - Score on test : 0.29754601227
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145448Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145448Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..2597674f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145448Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.860313315927, with STD : 0.0
+accuracy_score on test : 0.60736196319, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 2, max_depth : 25
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.860313315927
+ - Score on test : 0.60736196319
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.841949778434
+ - Score on test : 0.511450381679
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.841949778434
+ - Score on test : 0.511450381679
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.139686684073
+ - Score on test : 0.39263803681
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.860313315927
+ - Score on test : 0.60736196319
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.740908227603
+ - Score on test : 0.233473459459
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.969387755102
+ - Score on test : 0.676767676768
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.744125326371
+ - Score on test : 0.411042944785
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.860313315927
+ - Score on test : 0.60736196319
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.139686684073
+ - Score on test : 0.39263803681
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145455Results-KNN--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145455Results-KNN--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..8036455a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145455Results-KNN--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.656657963446, with STD : 0.0
+accuracy_score on test : 0.680981595092, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 38
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.656657963446
+ - Score on test : 0.680981595092
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.594761171032
+ - Score on test : 0.631205673759
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.594761171032
+ - Score on test : 0.631205673759
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.343342036554
+ - Score on test : 0.319018404908
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.656657963446
+ - Score on test : 0.680981595092
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.329045098264
+ - Score on test : 0.375918204951
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.725563909774
+ - Score on test : 0.747899159664
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.503916449086
+ - Score on test : 0.546012269939
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.656657963446
+ - Score on test : 0.680981595092
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.343342036554
+ - Score on test : 0.319018404908
+
+
+ Classification took 0:00:07
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145456Results-SGD--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145456Results-SGD--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..d322756b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145456Results-SGD--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.930809399478, with STD : 0.0
+accuracy_score on test : 0.751533742331, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.930809399478
+ - Score on test : 0.751533742331
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.926490984743
+ - Score on test : 0.744479495268
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.926490984743
+ - Score on test : 0.744479495268
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0691906005222
+ - Score on test : 0.248466257669
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.930809399478
+ - Score on test : 0.751533742331
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.867628291993
+ - Score on test : 0.503836084816
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.988165680473
+ - Score on test : 0.766233766234
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.872062663185
+ - Score on test : 0.723926380368
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.930809399478
+ - Score on test : 0.751533742331
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0691906005222
+ - Score on test : 0.248466257669
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145504Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145504Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..b981a309
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145504Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.834355828221, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 8934
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.834355828221
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.835365853659
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.835365853659
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.165644171779
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.834355828221
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.668761999862
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.830303030303
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.840490797546
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.834355828221
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.165644171779
+
+
+ Classification took 0:00:08
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145515Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145515Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..252f0e94
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145515Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.800613496933, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 8934
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.800613496933
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.798761609907
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.798761609907
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.199386503067
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.800613496933
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.60132884975
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.80625
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.791411042945
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.800613496933
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.199386503067
+
+
+ Classification took 0:00:10
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145520Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145520Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..174aa660
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145520Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.527607361963, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 8934
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.527607361963
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.669527896996
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.669527896996
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.472392638037
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.527607361963
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.107809560896
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.514851485149
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.957055214724
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.527607361963
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.472392638037
+
+
+ Classification took 0:00:15
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145752-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-145752-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..af6975a0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145752-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,1110 @@
+2017-09-22 14:57:58,097 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 14:57:58,100 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 14:57:58,167 DEBUG: Start: Loading data
+2017-09-22 14:57:58,167 DEBUG: Start: Loading data
+2017-09-22 14:57:58,178 DEBUG: Done: Loading data
+2017-09-22 14:57:58,178 DEBUG: Done: Loading data
+2017-09-22 14:57:58,178 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:57:58,178 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:57:58,179 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:57:58,179 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:57:58,201 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:57:58,201 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:57:58,201 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:57:58,201 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:57:58,201 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 14:57:58,201 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:57:58,201 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:57:58,201 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 14:57:59,513 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:57:59,514 DEBUG: Start: Training
+2017-09-22 14:57:59,774 DEBUG: Done: Training
+2017-09-22 14:57:59,774 DEBUG: Start: Predicting
+2017-09-22 14:57:59,787 DEBUG: Done: Predicting
+2017-09-22 14:57:59,787 DEBUG: Info: Time for training and predicting: 1.61906695366[s]
+2017-09-22 14:57:59,787 DEBUG: Start: Getting Results
+2017-09-22 14:57:59,816 DEBUG: Done: Getting Results
+2017-09-22 14:57:59,816 INFO: Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.723237597911, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.723237597911
+ - Score on test : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.651315789474
+ - Score on test : 0.548387096774
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.651315789474
+ - Score on test : 0.548387096774
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.276762402089
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.723237597911
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.490124281647
+ - Score on test : 0.356329839274
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.88
+ - Score on test : 0.8
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.516971279373
+ - Score on test : 0.41717791411
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.723237597911
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.276762402089
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:00:01
+2017-09-22 14:57:59,816 INFO: Done: Result Analysis
+2017-09-22 14:58:00,870 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:00,870 DEBUG: Start: Training
+2017-09-22 14:58:02,007 DEBUG: Done: Training
+2017-09-22 14:58:02,007 DEBUG: Start: Predicting
+2017-09-22 14:58:02,026 DEBUG: Done: Predicting
+2017-09-22 14:58:02,026 DEBUG: Info: Time for training and predicting: 3.8586730957[s]
+2017-09-22 14:58:02,026 DEBUG: Start: Getting Results
+2017-09-22 14:58:02,057 DEBUG: Done: Getting Results
+2017-09-22 14:58:02,057 INFO: Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.613496932515, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.613496932515
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.59872611465
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.59872611465
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.386503067485
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.613496932515
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.227611513211
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.622516556291
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.576687116564
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.613496932515
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.386503067485
+
+
+ Classification took 0:00:03
+2017-09-22 14:58:02,058 INFO: Done: Result Analysis
+2017-09-22 14:58:02,130 DEBUG: Start: Loading data
+2017-09-22 14:58:02,130 DEBUG: Start: Loading data
+2017-09-22 14:58:02,143 DEBUG: Done: Loading data
+2017-09-22 14:58:02,143 DEBUG: Done: Loading data
+2017-09-22 14:58:02,143 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 14:58:02,143 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 14:58:02,143 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:02,143 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:02,166 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:58:02,166 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:58:02,166 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:02,166 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 14:58:02,168 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:58:02,168 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:58:02,168 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:02,169 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 14:58:03,365 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:03,365 DEBUG: Start: Training
+2017-09-22 14:58:03,688 DEBUG: Done: Training
+2017-09-22 14:58:03,688 DEBUG: Start: Predicting
+2017-09-22 14:58:03,760 DEBUG: Done: Predicting
+2017-09-22 14:58:03,760 DEBUG: Info: Time for training and predicting: 1.62965607643[s]
+2017-09-22 14:58:03,760 DEBUG: Start: Getting Results
+2017-09-22 14:58:03,789 DEBUG: Done: Getting Results
+2017-09-22 14:58:03,789 INFO: Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.993472584856, with STD : 0.0
+accuracy_score on test : 0.733128834356, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 23, max_depth : 10
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.733128834356
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.993446920052
+ - Score on test : 0.722044728435
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.993446920052
+ - Score on test : 0.722044728435
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.266871165644
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.733128834356
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.986975447768
+ - Score on test : 0.467747665721
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997368421053
+ - Score on test : 0.753333333333
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.98955613577
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.733128834356
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.266871165644
+
+
+ Classification took 0:00:01
+2017-09-22 14:58:03,789 INFO: Done: Result Analysis
+2017-09-22 14:58:04,884 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:04,885 DEBUG: Start: Training
+2017-09-22 14:58:04,931 DEBUG: Done: Training
+2017-09-22 14:58:04,931 DEBUG: Start: Predicting
+2017-09-22 14:58:11,932 DEBUG: Done: Predicting
+2017-09-22 14:58:11,932 DEBUG: Info: Time for training and predicting: 9.80107402802[s]
+2017-09-22 14:58:11,932 DEBUG: Start: Getting Results
+2017-09-22 14:58:11,959 DEBUG: Done: Getting Results
+2017-09-22 14:58:11,959 INFO: Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.718015665796, with STD : 0.0
+accuracy_score on test : 0.644171779141, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.718015665796
+ - Score on test : 0.644171779141
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.698324022346
+ - Score on test : 0.639751552795
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.698324022346
+ - Score on test : 0.639751552795
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.281984334204
+ - Score on test : 0.355828220859
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.718015665796
+ - Score on test : 0.644171779141
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.439795120132
+ - Score on test : 0.288430418565
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.750750750751
+ - Score on test : 0.647798742138
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.65274151436
+ - Score on test : 0.631901840491
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.718015665796
+ - Score on test : 0.644171779141
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.281984334204
+ - Score on test : 0.355828220859
+
+
+ Classification took 0:00:09
+2017-09-22 14:58:11,959 INFO: Done: Result Analysis
+2017-09-22 14:58:12,112 DEBUG: Start: Loading data
+2017-09-22 14:58:12,112 DEBUG: Start: Loading data
+2017-09-22 14:58:12,126 DEBUG: Done: Loading data
+2017-09-22 14:58:12,126 DEBUG: Done: Loading data
+2017-09-22 14:58:12,126 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 14:58:12,126 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 14:58:12,126 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:12,126 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:12,156 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:58:12,156 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:58:12,156 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:58:12,156 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:58:12,156 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:12,156 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:12,156 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 14:58:12,156 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 14:58:12,519 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:12,519 DEBUG: Start: Training
+2017-09-22 14:58:12,572 DEBUG: Done: Training
+2017-09-22 14:58:12,572 DEBUG: Start: Predicting
+2017-09-22 14:58:12,582 DEBUG: Done: Predicting
+2017-09-22 14:58:12,582 DEBUG: Info: Time for training and predicting: 0.469763994217[s]
+2017-09-22 14:58:12,582 DEBUG: Start: Getting Results
+2017-09-22 14:58:12,613 DEBUG: Done: Getting Results
+2017-09-22 14:58:12,613 INFO: Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.678851174935, with STD : 0.0
+accuracy_score on test : 0.668711656442, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.678851174935
+ - Score on test : 0.668711656442
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.681347150259
+ - Score on test : 0.666666666667
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.681347150259
+ - Score on test : 0.666666666667
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.321148825065
+ - Score on test : 0.331288343558
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.678851174935
+ - Score on test : 0.668711656442
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.3577462511
+ - Score on test : 0.337448715527
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.676092544987
+ - Score on test : 0.670807453416
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.686684073107
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.678851174935
+ - Score on test : 0.668711656442
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.321148825065
+ - Score on test : 0.331288343558
+
+
+ Classification took 0:00:00
+2017-09-22 14:58:12,613 INFO: Done: Result Analysis
+2017-09-22 14:58:15,661 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:15,661 DEBUG: Start: Training
+2017-09-22 14:58:21,094 DEBUG: Done: Training
+2017-09-22 14:58:21,094 DEBUG: Start: Predicting
+2017-09-22 14:58:23,954 DEBUG: Done: Predicting
+2017-09-22 14:58:23,954 DEBUG: Info: Time for training and predicting: 11.8417050838[s]
+2017-09-22 14:58:23,955 DEBUG: Start: Getting Results
+2017-09-22 14:58:23,981 DEBUG: Done: Getting Results
+2017-09-22 14:58:23,981 INFO: Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.625766871166, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 285
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.625766871166
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.636904761905
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.636904761905
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.374233128834
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.625766871166
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.252008442199
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.618497109827
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.656441717791
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.625766871166
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.374233128834
+
+
+ Classification took 0:00:11
+2017-09-22 14:58:23,981 INFO: Done: Result Analysis
+2017-09-22 14:58:24,093 DEBUG: Start: Loading data
+2017-09-22 14:58:24,094 DEBUG: Start: Loading data
+2017-09-22 14:58:24,106 DEBUG: Done: Loading data
+2017-09-22 14:58:24,107 DEBUG: Done: Loading data
+2017-09-22 14:58:24,107 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 14:58:24,107 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 14:58:24,107 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:24,107 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:24,137 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:58:24,137 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 14:58:24,137 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:58:24,137 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 14:58:24,137 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:24,137 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:24,137 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 14:58:24,137 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 14:58:29,093 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:29,094 DEBUG: Start: Training
+2017-09-22 14:58:29,965 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:29,965 DEBUG: Start: Training
+2017-09-22 14:58:37,756 DEBUG: Done: Training
+2017-09-22 14:58:37,757 DEBUG: Start: Predicting
+2017-09-22 14:58:39,560 DEBUG: Done: Training
+2017-09-22 14:58:39,560 DEBUG: Start: Predicting
+2017-09-22 14:58:42,406 DEBUG: Done: Predicting
+2017-09-22 14:58:42,407 DEBUG: Info: Time for training and predicting: 18.3120458126[s]
+2017-09-22 14:58:42,407 DEBUG: Start: Getting Results
+2017-09-22 14:58:42,436 DEBUG: Done: Getting Results
+2017-09-22 14:58:42,436 INFO: Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.801566579634, with STD : 0.0
+accuracy_score on test : 0.674846625767, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 285
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.801566579634
+ - Score on test : 0.674846625767
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.80310880829
+ - Score on test : 0.698863636364
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.80310880829
+ - Score on test : 0.698863636364
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.198433420366
+ - Score on test : 0.325153374233
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.801566579634
+ - Score on test : 0.674846625767
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.603207182512
+ - Score on test : 0.35422863859
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.796915167095
+ - Score on test : 0.650793650794
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.809399477807
+ - Score on test : 0.754601226994
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.801566579634
+ - Score on test : 0.674846625767
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.198433420366
+ - Score on test : 0.325153374233
+
+
+ Classification took 0:00:18
+2017-09-22 14:58:42,436 INFO: Done: Result Analysis
+2017-09-22 14:58:44,586 DEBUG: Done: Predicting
+2017-09-22 14:58:44,587 DEBUG: Info: Time for training and predicting: 20.492634058[s]
+2017-09-22 14:58:44,587 DEBUG: Start: Getting Results
+2017-09-22 14:58:44,614 DEBUG: Done: Getting Results
+2017-09-22 14:58:44,614 INFO: Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.947780678851, with STD : 0.0
+accuracy_score on test : 0.570552147239, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 285
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.947780678851
+ - Score on test : 0.570552147239
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.945504087193
+ - Score on test : 0.406779661017
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.945504087193
+ - Score on test : 0.406779661017
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0522193211488
+ - Score on test : 0.429447852761
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.947780678851
+ - Score on test : 0.570552147239
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.898703666376
+ - Score on test : 0.16924121923
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.988603988604
+ - Score on test : 0.657534246575
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.906005221932
+ - Score on test : 0.294478527607
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.947780678851
+ - Score on test : 0.570552147239
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0522193211488
+ - Score on test : 0.429447852761
+
+
+ Classification took 0:00:20
+2017-09-22 14:58:44,614 INFO: Done: Result Analysis
+2017-09-22 14:58:44,702 DEBUG: Start: Loading data
+2017-09-22 14:58:44,702 DEBUG: Start: Loading data
+2017-09-22 14:58:44,713 DEBUG: Done: Loading data
+2017-09-22 14:58:44,713 DEBUG: Done: Loading data
+2017-09-22 14:58:44,713 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 14:58:44,713 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 14:58:44,713 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:44,713 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:44,740 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:58:44,740 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:58:44,740 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:58:44,740 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:58:44,740 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:44,740 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:44,741 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 14:58:44,741 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 14:58:45,493 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:45,493 DEBUG: Start: Training
+2017-09-22 14:58:45,601 DEBUG: Done: Training
+2017-09-22 14:58:45,601 DEBUG: Start: Predicting
+2017-09-22 14:58:45,612 DEBUG: Done: Predicting
+2017-09-22 14:58:45,612 DEBUG: Info: Time for training and predicting: 0.909150123596[s]
+2017-09-22 14:58:45,612 DEBUG: Start: Getting Results
+2017-09-22 14:58:45,641 DEBUG: Done: Getting Results
+2017-09-22 14:58:45,641 INFO: Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.732375979112, with STD : 0.0
+accuracy_score on test : 0.708588957055, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.732375979112
+ - Score on test : 0.708588957055
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.702467343977
+ - Score on test : 0.675767918089
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.702467343977
+ - Score on test : 0.675767918089
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.267624020888
+ - Score on test : 0.291411042945
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.732375979112
+ - Score on test : 0.708588957055
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.47443899058
+ - Score on test : 0.425999609378
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.790849673203
+ - Score on test : 0.761538461538
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.631853785901
+ - Score on test : 0.60736196319
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.732375979112
+ - Score on test : 0.708588957055
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.267624020888
+ - Score on test : 0.291411042945
+
+
+ Classification took 0:00:00
+2017-09-22 14:58:45,642 INFO: Done: Result Analysis
+2017-09-22 14:58:46,048 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:46,048 DEBUG: Start: Training
+2017-09-22 14:58:46,492 DEBUG: Done: Training
+2017-09-22 14:58:46,492 DEBUG: Start: Predicting
+2017-09-22 14:58:46,505 DEBUG: Done: Predicting
+2017-09-22 14:58:46,505 DEBUG: Info: Time for training and predicting: 1.80226302147[s]
+2017-09-22 14:58:46,505 DEBUG: Start: Getting Results
+2017-09-22 14:58:46,532 DEBUG: Done: Getting Results
+2017-09-22 14:58:46,532 INFO: Classification on awaexp database for lss-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.677914110429, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.677914110429
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.672897196262
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.672897196262
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.322085889571
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.677914110429
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.355995746702
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.683544303797
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.677914110429
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.322085889571
+
+
+ Classification took 0:00:01
+2017-09-22 14:58:46,532 INFO: Done: Result Analysis
+2017-09-22 14:58:46,663 DEBUG: Start: Loading data
+2017-09-22 14:58:46,664 DEBUG: Start: Loading data
+2017-09-22 14:58:46,672 DEBUG: Done: Loading data
+2017-09-22 14:58:46,672 DEBUG: Done: Loading data
+2017-09-22 14:58:46,672 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 14:58:46,672 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 14:58:46,672 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:46,672 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:46,690 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:58:46,690 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:58:46,690 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:46,690 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:58:46,690 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 14:58:46,690 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:58:46,690 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:46,690 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 14:58:47,571 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:47,571 DEBUG: Start: Training
+2017-09-22 14:58:47,927 DEBUG: Done: Training
+2017-09-22 14:58:47,927 DEBUG: Start: Predicting
+2017-09-22 14:58:48,020 DEBUG: Done: Predicting
+2017-09-22 14:58:48,021 DEBUG: Info: Time for training and predicting: 1.35673904419[s]
+2017-09-22 14:58:48,021 DEBUG: Start: Getting Results
+2017-09-22 14:58:48,063 DEBUG: Done: Getting Results
+2017-09-22 14:58:48,063 INFO: Classification on awaexp database for lss-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.992167101828, with STD : 0.0
+accuracy_score on test : 0.69018404908, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 23, max_depth : 10
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.69018404908
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.992125984252
+ - Score on test : 0.694864048338
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.992125984252
+ - Score on test : 0.694864048338
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00783289817232
+ - Score on test : 0.30981595092
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.69018404908
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.984387890829
+ - Score on test : 0.380547177509
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997361477573
+ - Score on test : 0.684523809524
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.986945169713
+ - Score on test : 0.705521472393
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.69018404908
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00783289817232
+ - Score on test : 0.30981595092
+
+
+ Classification took 0:00:01
+2017-09-22 14:58:48,063 INFO: Done: Result Analysis
+2017-09-22 14:58:48,760 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:48,760 DEBUG: Start: Training
+2017-09-22 14:58:48,791 DEBUG: Done: Training
+2017-09-22 14:58:48,791 DEBUG: Start: Predicting
+2017-09-22 14:58:53,891 DEBUG: Done: Predicting
+2017-09-22 14:58:53,891 DEBUG: Info: Time for training and predicting: 7.2273850441[s]
+2017-09-22 14:58:53,891 DEBUG: Start: Getting Results
+2017-09-22 14:58:53,920 DEBUG: Done: Getting Results
+2017-09-22 14:58:53,920 INFO: Classification on awaexp database for lss-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.702349869452, with STD : 0.0
+accuracy_score on test : 0.693251533742, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.702349869452
+ - Score on test : 0.693251533742
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.64375
+ - Score on test : 0.63503649635
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.64375
+ - Score on test : 0.63503649635
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.297650130548
+ - Score on test : 0.306748466258
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.702349869452
+ - Score on test : 0.693251533742
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.428554683149
+ - Score on test : 0.407811839631
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.801556420233
+ - Score on test : 0.783783783784
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.537859007833
+ - Score on test : 0.533742331288
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.702349869452
+ - Score on test : 0.693251533742
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.297650130548
+ - Score on test : 0.306748466258
+
+
+ Classification took 0:00:07
+2017-09-22 14:58:53,920 INFO: Done: Result Analysis
+2017-09-22 14:58:54,044 DEBUG: Start: Loading data
+2017-09-22 14:58:54,044 DEBUG: Start: Loading data
+2017-09-22 14:58:54,055 DEBUG: Done: Loading data
+2017-09-22 14:58:54,055 DEBUG: Done: Loading data
+2017-09-22 14:58:54,055 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 14:58:54,055 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 14:58:54,055 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:54,055 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:58:54,082 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:58:54,082 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:58:54,082 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:58:54,082 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:54,082 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:58:54,082 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 14:58:54,083 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:58:54,083 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 14:58:54,380 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:54,380 DEBUG: Start: Training
+2017-09-22 14:58:54,404 DEBUG: Done: Training
+2017-09-22 14:58:54,404 DEBUG: Start: Predicting
+2017-09-22 14:58:54,411 DEBUG: Done: Predicting
+2017-09-22 14:58:54,411 DEBUG: Info: Time for training and predicting: 0.366451025009[s]
+2017-09-22 14:58:54,411 DEBUG: Start: Getting Results
+2017-09-22 14:58:54,441 DEBUG: Done: Getting Results
+2017-09-22 14:58:54,442 INFO: Classification on awaexp database for lss-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.900783289817, with STD : 0.0
+accuracy_score on test : 0.766871165644, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.900783289817
+ - Score on test : 0.766871165644
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.908872901679
+ - Score on test : 0.798941798942
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.908872901679
+ - Score on test : 0.798941798942
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0992167101828
+ - Score on test : 0.233128834356
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.900783289817
+ - Score on test : 0.766871165644
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.814507012356
+ - Score on test : 0.563168730919
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.840354767184
+ - Score on test : 0.702325581395
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.98955613577
+ - Score on test : 0.926380368098
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.900783289817
+ - Score on test : 0.766871165644
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0992167101828
+ - Score on test : 0.233128834356
+
+
+ Classification took 0:00:00
+2017-09-22 14:58:54,442 INFO: Done: Result Analysis
+2017-09-22 14:58:56,527 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:58:56,528 DEBUG: Start: Training
+2017-09-22 14:59:00,184 DEBUG: Done: Training
+2017-09-22 14:59:00,184 DEBUG: Start: Predicting
+2017-09-22 14:59:01,908 DEBUG: Done: Predicting
+2017-09-22 14:59:01,909 DEBUG: Info: Time for training and predicting: 7.86400103569[s]
+2017-09-22 14:59:01,909 DEBUG: Start: Getting Results
+2017-09-22 14:59:01,936 DEBUG: Done: Getting Results
+2017-09-22 14:59:01,936 INFO: Classification on awaexp database for lss-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.779141104294, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 285
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.777777777778
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.777777777778
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.220858895706
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.558324238417
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.782608695652
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.773006134969
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.220858895706
+
+
+ Classification took 0:00:07
+2017-09-22 14:59:01,936 INFO: Done: Result Analysis
+2017-09-22 14:59:02,016 DEBUG: Start: Loading data
+2017-09-22 14:59:02,016 DEBUG: Start: Loading data
+2017-09-22 14:59:02,028 DEBUG: Done: Loading data
+2017-09-22 14:59:02,028 DEBUG: Done: Loading data
+2017-09-22 14:59:02,028 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 14:59:02,028 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 14:59:02,028 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:59:02,028 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 14:59:02,055 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:59:02,055 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 14:59:02,055 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:59:02,055 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 14:59:02,055 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:59:02,055 DEBUG: Done: Determine Train/Test split
+2017-09-22 14:59:02,056 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 14:59:02,056 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 14:59:04,864 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:59:04,864 DEBUG: Start: Training
+2017-09-22 14:59:08,851 DEBUG: Done: RandomSearch best settings
+2017-09-22 14:59:08,851 DEBUG: Start: Training
+2017-09-22 14:59:09,411 DEBUG: Done: Training
+2017-09-22 14:59:09,411 DEBUG: Start: Predicting
+2017-09-22 14:59:11,380 DEBUG: Done: Predicting
+2017-09-22 14:59:11,380 DEBUG: Info: Time for training and predicting: 9.36436104774[s]
+2017-09-22 14:59:11,380 DEBUG: Start: Getting Results
+2017-09-22 14:59:11,425 DEBUG: Done: Getting Results
+2017-09-22 14:59:11,425 INFO: Classification on awaexp database for lss-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.509138381201, with STD : 0.0
+accuracy_score on test : 0.441717791411, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 4430
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.509138381201
+ - Score on test : 0.441717791411
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.505263157895
+ - Score on test : 0.438271604938
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.505263157895
+ - Score on test : 0.438271604938
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.490861618799
+ - Score on test : 0.558282208589
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.509138381201
+ - Score on test : 0.441717791411
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0182790055307
+ - Score on test : -0.116573192637
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.509283819629
+ - Score on test : 0.44099378882
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.501305483029
+ - Score on test : 0.435582822086
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.509138381201
+ - Score on test : 0.441717791411
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.490861618799
+ - Score on test : 0.558282208589
+
+
+ Classification took 0:00:09
+2017-09-22 14:59:11,426 INFO: Done: Result Analysis
+2017-09-22 14:59:16,144 DEBUG: Done: Training
+2017-09-22 14:59:16,145 DEBUG: Start: Predicting
+2017-09-22 14:59:19,615 DEBUG: Done: Predicting
+2017-09-22 14:59:19,616 DEBUG: Info: Time for training and predicting: 17.5995430946[s]
+2017-09-22 14:59:19,616 DEBUG: Start: Getting Results
+2017-09-22 14:59:19,642 DEBUG: Done: Getting Results
+2017-09-22 14:59:19,642 INFO: Classification on awaexp database for lss-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.521472392638, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 285
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.665236051502
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.665236051502
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.478527607362
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.0838518806968
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511551155116
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.950920245399
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.478527607362
+
+
+ Classification took 0:00:17
+2017-09-22 14:59:19,642 INFO: Done: Result Analysis
+2017-09-22 14:59:19,730 INFO: ### Main Programm for Multiview Classification
+2017-09-22 14:59:19,730 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 14:59:19,731 INFO: ### Main Programm for Multiview Classification
+2017-09-22 14:59:19,731 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 14:59:19,731 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 14:59:19,732 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 14:59:19,732 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 14:59:19,732 INFO: Done: Read Database Files
+2017-09-22 14:59:19,732 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 14:59:19,733 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 14:59:19,733 INFO: Done: Read Database Files
+2017-09-22 14:59:19,733 INFO: Start: Determine validation split for ratio 0.7
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145759Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145759Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..30877520
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145759Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.723237597911, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.723237597911
+ - Score on test : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.651315789474
+ - Score on test : 0.548387096774
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.651315789474
+ - Score on test : 0.548387096774
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.276762402089
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.723237597911
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.490124281647
+ - Score on test : 0.356329839274
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.88
+ - Score on test : 0.8
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.516971279373
+ - Score on test : 0.41717791411
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.723237597911
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.276762402089
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145802Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145802Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..bec8cc08
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145802Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.613496932515, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.613496932515
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.59872611465
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.59872611465
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.386503067485
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.613496932515
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.227611513211
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.622516556291
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.576687116564
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.613496932515
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.386503067485
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145803Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145803Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..df467afc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145803Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.993472584856, with STD : 0.0
+accuracy_score on test : 0.733128834356, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 23, max_depth : 10
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.733128834356
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.993446920052
+ - Score on test : 0.722044728435
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.993446920052
+ - Score on test : 0.722044728435
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.266871165644
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.733128834356
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.986975447768
+ - Score on test : 0.467747665721
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997368421053
+ - Score on test : 0.753333333333
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.98955613577
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.733128834356
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.266871165644
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145811Results-KNN--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145811Results-KNN--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..d772dc2b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145811Results-KNN--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.718015665796, with STD : 0.0
+accuracy_score on test : 0.644171779141, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.718015665796
+ - Score on test : 0.644171779141
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.698324022346
+ - Score on test : 0.639751552795
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.698324022346
+ - Score on test : 0.639751552795
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.281984334204
+ - Score on test : 0.355828220859
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.718015665796
+ - Score on test : 0.644171779141
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.439795120132
+ - Score on test : 0.288430418565
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.750750750751
+ - Score on test : 0.647798742138
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.65274151436
+ - Score on test : 0.631901840491
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.718015665796
+ - Score on test : 0.644171779141
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.281984334204
+ - Score on test : 0.355828220859
+
+
+ Classification took 0:00:09
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145812Results-SGD--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145812Results-SGD--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..c0614707
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145812Results-SGD--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.678851174935, with STD : 0.0
+accuracy_score on test : 0.668711656442, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.678851174935
+ - Score on test : 0.668711656442
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.681347150259
+ - Score on test : 0.666666666667
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.681347150259
+ - Score on test : 0.666666666667
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.321148825065
+ - Score on test : 0.331288343558
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.678851174935
+ - Score on test : 0.668711656442
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.3577462511
+ - Score on test : 0.337448715527
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.676092544987
+ - Score on test : 0.670807453416
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.686684073107
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.678851174935
+ - Score on test : 0.668711656442
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.321148825065
+ - Score on test : 0.331288343558
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145823Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145823Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..2f337c0b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145823Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.625766871166, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 285
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.625766871166
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.636904761905
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.636904761905
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.374233128834
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.625766871166
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.252008442199
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.618497109827
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.656441717791
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.625766871166
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.374233128834
+
+
+ Classification took 0:00:11
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145842Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145842Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..238dc869
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145842Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.801566579634, with STD : 0.0
+accuracy_score on test : 0.674846625767, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 285
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.801566579634
+ - Score on test : 0.674846625767
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.80310880829
+ - Score on test : 0.698863636364
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.80310880829
+ - Score on test : 0.698863636364
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.198433420366
+ - Score on test : 0.325153374233
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.801566579634
+ - Score on test : 0.674846625767
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.603207182512
+ - Score on test : 0.35422863859
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.796915167095
+ - Score on test : 0.650793650794
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.809399477807
+ - Score on test : 0.754601226994
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.801566579634
+ - Score on test : 0.674846625767
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.198433420366
+ - Score on test : 0.325153374233
+
+
+ Classification took 0:00:18
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145844Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145844Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..3143bf32
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145844Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.947780678851, with STD : 0.0
+accuracy_score on test : 0.570552147239, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 285
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.947780678851
+ - Score on test : 0.570552147239
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.945504087193
+ - Score on test : 0.406779661017
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.945504087193
+ - Score on test : 0.406779661017
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0522193211488
+ - Score on test : 0.429447852761
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.947780678851
+ - Score on test : 0.570552147239
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.898703666376
+ - Score on test : 0.16924121923
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.988603988604
+ - Score on test : 0.657534246575
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.906005221932
+ - Score on test : 0.294478527607
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.947780678851
+ - Score on test : 0.570552147239
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0522193211488
+ - Score on test : 0.429447852761
+
+
+ Classification took 0:00:20
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145845Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145845Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..37307630
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145845Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.732375979112, with STD : 0.0
+accuracy_score on test : 0.708588957055, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.732375979112
+ - Score on test : 0.708588957055
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.702467343977
+ - Score on test : 0.675767918089
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.702467343977
+ - Score on test : 0.675767918089
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.267624020888
+ - Score on test : 0.291411042945
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.732375979112
+ - Score on test : 0.708588957055
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.47443899058
+ - Score on test : 0.425999609378
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.790849673203
+ - Score on test : 0.761538461538
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.631853785901
+ - Score on test : 0.60736196319
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.732375979112
+ - Score on test : 0.708588957055
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.267624020888
+ - Score on test : 0.291411042945
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145846Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145846Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..b0d3c7d2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145846Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for lss-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.677914110429, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.677914110429
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.672897196262
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.672897196262
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.322085889571
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.677914110429
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.355995746702
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.683544303797
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.677914110429
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.322085889571
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145848Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145848Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..fdaf6a03
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145848Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.992167101828, with STD : 0.0
+accuracy_score on test : 0.69018404908, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 23, max_depth : 10
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.69018404908
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.992125984252
+ - Score on test : 0.694864048338
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.992125984252
+ - Score on test : 0.694864048338
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00783289817232
+ - Score on test : 0.30981595092
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.69018404908
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.984387890829
+ - Score on test : 0.380547177509
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997361477573
+ - Score on test : 0.684523809524
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.986945169713
+ - Score on test : 0.705521472393
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.69018404908
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00783289817232
+ - Score on test : 0.30981595092
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145853Results-KNN--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145853Results-KNN--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..c8044b8e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145853Results-KNN--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.702349869452, with STD : 0.0
+accuracy_score on test : 0.693251533742, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.702349869452
+ - Score on test : 0.693251533742
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.64375
+ - Score on test : 0.63503649635
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.64375
+ - Score on test : 0.63503649635
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.297650130548
+ - Score on test : 0.306748466258
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.702349869452
+ - Score on test : 0.693251533742
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.428554683149
+ - Score on test : 0.407811839631
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.801556420233
+ - Score on test : 0.783783783784
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.537859007833
+ - Score on test : 0.533742331288
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.702349869452
+ - Score on test : 0.693251533742
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.297650130548
+ - Score on test : 0.306748466258
+
+
+ Classification took 0:00:07
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145854Results-SGD--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145854Results-SGD--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..2fdde68d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145854Results-SGD--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.900783289817, with STD : 0.0
+accuracy_score on test : 0.766871165644, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.900783289817
+ - Score on test : 0.766871165644
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.908872901679
+ - Score on test : 0.798941798942
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.908872901679
+ - Score on test : 0.798941798942
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0992167101828
+ - Score on test : 0.233128834356
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.900783289817
+ - Score on test : 0.766871165644
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.814507012356
+ - Score on test : 0.563168730919
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.840354767184
+ - Score on test : 0.702325581395
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.98955613577
+ - Score on test : 0.926380368098
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.900783289817
+ - Score on test : 0.766871165644
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0992167101828
+ - Score on test : 0.233128834356
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145901Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145901Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..a429bde0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145901Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.779141104294, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 285
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.777777777778
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.777777777778
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.220858895706
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.558324238417
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.782608695652
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.773006134969
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.220858895706
+
+
+ Classification took 0:00:07
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145911Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145911Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..60f93844
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145911Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.509138381201, with STD : 0.0
+accuracy_score on test : 0.441717791411, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 4430
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.509138381201
+ - Score on test : 0.441717791411
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.505263157895
+ - Score on test : 0.438271604938
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.505263157895
+ - Score on test : 0.438271604938
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.490861618799
+ - Score on test : 0.558282208589
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.509138381201
+ - Score on test : 0.441717791411
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0182790055307
+ - Score on test : -0.116573192637
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.509283819629
+ - Score on test : 0.44099378882
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.501305483029
+ - Score on test : 0.435582822086
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.509138381201
+ - Score on test : 0.441717791411
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.490861618799
+ - Score on test : 0.558282208589
+
+
+ Classification took 0:00:09
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-145919Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-145919Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..ad610a49
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-145919Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.521472392638, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 285
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.665236051502
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.665236051502
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.478527607362
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.0838518806968
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511551155116
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.950920245399
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.478527607362
+
+
+ Classification took 0:00:17
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150120-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-150120-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..f556f1c1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150120-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,1110 @@
+2017-09-22 15:01:26,012 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 15:01:26,014 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 15:01:26,083 DEBUG: Start: Loading data
+2017-09-22 15:01:26,083 DEBUG: Start: Loading data
+2017-09-22 15:01:26,096 DEBUG: Done: Loading data
+2017-09-22 15:01:26,096 DEBUG: Done: Loading data
+2017-09-22 15:01:26,096 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 15:01:26,096 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 15:01:26,096 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:01:26,096 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:01:26,130 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:01:26,130 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:01:26,130 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:01:26,130 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:01:26,130 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:01:26,131 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:01:26,131 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 15:01:26,131 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 15:01:29,195 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:01:29,195 DEBUG: Start: Training
+2017-09-22 15:01:29,224 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:01:29,225 DEBUG: Start: Training
+2017-09-22 15:01:30,013 DEBUG: Done: Training
+2017-09-22 15:01:30,014 DEBUG: Start: Predicting
+2017-09-22 15:01:30,032 DEBUG: Done: Predicting
+2017-09-22 15:01:30,032 DEBUG: Info: Time for training and predicting: 3.94928002357[s]
+2017-09-22 15:01:30,032 DEBUG: Start: Getting Results
+2017-09-22 15:01:30,062 DEBUG: Done: Getting Results
+2017-09-22 15:01:30,062 INFO: Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.654654654655
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.654654654655
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.294750450473
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.641176470588
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:03
+2017-09-22 15:01:30,062 INFO: Done: Result Analysis
+2017-09-22 15:01:30,110 DEBUG: Done: Training
+2017-09-22 15:01:30,110 DEBUG: Start: Predicting
+2017-09-22 15:01:30,124 DEBUG: Done: Predicting
+2017-09-22 15:01:30,124 DEBUG: Info: Time for training and predicting: 4.04104685783[s]
+2017-09-22 15:01:30,124 DEBUG: Start: Getting Results
+2017-09-22 15:01:30,151 DEBUG: Done: Getting Results
+2017-09-22 15:01:30,151 INFO: Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.628834355828, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.628834355828
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.627692307692
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.627692307692
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.371165644172
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.628834355828
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.257673560841
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.62962962963
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.628834355828
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.371165644172
+
+
+ Classification took 0:00:04
+2017-09-22 15:01:30,151 INFO: Done: Result Analysis
+2017-09-22 15:01:30,250 DEBUG: Start: Loading data
+2017-09-22 15:01:30,250 DEBUG: Start: Loading data
+2017-09-22 15:01:30,263 DEBUG: Done: Loading data
+2017-09-22 15:01:30,263 DEBUG: Done: Loading data
+2017-09-22 15:01:30,263 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 15:01:30,263 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 15:01:30,263 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:01:30,263 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:01:30,296 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:01:30,297 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:01:30,297 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:01:30,297 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 15:01:30,298 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:01:30,298 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:01:30,298 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:01:30,298 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 15:01:31,490 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:01:31,490 DEBUG: Start: Training
+2017-09-22 15:01:31,817 DEBUG: Done: Training
+2017-09-22 15:01:31,817 DEBUG: Start: Predicting
+2017-09-22 15:01:31,891 DEBUG: Done: Predicting
+2017-09-22 15:01:31,891 DEBUG: Info: Time for training and predicting: 1.64091086388[s]
+2017-09-22 15:01:31,891 DEBUG: Start: Getting Results
+2017-09-22 15:01:31,926 DEBUG: Done: Getting Results
+2017-09-22 15:01:31,926 INFO: Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.742331288344, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, 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 : 0.998694516971
+ - Score on test : 0.742331288344
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.732484076433
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.732484076433
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.257668711656
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.742331288344
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.485981339017
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.761589403974
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.705521472393
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.742331288344
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.257668711656
+
+
+ Classification took 0:00:01
+2017-09-22 15:01:31,926 INFO: Done: Result Analysis
+2017-09-22 15:01:33,540 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:01:33,540 DEBUG: Start: Training
+2017-09-22 15:01:33,591 DEBUG: Done: Training
+2017-09-22 15:01:33,591 DEBUG: Start: Predicting
+2017-09-22 15:01:40,710 DEBUG: Done: Predicting
+2017-09-22 15:01:40,711 DEBUG: Info: Time for training and predicting: 10.4605879784[s]
+2017-09-22 15:01:40,711 DEBUG: Start: Getting Results
+2017-09-22 15:01:40,738 DEBUG: Done: Getting Results
+2017-09-22 15:01:40,738 INFO: Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.689295039164, with STD : 0.0
+accuracy_score on test : 0.638036809816, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.689295039164
+ - Score on test : 0.638036809816
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.739035087719
+ - Score on test : 0.70202020202
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.739035087719
+ - Score on test : 0.70202020202
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.310704960836
+ - Score on test : 0.361963190184
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.689295039164
+ - Score on test : 0.638036809816
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.409511397204
+ - Score on test : 0.305698338982
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.637051039698
+ - Score on test : 0.596566523605
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.879895561358
+ - Score on test : 0.852760736196
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.689295039164
+ - Score on test : 0.638036809816
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.310704960836
+ - Score on test : 0.361963190184
+
+
+ Classification took 0:00:10
+2017-09-22 15:01:40,738 INFO: Done: Result Analysis
+2017-09-22 15:01:40,830 DEBUG: Start: Loading data
+2017-09-22 15:01:40,830 DEBUG: Start: Loading data
+2017-09-22 15:01:40,843 DEBUG: Done: Loading data
+2017-09-22 15:01:40,843 DEBUG: Done: Loading data
+2017-09-22 15:01:40,844 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 15:01:40,844 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 15:01:40,844 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:01:40,844 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:01:40,877 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:01:40,877 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:01:40,877 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:01:40,877 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 15:01:40,878 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:01:40,878 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:01:40,878 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:01:40,878 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 15:01:41,486 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:01:41,487 DEBUG: Start: Training
+2017-09-22 15:01:41,634 DEBUG: Done: Training
+2017-09-22 15:01:41,634 DEBUG: Start: Predicting
+2017-09-22 15:01:41,643 DEBUG: Done: Predicting
+2017-09-22 15:01:41,643 DEBUG: Info: Time for training and predicting: 0.812498092651[s]
+2017-09-22 15:01:41,643 DEBUG: Start: Getting Results
+2017-09-22 15:01:41,674 DEBUG: Done: Getting Results
+2017-09-22 15:01:41,674 INFO: Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.72454308094, with STD : 0.0
+accuracy_score on test : 0.671779141104, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.72454308094
+ - Score on test : 0.671779141104
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.720529801325
+ - Score on test : 0.676737160121
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.720529801325
+ - Score on test : 0.676737160121
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.27545691906
+ - Score on test : 0.328220858896
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.72454308094
+ - Score on test : 0.671779141104
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.449271496384
+ - Score on test : 0.343720031298
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.731182795699
+ - Score on test : 0.666666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.710182767624
+ - Score on test : 0.687116564417
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.72454308094
+ - Score on test : 0.671779141104
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.27545691906
+ - Score on test : 0.328220858896
+
+
+ Classification took 0:00:00
+2017-09-22 15:01:41,675 INFO: Done: Result Analysis
+2017-09-22 15:01:44,617 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:01:44,617 DEBUG: Start: Training
+2017-09-22 15:01:50,152 DEBUG: Done: Training
+2017-09-22 15:01:50,153 DEBUG: Start: Predicting
+2017-09-22 15:01:53,024 DEBUG: Done: Predicting
+2017-09-22 15:01:53,025 DEBUG: Info: Time for training and predicting: 12.1938760281[s]
+2017-09-22 15:01:53,025 DEBUG: Start: Getting Results
+2017-09-22 15:01:53,051 DEBUG: Done: Getting Results
+2017-09-22 15:01:53,051 INFO: Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.653374233129, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.346625766871
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.307728727448
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.642045454545
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.346625766871
+
+
+ Classification took 0:00:12
+2017-09-22 15:01:53,052 INFO: Done: Result Analysis
+2017-09-22 15:01:53,216 DEBUG: Start: Loading data
+2017-09-22 15:01:53,217 DEBUG: Start: Loading data
+2017-09-22 15:01:53,230 DEBUG: Done: Loading data
+2017-09-22 15:01:53,230 DEBUG: Done: Loading data
+2017-09-22 15:01:53,230 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 15:01:53,230 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 15:01:53,230 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:01:53,230 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:01:53,264 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:01:53,264 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:01:53,264 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:01:53,264 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 15:01:53,265 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:01:53,265 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:01:53,265 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:01:53,265 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 15:01:59,955 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:01:59,955 DEBUG: Start: Training
+2017-09-22 15:02:02,365 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:02:02,366 DEBUG: Start: Training
+2017-09-22 15:02:10,969 DEBUG: Done: Training
+2017-09-22 15:02:10,969 DEBUG: Start: Predicting
+2017-09-22 15:02:17,000 DEBUG: Done: Predicting
+2017-09-22 15:02:17,001 DEBUG: Info: Time for training and predicting: 23.7835571766[s]
+2017-09-22 15:02:17,001 DEBUG: Start: Getting Results
+2017-09-22 15:02:17,046 DEBUG: Done: Getting Results
+2017-09-22 15:02:17,046 INFO: Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.926892950392, with STD : 0.0
+accuracy_score on test : 0.674846625767, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.926892950392
+ - Score on test : 0.674846625767
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.927648578811
+ - Score on test : 0.684523809524
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.927648578811
+ - Score on test : 0.684523809524
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0731070496084
+ - Score on test : 0.325153374233
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.926892950392
+ - Score on test : 0.674846625767
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.85397221395
+ - Score on test : 0.350353200131
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.918158567775
+ - Score on test : 0.664739884393
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.937336814621
+ - Score on test : 0.705521472393
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.926892950392
+ - Score on test : 0.674846625767
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0731070496084
+ - Score on test : 0.325153374233
+
+
+ Classification took 0:00:23
+2017-09-22 15:02:17,047 INFO: Done: Result Analysis
+2017-09-22 15:02:17,195 DEBUG: Done: Training
+2017-09-22 15:02:17,195 DEBUG: Start: Predicting
+2017-09-22 15:02:21,914 DEBUG: Done: Predicting
+2017-09-22 15:02:21,914 DEBUG: Info: Time for training and predicting: 28.6970870495[s]
+2017-09-22 15:02:21,915 DEBUG: Start: Getting Results
+2017-09-22 15:02:21,947 DEBUG: Done: Getting Results
+2017-09-22 15:02:21,947 INFO: Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.934725848564, with STD : 0.0
+accuracy_score on test : 0.546012269939, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.934725848564
+ - Score on test : 0.546012269939
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.931129476584
+ - Score on test : 0.350877192982
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.931129476584
+ - Score on test : 0.350877192982
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.065274151436
+ - Score on test : 0.453987730061
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.934725848564
+ - Score on test : 0.546012269939
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.874232585037
+ - Score on test : 0.115163359926
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.985422740525
+ - Score on test : 0.615384615385
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.882506527415
+ - Score on test : 0.245398773006
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.934725848564
+ - Score on test : 0.546012269939
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.065274151436
+ - Score on test : 0.453987730061
+
+
+ Classification took 0:00:28
+2017-09-22 15:02:21,948 INFO: Done: Result Analysis
+2017-09-22 15:02:22,034 DEBUG: Start: Loading data
+2017-09-22 15:02:22,035 DEBUG: Start: Loading data
+2017-09-22 15:02:22,045 DEBUG: Done: Loading data
+2017-09-22 15:02:22,045 DEBUG: Done: Loading data
+2017-09-22 15:02:22,045 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 15:02:22,046 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 15:02:22,046 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:02:22,046 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:02:22,073 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:02:22,073 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:02:22,074 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:02:22,074 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 15:02:22,074 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:02:22,074 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:02:22,075 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:02:22,075 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 15:02:23,764 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:02:23,764 DEBUG: Start: Training
+2017-09-22 15:02:23,825 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:02:23,825 DEBUG: Start: Training
+2017-09-22 15:02:24,472 DEBUG: Done: Training
+2017-09-22 15:02:24,472 DEBUG: Start: Predicting
+2017-09-22 15:02:24,474 DEBUG: Done: Training
+2017-09-22 15:02:24,474 DEBUG: Start: Predicting
+2017-09-22 15:02:24,488 DEBUG: Done: Predicting
+2017-09-22 15:02:24,488 DEBUG: Info: Time for training and predicting: 2.45340418816[s]
+2017-09-22 15:02:24,489 DEBUG: Start: Getting Results
+2017-09-22 15:02:24,492 DEBUG: Done: Predicting
+2017-09-22 15:02:24,492 DEBUG: Info: Time for training and predicting: 2.45775198936[s]
+2017-09-22 15:02:24,493 DEBUG: Start: Getting Results
+2017-09-22 15:02:24,532 DEBUG: Done: Getting Results
+2017-09-22 15:02:24,532 INFO: Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670658682635
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670658682635
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.325545701512
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.654970760234
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.687116564417
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:02
+2017-09-22 15:02:24,532 INFO: Done: Result Analysis
+2017-09-22 15:02:24,536 DEBUG: Done: Getting Results
+2017-09-22 15:02:24,537 INFO: Classification on awaexp database for lss-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.634969325153, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.634969325153
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.624605678233
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.624605678233
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.365030674847
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.634969325153
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.270351069901
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.642857142857
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.60736196319
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.634969325153
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.365030674847
+
+
+ Classification took 0:00:02
+2017-09-22 15:02:24,537 INFO: Done: Result Analysis
+2017-09-22 15:02:24,694 DEBUG: Start: Loading data
+2017-09-22 15:02:24,694 DEBUG: Start: Loading data
+2017-09-22 15:02:24,705 DEBUG: Done: Loading data
+2017-09-22 15:02:24,705 DEBUG: Done: Loading data
+2017-09-22 15:02:24,705 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 15:02:24,705 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 15:02:24,705 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:02:24,705 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:02:24,734 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:02:24,734 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:02:24,734 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:02:24,735 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 15:02:24,735 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:02:24,735 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:02:24,735 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:02:24,736 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 15:02:25,705 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:02:25,706 DEBUG: Start: Training
+2017-09-22 15:02:25,948 DEBUG: Done: Training
+2017-09-22 15:02:25,948 DEBUG: Start: Predicting
+2017-09-22 15:02:26,015 DEBUG: Done: Predicting
+2017-09-22 15:02:26,015 DEBUG: Info: Time for training and predicting: 1.32054591179[s]
+2017-09-22 15:02:26,015 DEBUG: Start: Getting Results
+2017-09-22 15:02:26,057 DEBUG: Done: Getting Results
+2017-09-22 15:02:26,057 INFO: Classification on awaexp database for lss-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.687116564417, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, 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 : 0.998694516971
+ - Score on test : 0.687116564417
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.692771084337
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.692771084337
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.312883435583
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.687116564417
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.374486922712
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.680473372781
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.705521472393
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.687116564417
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.312883435583
+
+
+ Classification took 0:00:01
+2017-09-22 15:02:26,057 INFO: Done: Result Analysis
+2017-09-22 15:02:27,144 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:02:27,144 DEBUG: Start: Training
+2017-09-22 15:02:27,173 DEBUG: Done: Training
+2017-09-22 15:02:27,173 DEBUG: Start: Predicting
+2017-09-22 15:02:32,275 DEBUG: Done: Predicting
+2017-09-22 15:02:32,275 DEBUG: Info: Time for training and predicting: 7.58051395416[s]
+2017-09-22 15:02:32,275 DEBUG: Start: Getting Results
+2017-09-22 15:02:32,312 DEBUG: Done: Getting Results
+2017-09-22 15:02:32,312 INFO: Classification on awaexp database for lss-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.711488250653, with STD : 0.0
+accuracy_score on test : 0.696319018405, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.711488250653
+ - Score on test : 0.696319018405
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.690042075736
+ - Score on test : 0.650176678445
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.690042075736
+ - Score on test : 0.650176678445
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.288511749347
+ - Score on test : 0.303680981595
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.711488250653
+ - Score on test : 0.696319018405
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.427085473492
+ - Score on test : 0.407057481052
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.745454545455
+ - Score on test : 0.766666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.642297650131
+ - Score on test : 0.564417177914
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.711488250653
+ - Score on test : 0.696319018405
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.288511749347
+ - Score on test : 0.303680981595
+
+
+ Classification took 0:00:07
+2017-09-22 15:02:32,313 INFO: Done: Result Analysis
+2017-09-22 15:02:32,473 DEBUG: Start: Loading data
+2017-09-22 15:02:32,473 DEBUG: Start: Loading data
+2017-09-22 15:02:32,483 DEBUG: Done: Loading data
+2017-09-22 15:02:32,483 DEBUG: Done: Loading data
+2017-09-22 15:02:32,483 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 15:02:32,483 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 15:02:32,483 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:02:32,483 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:02:32,511 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:02:32,511 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:02:32,511 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:02:32,511 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 15:02:32,511 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:02:32,512 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:02:32,512 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:02:32,512 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 15:02:32,894 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:02:32,895 DEBUG: Start: Training
+2017-09-22 15:02:32,994 DEBUG: Done: Training
+2017-09-22 15:02:32,995 DEBUG: Start: Predicting
+2017-09-22 15:02:33,002 DEBUG: Done: Predicting
+2017-09-22 15:02:33,003 DEBUG: Info: Time for training and predicting: 0.529504060745[s]
+2017-09-22 15:02:33,003 DEBUG: Start: Getting Results
+2017-09-22 15:02:33,034 DEBUG: Done: Getting Results
+2017-09-22 15:02:33,035 INFO: Classification on awaexp database for lss-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.94908616188, with STD : 0.0
+accuracy_score on test : 0.78527607362, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.94908616188
+ - Score on test : 0.78527607362
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.951188986233
+ - Score on test : 0.802259887006
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.951188986233
+ - Score on test : 0.802259887006
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0509138381201
+ - Score on test : 0.21472392638
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.94908616188
+ - Score on test : 0.78527607362
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.901524959581
+ - Score on test : 0.579161095181
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.913461538462
+ - Score on test : 0.743455497382
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.871165644172
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.94908616188
+ - Score on test : 0.78527607362
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0509138381201
+ - Score on test : 0.21472392638
+
+
+ Classification took 0:00:00
+2017-09-22 15:02:33,035 INFO: Done: Result Analysis
+2017-09-22 15:02:34,972 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:02:34,972 DEBUG: Start: Training
+2017-09-22 15:02:38,711 DEBUG: Done: Training
+2017-09-22 15:02:38,711 DEBUG: Start: Predicting
+2017-09-22 15:02:40,569 DEBUG: Done: Predicting
+2017-09-22 15:02:40,570 DEBUG: Info: Time for training and predicting: 8.09667515755[s]
+2017-09-22 15:02:40,570 DEBUG: Start: Getting Results
+2017-09-22 15:02:40,597 DEBUG: Done: Getting Results
+2017-09-22 15:02:40,597 INFO: Classification on awaexp database for lss-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.760736196319, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757763975155
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757763975155
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.239263803681
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521629480383
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.767295597484
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.748466257669
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.239263803681
+
+
+ Classification took 0:00:08
+2017-09-22 15:02:40,597 INFO: Done: Result Analysis
+2017-09-22 15:02:40,747 DEBUG: Start: Loading data
+2017-09-22 15:02:40,748 DEBUG: Start: Loading data
+2017-09-22 15:02:40,758 DEBUG: Done: Loading data
+2017-09-22 15:02:40,758 DEBUG: Done: Loading data
+2017-09-22 15:02:40,758 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 15:02:40,758 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 15:02:40,759 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:02:40,759 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:02:40,785 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:02:40,786 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:02:40,786 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:02:40,786 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 15:02:40,787 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:02:40,787 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:02:40,788 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:02:40,788 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 15:02:44,010 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:02:44,010 DEBUG: Start: Training
+2017-09-22 15:02:46,331 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:02:46,332 DEBUG: Start: Training
+2017-09-22 15:02:47,036 DEBUG: Done: Training
+2017-09-22 15:02:47,036 DEBUG: Start: Predicting
+2017-09-22 15:02:48,488 DEBUG: Done: Predicting
+2017-09-22 15:02:48,489 DEBUG: Info: Time for training and predicting: 7.74067902565[s]
+2017-09-22 15:02:48,489 DEBUG: Start: Getting Results
+2017-09-22 15:02:48,518 DEBUG: Done: Getting Results
+2017-09-22 15:02:48,518 INFO: Classification on awaexp database for lss-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.661879895561, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.681426814268
+ - Score on test : 0.681818181818
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.681426814268
+ - Score on test : 0.681818181818
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.338120104439
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.32622543483
+ - Score on test : 0.316941413476
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.644186046512
+ - Score on test : 0.634920634921
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.723237597911
+ - Score on test : 0.736196319018
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.338120104439
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:00:07
+2017-09-22 15:02:48,518 INFO: Done: Result Analysis
+2017-09-22 15:02:52,780 DEBUG: Done: Training
+2017-09-22 15:02:52,780 DEBUG: Start: Predicting
+2017-09-22 15:02:56,290 DEBUG: Done: Predicting
+2017-09-22 15:02:56,290 DEBUG: Info: Time for training and predicting: 15.5415859222[s]
+2017-09-22 15:02:56,290 DEBUG: Start: Getting Results
+2017-09-22 15:02:56,316 DEBUG: Done: Getting Results
+2017-09-22 15:02:56,316 INFO: Classification on awaexp database for lss-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.521472392638, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.669491525424
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.669491525424
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.478527607362
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.0965815875096
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511326860841
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.969325153374
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.478527607362
+
+
+ Classification took 0:00:15
+2017-09-22 15:02:56,317 INFO: Done: Result Analysis
+2017-09-22 15:02:56,431 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:02:56,432 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:02:56,433 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:02:56,433 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:02:56,434 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:02:56,435 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:02:56,435 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:02:56,436 INFO: Done: Read Database Files
+2017-09-22 15:02:56,436 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:02:56,436 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:02:56,436 INFO: Done: Read Database Files
+2017-09-22 15:02:56,436 INFO: Start: Determine validation split for ratio 0.7
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150130Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150130Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..8e5e9f9f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150130Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.654654654655
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.654654654655
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.294750450473
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.641176470588
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150130Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150130Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..957fabe5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150130Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.628834355828, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.628834355828
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.627692307692
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.627692307692
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.371165644172
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.628834355828
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.257673560841
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.62962962963
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.628834355828
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.371165644172
+
+
+ Classification took 0:00:04
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150131Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150131Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..2bec4a8d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150131Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.742331288344, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, 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 : 0.998694516971
+ - Score on test : 0.742331288344
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.732484076433
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.732484076433
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.257668711656
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.742331288344
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.485981339017
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.761589403974
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.705521472393
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.742331288344
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.257668711656
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150140Results-KNN--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150140Results-KNN--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..c893a067
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150140Results-KNN--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.689295039164, with STD : 0.0
+accuracy_score on test : 0.638036809816, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.689295039164
+ - Score on test : 0.638036809816
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.739035087719
+ - Score on test : 0.70202020202
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.739035087719
+ - Score on test : 0.70202020202
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.310704960836
+ - Score on test : 0.361963190184
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.689295039164
+ - Score on test : 0.638036809816
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.409511397204
+ - Score on test : 0.305698338982
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.637051039698
+ - Score on test : 0.596566523605
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.879895561358
+ - Score on test : 0.852760736196
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.689295039164
+ - Score on test : 0.638036809816
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.310704960836
+ - Score on test : 0.361963190184
+
+
+ Classification took 0:00:10
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150141Results-SGD--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150141Results-SGD--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..5078d672
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150141Results-SGD--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.72454308094, with STD : 0.0
+accuracy_score on test : 0.671779141104, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.72454308094
+ - Score on test : 0.671779141104
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.720529801325
+ - Score on test : 0.676737160121
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.720529801325
+ - Score on test : 0.676737160121
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.27545691906
+ - Score on test : 0.328220858896
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.72454308094
+ - Score on test : 0.671779141104
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.449271496384
+ - Score on test : 0.343720031298
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.731182795699
+ - Score on test : 0.666666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.710182767624
+ - Score on test : 0.687116564417
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.72454308094
+ - Score on test : 0.671779141104
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.27545691906
+ - Score on test : 0.328220858896
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150153Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150153Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..443ccb0c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150153Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.653374233129, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.346625766871
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.307728727448
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.642045454545
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.653374233129
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.346625766871
+
+
+ Classification took 0:00:12
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150217Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150217Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..104c1623
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150217Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.926892950392, with STD : 0.0
+accuracy_score on test : 0.674846625767, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.926892950392
+ - Score on test : 0.674846625767
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.927648578811
+ - Score on test : 0.684523809524
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.927648578811
+ - Score on test : 0.684523809524
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0731070496084
+ - Score on test : 0.325153374233
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.926892950392
+ - Score on test : 0.674846625767
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.85397221395
+ - Score on test : 0.350353200131
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.918158567775
+ - Score on test : 0.664739884393
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.937336814621
+ - Score on test : 0.705521472393
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.926892950392
+ - Score on test : 0.674846625767
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0731070496084
+ - Score on test : 0.325153374233
+
+
+ Classification took 0:00:23
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150221Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150221Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..a7f7aeba
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150221Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.934725848564, with STD : 0.0
+accuracy_score on test : 0.546012269939, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.934725848564
+ - Score on test : 0.546012269939
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.931129476584
+ - Score on test : 0.350877192982
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.931129476584
+ - Score on test : 0.350877192982
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.065274151436
+ - Score on test : 0.453987730061
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.934725848564
+ - Score on test : 0.546012269939
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.874232585037
+ - Score on test : 0.115163359926
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.985422740525
+ - Score on test : 0.615384615385
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.882506527415
+ - Score on test : 0.245398773006
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.934725848564
+ - Score on test : 0.546012269939
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.065274151436
+ - Score on test : 0.453987730061
+
+
+ Classification took 0:00:28
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150224Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150224Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..c4a70d20
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150224Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for lss-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.634969325153, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.634969325153
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.624605678233
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.624605678233
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.365030674847
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.634969325153
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.270351069901
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.642857142857
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.60736196319
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.634969325153
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.365030674847
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150224Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150224Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..2352e310
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150224Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670658682635
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670658682635
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.325545701512
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.654970760234
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.687116564417
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150226Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150226Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..9872a6e3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150226Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.687116564417, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 15, 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 : 0.998694516971
+ - Score on test : 0.687116564417
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.692771084337
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.692771084337
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.312883435583
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.687116564417
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.374486922712
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.680473372781
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.705521472393
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.687116564417
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.312883435583
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150232Results-KNN--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150232Results-KNN--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..6b10db3d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150232Results-KNN--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.711488250653, with STD : 0.0
+accuracy_score on test : 0.696319018405, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.711488250653
+ - Score on test : 0.696319018405
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.690042075736
+ - Score on test : 0.650176678445
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.690042075736
+ - Score on test : 0.650176678445
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.288511749347
+ - Score on test : 0.303680981595
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.711488250653
+ - Score on test : 0.696319018405
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.427085473492
+ - Score on test : 0.407057481052
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.745454545455
+ - Score on test : 0.766666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.642297650131
+ - Score on test : 0.564417177914
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.711488250653
+ - Score on test : 0.696319018405
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.288511749347
+ - Score on test : 0.303680981595
+
+
+ Classification took 0:00:07
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150233Results-SGD--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150233Results-SGD--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..492c2a10
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150233Results-SGD--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.94908616188, with STD : 0.0
+accuracy_score on test : 0.78527607362, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.94908616188
+ - Score on test : 0.78527607362
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.951188986233
+ - Score on test : 0.802259887006
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.951188986233
+ - Score on test : 0.802259887006
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0509138381201
+ - Score on test : 0.21472392638
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.94908616188
+ - Score on test : 0.78527607362
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.901524959581
+ - Score on test : 0.579161095181
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.913461538462
+ - Score on test : 0.743455497382
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.871165644172
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.94908616188
+ - Score on test : 0.78527607362
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0509138381201
+ - Score on test : 0.21472392638
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150240Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150240Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..00fc11ce
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150240Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.760736196319, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757763975155
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757763975155
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.239263803681
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521629480383
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.767295597484
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.748466257669
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.239263803681
+
+
+ Classification took 0:00:08
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150248Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150248Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..6d14326a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150248Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.661879895561, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.681426814268
+ - Score on test : 0.681818181818
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.681426814268
+ - Score on test : 0.681818181818
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.338120104439
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.32622543483
+ - Score on test : 0.316941413476
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.644186046512
+ - Score on test : 0.634920634921
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.723237597911
+ - Score on test : 0.736196319018
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.338120104439
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:00:07
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150256Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150256Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..369a7064
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150256Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.521472392638, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 2330
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.669491525424
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.669491525424
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.478527607362
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.0965815875096
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.511326860841
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.969325153374
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.521472392638
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.478527607362
+
+
+ Classification took 0:00:15
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150459-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-150459-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150635-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-150635-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..b60ea211
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150635-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,626 @@
+2017-09-22 15:06:41,766 DEBUG: Info: Enough copies of the dataset are already available
+2017-09-22 15:06:41,768 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 15:06:41,843 DEBUG: Start: Loading data
+2017-09-22 15:06:41,843 DEBUG: Start: Loading data
+2017-09-22 15:06:41,866 DEBUG: Done: Loading data
+2017-09-22 15:06:41,866 DEBUG: Done: Loading data
+2017-09-22 15:06:41,866 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 15:06:41,866 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 15:06:41,866 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:06:41,866 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:06:41,903 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:06:41,903 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:06:41,903 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:06:41,903 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:06:41,903 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:06:41,903 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:06:41,903 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 15:06:41,903 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 15:06:43,675 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:06:43,675 DEBUG: Start: Training
+2017-09-22 15:06:43,984 DEBUG: Done: Training
+2017-09-22 15:06:43,984 DEBUG: Start: Predicting
+2017-09-22 15:06:43,999 DEBUG: Done: Predicting
+2017-09-22 15:06:43,999 DEBUG: Info: Time for training and predicting: 2.15576291084[s]
+2017-09-22 15:06:43,999 DEBUG: Start: Getting Results
+2017-09-22 15:06:44,044 DEBUG: Done: Getting Results
+2017-09-22 15:06:44,044 INFO: Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.728459530026, with STD : 0.0
+accuracy_score on test : 0.677914110429, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.728459530026
+ - Score on test : 0.677914110429
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.718918918919
+ - Score on test : 0.672897196262
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.718918918919
+ - Score on test : 0.672897196262
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.271540469974
+ - Score on test : 0.322085889571
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.728459530026
+ - Score on test : 0.677914110429
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.457975543398
+ - Score on test : 0.355995746702
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.745098039216
+ - Score on test : 0.683544303797
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.694516971279
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.728459530026
+ - Score on test : 0.677914110429
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.271540469974
+ - Score on test : 0.322085889571
+
+
+ Classification took 0:00:02
+2017-09-22 15:06:44,044 INFO: Done: Result Analysis
+2017-09-22 15:06:44,878 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:06:44,878 DEBUG: Start: Training
+2017-09-22 15:06:45,718 DEBUG: Done: Training
+2017-09-22 15:06:45,718 DEBUG: Start: Predicting
+2017-09-22 15:06:45,734 DEBUG: Done: Predicting
+2017-09-22 15:06:45,734 DEBUG: Info: Time for training and predicting: 3.89099097252[s]
+2017-09-22 15:06:45,734 DEBUG: Start: Getting Results
+2017-09-22 15:06:45,762 DEBUG: Done: Getting Results
+2017-09-22 15:06:45,762 INFO: Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.650455927052
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.650455927052
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.294528416204
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.644578313253
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:03
+2017-09-22 15:06:45,762 INFO: Done: Result Analysis
+2017-09-22 15:06:45,911 DEBUG: Start: Loading data
+2017-09-22 15:06:45,912 DEBUG: Start: Loading data
+2017-09-22 15:06:45,929 DEBUG: Done: Loading data
+2017-09-22 15:06:45,929 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 15:06:45,929 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:06:45,929 DEBUG: Done: Loading data
+2017-09-22 15:06:45,929 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 15:06:45,930 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:06:45,953 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:06:45,953 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:06:45,953 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:06:45,953 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:06:45,954 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:06:45,954 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:06:45,954 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 15:06:45,954 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 15:06:46,904 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:06:46,904 DEBUG: Start: Training
+2017-09-22 15:06:47,242 DEBUG: Done: Training
+2017-09-22 15:06:47,242 DEBUG: Start: Predicting
+2017-09-22 15:06:47,312 DEBUG: Done: Predicting
+2017-09-22 15:06:47,313 DEBUG: Info: Time for training and predicting: 1.40054106712[s]
+2017-09-22 15:06:47,313 DEBUG: Start: Getting Results
+2017-09-22 15:06:47,341 DEBUG: Done: Getting Results
+2017-09-22 15:06:47,341 INFO: Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.757668711656, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.757668711656
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.755417956656
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.755417956656
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.242331288344
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757668711656
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.515424728358
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.7625
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.748466257669
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757668711656
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.242331288344
+
+
+ Classification took 0:00:01
+2017-09-22 15:06:47,341 INFO: Done: Result Analysis
+2017-09-22 15:06:48,591 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:06:48,591 DEBUG: Start: Training
+2017-09-22 15:06:48,652 DEBUG: Done: Training
+2017-09-22 15:06:48,652 DEBUG: Start: Predicting
+2017-09-22 15:06:56,122 DEBUG: Done: Predicting
+2017-09-22 15:06:56,122 DEBUG: Info: Time for training and predicting: 10.2107279301[s]
+2017-09-22 15:06:56,122 DEBUG: Start: Getting Results
+2017-09-22 15:06:56,149 DEBUG: Done: Getting Results
+2017-09-22 15:06:56,149 INFO: Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.727154046997, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.727154046997
+ - Score on test : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.731707317073
+ - Score on test : 0.679665738162
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.731707317073
+ - Score on test : 0.679665738162
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.272845953003
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.727154046997
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.454570023906
+ - Score on test : 0.30070560662
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.719696969697
+ - Score on test : 0.622448979592
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.744125326371
+ - Score on test : 0.748466257669
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.727154046997
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.272845953003
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:10
+2017-09-22 15:06:56,150 INFO: Done: Result Analysis
+2017-09-22 15:06:56,294 DEBUG: Start: Loading data
+2017-09-22 15:06:56,294 DEBUG: Start: Loading data
+2017-09-22 15:06:56,316 DEBUG: Done: Loading data
+2017-09-22 15:06:56,316 DEBUG: Done: Loading data
+2017-09-22 15:06:56,316 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 15:06:56,316 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 15:06:56,316 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:06:56,316 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:06:56,352 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:06:56,352 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:06:56,352 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:06:56,352 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:06:56,353 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:06:56,353 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:06:56,353 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 15:06:56,353 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 15:06:56,758 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:06:56,758 DEBUG: Start: Training
+2017-09-22 15:06:56,821 DEBUG: Done: Training
+2017-09-22 15:06:56,821 DEBUG: Start: Predicting
+2017-09-22 15:06:56,833 DEBUG: Done: Predicting
+2017-09-22 15:06:56,834 DEBUG: Info: Time for training and predicting: 0.539541006088[s]
+2017-09-22 15:06:56,834 DEBUG: Start: Getting Results
+2017-09-22 15:06:56,876 DEBUG: Done: Getting Results
+2017-09-22 15:06:56,876 INFO: Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.68407310705, with STD : 0.0
+accuracy_score on test : 0.625766871166, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.68407310705
+ - Score on test : 0.625766871166
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.688144329897
+ - Score on test : 0.625766871166
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.688144329897
+ - Score on test : 0.625766871166
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.31592689295
+ - Score on test : 0.374233128834
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.68407310705
+ - Score on test : 0.625766871166
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.368271763578
+ - Score on test : 0.251533742331
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.679389312977
+ - Score on test : 0.625766871166
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.697127937337
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.68407310705
+ - Score on test : 0.625766871166
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.31592689295
+ - Score on test : 0.374233128834
+
+
+ Classification took 0:00:00
+2017-09-22 15:06:56,877 INFO: Done: Result Analysis
+2017-09-22 15:07:00,213 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:07:00,213 DEBUG: Start: Training
+2017-09-22 15:07:05,916 DEBUG: Done: Training
+2017-09-22 15:07:05,917 DEBUG: Start: Predicting
+2017-09-22 15:07:08,903 DEBUG: Done: Predicting
+2017-09-22 15:07:08,903 DEBUG: Info: Time for training and predicting: 12.6086997986[s]
+2017-09-22 15:07:08,903 DEBUG: Start: Getting Results
+2017-09-22 15:07:08,930 DEBUG: Done: Getting Results
+2017-09-22 15:07:08,930 INFO: Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.650306748466, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 5890
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.650306748466
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662721893491
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662721893491
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.349693251534
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.650306748466
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.301431463441
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.64
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.687116564417
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.650306748466
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.349693251534
+
+
+ Classification took 0:00:12
+2017-09-22 15:07:08,930 INFO: Done: Result Analysis
+2017-09-22 15:07:09,078 DEBUG: Start: Loading data
+2017-09-22 15:07:09,078 DEBUG: Start: Loading data
+2017-09-22 15:07:09,098 DEBUG: Done: Loading data
+2017-09-22 15:07:09,098 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 15:07:09,098 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:07:09,098 DEBUG: Done: Loading data
+2017-09-22 15:07:09,099 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 15:07:09,099 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:07:09,134 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:07:09,134 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:07:09,134 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:07:09,134 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:07:09,135 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:07:09,135 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:07:09,135 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 15:07:09,135 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 15:07:13,889 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:07:13,890 DEBUG: Start: Training
+2017-09-22 15:07:15,638 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:07:15,638 DEBUG: Start: Training
+2017-09-22 15:07:21,640 DEBUG: Done: Training
+2017-09-22 15:07:21,640 DEBUG: Start: Predicting
+2017-09-22 15:07:25,350 DEBUG: Done: Predicting
+2017-09-22 15:07:25,350 DEBUG: Info: Time for training and predicting: 16.2716319561[s]
+2017-09-22 15:07:25,350 DEBUG: Start: Getting Results
+2017-09-22 15:07:25,382 DEBUG: Done: Getting Results
+2017-09-22 15:07:25,382 INFO: Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.95953002611, with STD : 0.0
+accuracy_score on test : 0.668711656442, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 5890
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.95953002611
+ - Score on test : 0.668711656442
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.959687906372
+ - Score on test : 0.674698795181
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.959687906372
+ - Score on test : 0.674698795181
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0404699738903
+ - Score on test : 0.331288343558
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.95953002611
+ - Score on test : 0.668711656442
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.919088247657
+ - Score on test : 0.337652143429
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.955958549223
+ - Score on test : 0.662721893491
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.963446475196
+ - Score on test : 0.687116564417
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.95953002611
+ - Score on test : 0.668711656442
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0404699738903
+ - Score on test : 0.331288343558
+
+
+ Classification took 0:00:16
+2017-09-22 15:07:25,382 INFO: Done: Result Analysis
+2017-09-22 15:07:26,028 DEBUG: Done: Training
+2017-09-22 15:07:26,028 DEBUG: Start: Predicting
+2017-09-22 15:07:30,810 DEBUG: Done: Predicting
+2017-09-22 15:07:30,810 DEBUG: Info: Time for training and predicting: 21.7312428951[s]
+2017-09-22 15:07:30,810 DEBUG: Start: Getting Results
+2017-09-22 15:07:30,837 DEBUG: Done: Getting Results
+2017-09-22 15:07:30,837 INFO: Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.946475195822, with STD : 0.0
+accuracy_score on test : 0.509202453988, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 5890
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.509202453988
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.945113788487
+ - Score on test : 0.370078740157
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.945113788487
+ - Score on test : 0.370078740157
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.490797546012
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.509202453988
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.894051194358
+ - Score on test : 0.0205147688265
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.96978021978
+ - Score on test : 0.516483516484
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.921671018277
+ - Score on test : 0.288343558282
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.509202453988
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.490797546012
+
+
+ Classification took 0:00:21
+2017-09-22 15:07:30,837 INFO: Done: Result Analysis
+2017-09-22 15:07:31,002 DEBUG: Start: Loading data
+2017-09-22 15:07:31,003 DEBUG: Start: Loading data
+2017-09-22 15:07:31,020 DEBUG: Done: Loading data
+2017-09-22 15:07:31,020 DEBUG: Done: Loading data
+2017-09-22 15:07:31,020 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 15:07:31,020 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 15:07:31,020 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:07:31,020 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:07:31,052 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:07:31,052 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:07:31,052 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:07:31,052 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 15:07:31,052 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:07:31,053 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:07:31,053 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:07:31,053 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 15:07:32,029 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:07:32,029 DEBUG: Start: Training
+2017-09-22 15:07:32,179 DEBUG: Done: Training
+2017-09-22 15:07:32,179 DEBUG: Start: Predicting
+2017-09-22 15:07:32,191 DEBUG: Done: Predicting
+2017-09-22 15:07:32,191 DEBUG: Info: Time for training and predicting: 1.1876680851[s]
+2017-09-22 15:07:32,192 DEBUG: Start: Getting Results
+2017-09-22 15:07:32,234 DEBUG: Done: Getting Results
+2017-09-22 15:07:32,234 INFO: Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.72454308094, with STD : 0.0
+accuracy_score on test : 0.726993865031, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.72454308094
+ - Score on test : 0.726993865031
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.693759071118
+ - Score on test : 0.696245733788
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.693759071118
+ - Score on test : 0.696245733788
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.27545691906
+ - Score on test : 0.273006134969
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.72454308094
+ - Score on test : 0.726993865031
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.458446665055
+ - Score on test : 0.463587810205
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.781045751634
+ - Score on test : 0.784615384615
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.624020887728
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.72454308094
+ - Score on test : 0.726993865031
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.27545691906
+ - Score on test : 0.273006134969
+
+
+ Classification took 0:00:01
+2017-09-22 15:07:32,234 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150644Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150644Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..cbca8eb0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150644Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.728459530026, with STD : 0.0
+accuracy_score on test : 0.677914110429, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.728459530026
+ - Score on test : 0.677914110429
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.718918918919
+ - Score on test : 0.672897196262
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.718918918919
+ - Score on test : 0.672897196262
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.271540469974
+ - Score on test : 0.322085889571
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.728459530026
+ - Score on test : 0.677914110429
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.457975543398
+ - Score on test : 0.355995746702
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.745098039216
+ - Score on test : 0.683544303797
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.694516971279
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.728459530026
+ - Score on test : 0.677914110429
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.271540469974
+ - Score on test : 0.322085889571
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150645Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150645Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..df58139f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150645Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.650455927052
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.650455927052
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.294528416204
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.644578313253
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150647Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150647Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..f33428be
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150647Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.757668711656, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.757668711656
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.755417956656
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.755417956656
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.242331288344
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757668711656
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.515424728358
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.7625
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.748466257669
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757668711656
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.242331288344
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150656Results-KNN--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150656Results-KNN--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..ea53ff6e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150656Results-KNN--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.727154046997, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 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 : 0.727154046997
+ - Score on test : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.731707317073
+ - Score on test : 0.679665738162
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.731707317073
+ - Score on test : 0.679665738162
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.272845953003
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.727154046997
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.454570023906
+ - Score on test : 0.30070560662
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.719696969697
+ - Score on test : 0.622448979592
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.744125326371
+ - Score on test : 0.748466257669
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.727154046997
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.272845953003
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:10
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150656Results-SGD--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150656Results-SGD--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..fba9a251
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150656Results-SGD--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.68407310705, with STD : 0.0
+accuracy_score on test : 0.625766871166, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.68407310705
+ - Score on test : 0.625766871166
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.688144329897
+ - Score on test : 0.625766871166
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.688144329897
+ - Score on test : 0.625766871166
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.31592689295
+ - Score on test : 0.374233128834
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.68407310705
+ - Score on test : 0.625766871166
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.368271763578
+ - Score on test : 0.251533742331
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.679389312977
+ - Score on test : 0.625766871166
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.697127937337
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.68407310705
+ - Score on test : 0.625766871166
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.31592689295
+ - Score on test : 0.374233128834
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150708Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150708Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..57fff150
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150708Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.650306748466, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 5890
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.650306748466
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662721893491
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662721893491
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.349693251534
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.650306748466
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.301431463441
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.64
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.687116564417
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.650306748466
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.349693251534
+
+
+ Classification took 0:00:12
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150725Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150725Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..98a63989
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150725Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.95953002611, with STD : 0.0
+accuracy_score on test : 0.668711656442, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 5890
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.95953002611
+ - Score on test : 0.668711656442
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.959687906372
+ - Score on test : 0.674698795181
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.959687906372
+ - Score on test : 0.674698795181
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0404699738903
+ - Score on test : 0.331288343558
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.95953002611
+ - Score on test : 0.668711656442
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.919088247657
+ - Score on test : 0.337652143429
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.955958549223
+ - Score on test : 0.662721893491
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.963446475196
+ - Score on test : 0.687116564417
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.95953002611
+ - Score on test : 0.668711656442
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0404699738903
+ - Score on test : 0.331288343558
+
+
+ Classification took 0:00:16
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150730Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150730Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..c856d9f1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150730Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.946475195822, with STD : 0.0
+accuracy_score on test : 0.509202453988, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 5890
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.509202453988
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.945113788487
+ - Score on test : 0.370078740157
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.945113788487
+ - Score on test : 0.370078740157
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.490797546012
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.509202453988
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.894051194358
+ - Score on test : 0.0205147688265
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.96978021978
+ - Score on test : 0.516483516484
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.921671018277
+ - Score on test : 0.288343558282
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.509202453988
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.490797546012
+
+
+ Classification took 0:00:21
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150732Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150732Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..ad68396c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150732Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.72454308094, with STD : 0.0
+accuracy_score on test : 0.726993865031, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.72454308094
+ - Score on test : 0.726993865031
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.693759071118
+ - Score on test : 0.696245733788
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.693759071118
+ - Score on test : 0.696245733788
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.27545691906
+ - Score on test : 0.273006134969
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.72454308094
+ - Score on test : 0.726993865031
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.458446665055
+ - Score on test : 0.463587810205
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.781045751634
+ - Score on test : 0.784615384615
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.624020887728
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.72454308094
+ - Score on test : 0.726993865031
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.27545691906
+ - Score on test : 0.273006134969
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150751-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-150751-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..6940dd20
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150751-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,2095 @@
+2017-09-22 15:07:57,293 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2017-09-22 15:07:57,293 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.08002225 Gbytes /!\
+2017-09-22 15:08:00,633 DEBUG: Start: Creating datasets for multiprocessing
+2017-09-22 15:08:00,636 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 15:08:00,708 DEBUG: Start: Loading data
+2017-09-22 15:08:00,708 DEBUG: Start: Loading data
+2017-09-22 15:08:00,727 DEBUG: Done: Loading data
+2017-09-22 15:08:00,727 DEBUG: Done: Loading data
+2017-09-22 15:08:00,727 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 15:08:00,727 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 15:08:00,727 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:00,727 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:00,753 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:08:00,753 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:08:00,753 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:00,753 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 15:08:00,758 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:08:00,758 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:08:00,758 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:00,758 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 15:08:02,439 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:02,439 DEBUG: Start: Training
+2017-09-22 15:08:02,938 DEBUG: Done: Training
+2017-09-22 15:08:02,939 DEBUG: Start: Predicting
+2017-09-22 15:08:02,952 DEBUG: Done: Predicting
+2017-09-22 15:08:02,952 DEBUG: Info: Time for training and predicting: 2.24309301376[s]
+2017-09-22 15:08:02,952 DEBUG: Start: Getting Results
+2017-09-22 15:08:02,980 DEBUG: Done: Getting Results
+2017-09-22 15:08:02,980 INFO: Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.891644908616, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.891644908616
+ - Score on test : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.890932982917
+ - Score on test : 0.658385093168
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.890932982917
+ - Score on test : 0.658385093168
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.108355091384
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.891644908616
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.783356573256
+ - Score on test : 0.325251323062
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.896825396825
+ - Score on test : 0.666666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.885117493473
+ - Score on test : 0.650306748466
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.891644908616
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.108355091384
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:02
+2017-09-22 15:08:02,980 INFO: Done: Result Analysis
+2017-09-22 15:08:03,017 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:03,017 DEBUG: Start: Training
+2017-09-22 15:08:03,899 DEBUG: Done: Training
+2017-09-22 15:08:03,899 DEBUG: Start: Predicting
+2017-09-22 15:08:03,916 DEBUG: Done: Predicting
+2017-09-22 15:08:03,916 DEBUG: Info: Time for training and predicting: 3.20808506012[s]
+2017-09-22 15:08:03,916 DEBUG: Start: Getting Results
+2017-09-22 15:08:03,946 DEBUG: Done: Getting Results
+2017-09-22 15:08:03,946 INFO: Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.658536585366
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.658536585366
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.312906990761
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.654545454545
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:00:03
+2017-09-22 15:08:03,946 INFO: Done: Result Analysis
+2017-09-22 15:08:04,069 DEBUG: Start: Loading data
+2017-09-22 15:08:04,069 DEBUG: Start: Loading data
+2017-09-22 15:08:04,090 DEBUG: Done: Loading data
+2017-09-22 15:08:04,090 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 15:08:04,090 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:04,090 DEBUG: Done: Loading data
+2017-09-22 15:08:04,091 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 15:08:04,091 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:04,124 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:08:04,125 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:08:04,125 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:04,125 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:08:04,125 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 15:08:04,125 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:08:04,125 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:04,125 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 15:08:04,865 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:04,865 DEBUG: Start: Training
+2017-09-22 15:08:05,151 DEBUG: Done: Training
+2017-09-22 15:08:05,152 DEBUG: Start: Predicting
+2017-09-22 15:08:05,218 DEBUG: Done: Predicting
+2017-09-22 15:08:05,218 DEBUG: Info: Time for training and predicting: 1.14848995209[s]
+2017-09-22 15:08:05,218 DEBUG: Start: Getting Results
+2017-09-22 15:08:05,261 DEBUG: Done: Getting Results
+2017-09-22 15:08:05,262 INFO: Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.993472584856, with STD : 0.0
+accuracy_score on test : 0.693251533742, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 13, 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 : 0.993472584856
+ - Score on test : 0.693251533742
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.993481095176
+ - Score on test : 0.685534591195
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.993481095176
+ - Score on test : 0.685534591195
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.306748466258
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.693251533742
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.986948533804
+ - Score on test : 0.386969418778
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.9921875
+ - Score on test : 0.703225806452
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.994778067885
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.693251533742
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.306748466258
+
+
+ Classification took 0:00:01
+2017-09-22 15:08:05,262 INFO: Done: Result Analysis
+2017-09-22 15:08:07,198 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:07,198 DEBUG: Start: Training
+2017-09-22 15:08:07,261 DEBUG: Done: Training
+2017-09-22 15:08:07,261 DEBUG: Start: Predicting
+2017-09-22 15:08:14,703 DEBUG: Done: Predicting
+2017-09-22 15:08:14,703 DEBUG: Info: Time for training and predicting: 10.6334049702[s]
+2017-09-22 15:08:14,703 DEBUG: Start: Getting Results
+2017-09-22 15:08:14,730 DEBUG: Done: Getting Results
+2017-09-22 15:08:14,730 INFO: Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.661879895561, with STD : 0.0
+accuracy_score on test : 0.610429447853, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 31
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.610429447853
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.715071507151
+ - Score on test : 0.661333333333
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.715071507151
+ - Score on test : 0.661333333333
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.338120104439
+ - Score on test : 0.389570552147
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.610429447853
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.348998204171
+ - Score on test : 0.231569919477
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.617870722433
+ - Score on test : 0.584905660377
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.848563968668
+ - Score on test : 0.760736196319
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.610429447853
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.338120104439
+ - Score on test : 0.389570552147
+
+
+ Classification took 0:00:10
+2017-09-22 15:08:14,730 INFO: Done: Result Analysis
+2017-09-22 15:08:14,856 DEBUG: Start: Loading data
+2017-09-22 15:08:14,857 DEBUG: Start: Loading data
+2017-09-22 15:08:14,870 DEBUG: Done: Loading data
+2017-09-22 15:08:14,870 DEBUG: Done: Loading data
+2017-09-22 15:08:14,870 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 15:08:14,871 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 15:08:14,871 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:14,871 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:14,896 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:08:14,897 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:08:14,897 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:14,897 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 15:08:14,899 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:08:14,899 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:08:14,899 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:14,899 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 15:08:15,451 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:15,451 DEBUG: Start: Training
+2017-09-22 15:08:15,614 DEBUG: Done: Training
+2017-09-22 15:08:15,615 DEBUG: Start: Predicting
+2017-09-22 15:08:15,628 DEBUG: Done: Predicting
+2017-09-22 15:08:15,628 DEBUG: Info: Time for training and predicting: 0.771407842636[s]
+2017-09-22 15:08:15,628 DEBUG: Start: Getting Results
+2017-09-22 15:08:15,671 DEBUG: Done: Getting Results
+2017-09-22 15:08:15,671 INFO: Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.5, with STD : 0.0
+accuracy_score on test : 0.5, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.666666666667
+ - Score on test : 0.666666666667
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.666666666667
+ - Score on test : 0.666666666667
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0
+ - Score on test : 0.0
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 1.0
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
+2017-09-22 15:08:15,672 INFO: Done: Result Analysis
+2017-09-22 15:08:18,394 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:18,394 DEBUG: Start: Training
+2017-09-22 15:08:24,042 DEBUG: Done: Training
+2017-09-22 15:08:24,042 DEBUG: Start: Predicting
+2017-09-22 15:08:27,009 DEBUG: Done: Predicting
+2017-09-22 15:08:27,009 DEBUG: Info: Time for training and predicting: 12.1518828869[s]
+2017-09-22 15:08:27,009 DEBUG: Start: Getting Results
+2017-09-22 15:08:27,036 DEBUG: Done: Getting Results
+2017-09-22 15:08:27,036 INFO: Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 2527
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.68023255814
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.68023255814
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.327154260952
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.646408839779
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.717791411043
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:12
+2017-09-22 15:08:27,036 INFO: Done: Result Analysis
+2017-09-22 15:08:27,157 DEBUG: Start: Loading data
+2017-09-22 15:08:27,158 DEBUG: Start: Loading data
+2017-09-22 15:08:27,173 DEBUG: Done: Loading data
+2017-09-22 15:08:27,173 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 15:08:27,173 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:27,173 DEBUG: Done: Loading data
+2017-09-22 15:08:27,174 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 15:08:27,174 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:27,198 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:08:27,198 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:08:27,198 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:27,199 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 15:08:27,199 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:08:27,199 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:08:27,199 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:27,199 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 15:08:32,137 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:32,138 DEBUG: Start: Training
+2017-09-22 15:08:33,190 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:33,190 DEBUG: Start: Training
+2017-09-22 15:08:40,811 DEBUG: Done: Training
+2017-09-22 15:08:40,811 DEBUG: Start: Predicting
+2017-09-22 15:08:43,216 DEBUG: Done: Training
+2017-09-22 15:08:43,216 DEBUG: Start: Predicting
+2017-09-22 15:08:45,312 DEBUG: Done: Predicting
+2017-09-22 15:08:45,312 DEBUG: Info: Time for training and predicting: 18.1537029743[s]
+2017-09-22 15:08:45,312 DEBUG: Start: Getting Results
+2017-09-22 15:08:45,341 DEBUG: Done: Getting Results
+2017-09-22 15:08:45,341 INFO: Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.835509138381, with STD : 0.0
+accuracy_score on test : 0.720858895706, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.835509138381
+ - Score on test : 0.720858895706
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.831550802139
+ - Score on test : 0.723404255319
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.831550802139
+ - Score on test : 0.723404255319
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.164490861619
+ - Score on test : 0.279141104294
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.835509138381
+ - Score on test : 0.720858895706
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.671760563981
+ - Score on test : 0.441792624306
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.852054794521
+ - Score on test : 0.71686746988
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.812010443864
+ - Score on test : 0.730061349693
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.835509138381
+ - Score on test : 0.720858895706
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.164490861619
+ - Score on test : 0.279141104294
+
+
+ Classification took 0:00:18
+2017-09-22 15:08:45,342 INFO: Done: Result Analysis
+2017-09-22 15:08:48,580 DEBUG: Done: Predicting
+2017-09-22 15:08:48,580 DEBUG: Info: Time for training and predicting: 21.422506094[s]
+2017-09-22 15:08:48,580 DEBUG: Start: Getting Results
+2017-09-22 15:08:48,608 DEBUG: Done: Getting Results
+2017-09-22 15:08:48,608 INFO: Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.93864229765, with STD : 0.0
+accuracy_score on test : 0.539877300613, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 8352
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.93864229765
+ - Score on test : 0.539877300613
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.93657219973
+ - Score on test : 0.385245901639
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.93657219973
+ - Score on test : 0.385245901639
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0613577023499
+ - Score on test : 0.460122699387
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.93864229765
+ - Score on test : 0.539877300613
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.879159518567
+ - Score on test : 0.0922821705
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.969273743017
+ - Score on test : 0.58024691358
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.906005221932
+ - Score on test : 0.288343558282
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.93864229765
+ - Score on test : 0.539877300613
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0613577023499
+ - Score on test : 0.460122699387
+
+
+ Classification took 0:00:21
+2017-09-22 15:08:48,608 INFO: Done: Result Analysis
+2017-09-22 15:08:48,770 DEBUG: Start: Loading data
+2017-09-22 15:08:48,770 DEBUG: Start: Loading data
+2017-09-22 15:08:48,782 DEBUG: Done: Loading data
+2017-09-22 15:08:48,782 DEBUG: Done: Loading data
+2017-09-22 15:08:48,782 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 15:08:48,782 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 15:08:48,782 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:48,782 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:48,803 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:08:48,803 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:08:48,803 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:48,803 DEBUG: Start: RandomSearch best settings with 2 iterations for DecisionTree
+2017-09-22 15:08:48,804 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:08:48,804 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:08:48,804 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:48,804 DEBUG: Start: RandomSearch best settings with 2 iterations for Adaboost
+2017-09-22 15:08:49,867 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:49,867 DEBUG: Start: Training
+2017-09-22 15:08:50,106 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:50,106 DEBUG: Start: Training
+2017-09-22 15:08:50,150 DEBUG: Done: Training
+2017-09-22 15:08:50,150 DEBUG: Start: Predicting
+2017-09-22 15:08:50,160 DEBUG: Done: Predicting
+2017-09-22 15:08:50,160 DEBUG: Info: Time for training and predicting: 1.3892531395[s]
+2017-09-22 15:08:50,160 DEBUG: Start: Getting Results
+2017-09-22 15:08:50,188 DEBUG: Done: Getting Results
+2017-09-22 15:08:50,188 INFO: Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.881201044386, with STD : 0.0
+accuracy_score on test : 0.736196319018, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.881201044386
+ - Score on test : 0.736196319018
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.873082287308
+ - Score on test : 0.727848101266
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.873082287308
+ - Score on test : 0.727848101266
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.118798955614
+ - Score on test : 0.263803680982
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.881201044386
+ - Score on test : 0.736196319018
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.768719228721
+ - Score on test : 0.473284147545
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.937125748503
+ - Score on test : 0.751633986928
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.817232375979
+ - Score on test : 0.705521472393
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.881201044386
+ - Score on test : 0.736196319018
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.118798955614
+ - Score on test : 0.263803680982
+
+
+ Classification took 0:00:01
+2017-09-22 15:08:50,189 INFO: Done: Result Analysis
+2017-09-22 15:08:50,541 DEBUG: Done: Training
+2017-09-22 15:08:50,542 DEBUG: Start: Predicting
+2017-09-22 15:08:50,554 DEBUG: Done: Predicting
+2017-09-22 15:08:50,554 DEBUG: Info: Time for training and predicting: 1.78375315666[s]
+2017-09-22 15:08:50,554 DEBUG: Start: Getting Results
+2017-09-22 15:08:50,582 DEBUG: Done: Getting Results
+2017-09-22 15:08:50,582 INFO: Classification on awaexp database for lss-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.671779141104, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.671779141104
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662460567823
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662460567823
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.328220858896
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.671779141104
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.344083179874
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.681818181818
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.644171779141
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.671779141104
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.328220858896
+
+
+ Classification took 0:00:01
+2017-09-22 15:08:50,582 INFO: Done: Result Analysis
+2017-09-22 15:08:50,738 DEBUG: Start: Loading data
+2017-09-22 15:08:50,739 DEBUG: Start: Loading data
+2017-09-22 15:08:50,750 DEBUG: Done: Loading data
+2017-09-22 15:08:50,750 DEBUG: Done: Loading data
+2017-09-22 15:08:50,750 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 15:08:50,751 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:50,751 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 15:08:50,751 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:50,771 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:08:50,771 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:08:50,771 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:08:50,771 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:08:50,771 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:50,772 DEBUG: Start: RandomSearch best settings with 2 iterations for RandomForest
+2017-09-22 15:08:50,772 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:50,772 DEBUG: Start: RandomSearch best settings with 2 iterations for KNN
+2017-09-22 15:08:51,207 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:51,207 DEBUG: Start: Training
+2017-09-22 15:08:51,362 DEBUG: Done: Training
+2017-09-22 15:08:51,362 DEBUG: Start: Predicting
+2017-09-22 15:08:51,407 DEBUG: Done: Predicting
+2017-09-22 15:08:51,407 DEBUG: Info: Time for training and predicting: 0.66797709465[s]
+2017-09-22 15:08:51,407 DEBUG: Start: Getting Results
+2017-09-22 15:08:51,436 DEBUG: Done: Getting Results
+2017-09-22 15:08:51,436 INFO: Classification on awaexp database for lss-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.996083550914, with STD : 0.0
+accuracy_score on test : 0.71472392638, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 13, 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 : 0.996083550914
+ - Score on test : 0.71472392638
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.996068152031
+ - Score on test : 0.710280373832
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.996068152031
+ - Score on test : 0.710280373832
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00391644908616
+ - Score on test : 0.28527607362
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.996083550914
+ - Score on test : 0.71472392638
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.992197540084
+ - Score on test : 0.429650039123
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.721518987342
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.699386503067
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.996083550914
+ - Score on test : 0.71472392638
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00391644908616
+ - Score on test : 0.28527607362
+
+
+ Classification took 0:00:00
+2017-09-22 15:08:51,437 INFO: Done: Result Analysis
+2017-09-22 15:08:52,686 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:52,686 DEBUG: Start: Training
+2017-09-22 15:08:52,725 DEBUG: Done: Training
+2017-09-22 15:08:52,725 DEBUG: Start: Predicting
+2017-09-22 15:08:58,030 DEBUG: Done: Predicting
+2017-09-22 15:08:58,030 DEBUG: Info: Time for training and predicting: 7.29149198532[s]
+2017-09-22 15:08:58,030 DEBUG: Start: Getting Results
+2017-09-22 15:08:58,057 DEBUG: Done: Getting Results
+2017-09-22 15:08:58,057 INFO: Classification on awaexp database for lss-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.674934725849, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 45
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.674934725849
+ - Score on test : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.627802690583
+ - Score on test : 0.625407166124
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.627802690583
+ - Score on test : 0.625407166124
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.325065274151
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.674934725849
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.361660570561
+ - Score on test : 0.296499726664
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.734265734266
+ - Score on test : 0.666666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.548302872063
+ - Score on test : 0.588957055215
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.674934725849
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.325065274151
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:07
+2017-09-22 15:08:58,057 INFO: Done: Result Analysis
+2017-09-22 15:08:58,213 DEBUG: Start: Loading data
+2017-09-22 15:08:58,213 DEBUG: Start: Loading data
+2017-09-22 15:08:58,226 DEBUG: Done: Loading data
+2017-09-22 15:08:58,226 DEBUG: Done: Loading data
+2017-09-22 15:08:58,226 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 15:08:58,226 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 15:08:58,226 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:58,226 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:08:58,247 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:08:58,247 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:08:58,247 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:58,247 DEBUG: Start: RandomSearch best settings with 2 iterations for SGD
+2017-09-22 15:08:58,248 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:08:58,248 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:08:58,248 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:08:58,248 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMLinear
+2017-09-22 15:08:58,785 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:08:58,786 DEBUG: Start: Training
+2017-09-22 15:08:58,946 DEBUG: Done: Training
+2017-09-22 15:08:58,946 DEBUG: Start: Predicting
+2017-09-22 15:08:58,955 DEBUG: Done: Predicting
+2017-09-22 15:08:58,955 DEBUG: Info: Time for training and predicting: 0.741857051849[s]
+2017-09-22 15:08:58,955 DEBUG: Start: Getting Results
+2017-09-22 15:08:59,005 DEBUG: Done: Getting Results
+2017-09-22 15:08:59,005 INFO: Classification on awaexp database for lss-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.8772845953, with STD : 0.0
+accuracy_score on test : 0.711656441718, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.8772845953
+ - Score on test : 0.711656441718
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.887290167866
+ - Score on test : 0.744565217391
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.887290167866
+ - Score on test : 0.744565217391
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.1227154047
+ - Score on test : 0.288343558282
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.8772845953
+ - Score on test : 0.711656441718
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.766750900883
+ - Score on test : 0.438106276437
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.820399113082
+ - Score on test : 0.668292682927
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.966057441253
+ - Score on test : 0.840490797546
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.8772845953
+ - Score on test : 0.711656441718
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.1227154047
+ - Score on test : 0.288343558282
+
+
+ Classification took 0:00:00
+2017-09-22 15:08:59,005 INFO: Done: Result Analysis
+2017-09-22 15:09:00,848 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:09:00,848 DEBUG: Start: Training
+2017-09-22 15:09:04,812 DEBUG: Done: Training
+2017-09-22 15:09:04,813 DEBUG: Start: Predicting
+2017-09-22 15:09:06,645 DEBUG: Done: Predicting
+2017-09-22 15:09:06,645 DEBUG: Info: Time for training and predicting: 8.4313750267[s]
+2017-09-22 15:09:06,645 DEBUG: Start: Getting Results
+2017-09-22 15:09:06,673 DEBUG: Done: Getting Results
+2017-09-22 15:09:06,674 INFO: Classification on awaexp database for lss-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.779141104294, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 2527
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.77358490566
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.77358490566
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.220858895706
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.558955827124
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.793548387097
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.754601226994
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.220858895706
+
+
+ Classification took 0:00:08
+2017-09-22 15:09:06,674 INFO: Done: Result Analysis
+2017-09-22 15:09:06,790 DEBUG: Start: Loading data
+2017-09-22 15:09:06,790 DEBUG: Start: Loading data
+2017-09-22 15:09:06,804 DEBUG: Done: Loading data
+2017-09-22 15:09:06,804 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 15:09:06,804 DEBUG: Done: Loading data
+2017-09-22 15:09:06,804 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:09:06,804 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 15:09:06,804 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:09:06,824 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:09:06,825 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:09:06,825 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:09:06,825 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMRBF
+2017-09-22 15:09:06,827 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:09:06,827 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:09:06,827 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:09:06,827 DEBUG: Start: RandomSearch best settings with 2 iterations for SVMPoly
+2017-09-22 15:09:09,053 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:09:09,053 DEBUG: Start: Training
+2017-09-22 15:09:11,190 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:09:11,191 DEBUG: Start: Training
+2017-09-22 15:09:13,006 DEBUG: Done: Training
+2017-09-22 15:09:13,006 DEBUG: Start: Predicting
+2017-09-22 15:09:14,995 DEBUG: Done: Predicting
+2017-09-22 15:09:14,995 DEBUG: Info: Time for training and predicting: 8.20430922508[s]
+2017-09-22 15:09:14,995 DEBUG: Start: Getting Results
+2017-09-22 15:09:15,027 DEBUG: Done: Getting Results
+2017-09-22 15:09:15,027 INFO: Classification on awaexp database for lss-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.513054830287, with STD : 0.0
+accuracy_score on test : 0.407975460123, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 8352
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.513054830287
+ - Score on test : 0.407975460123
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.504648074369
+ - Score on test : 0.398753894081
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.504648074369
+ - Score on test : 0.398753894081
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.486945169713
+ - Score on test : 0.592024539877
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.513054830287
+ - Score on test : 0.407975460123
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0261247140176
+ - Score on test : -0.184135731053
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.513513513514
+ - Score on test : 0.405063291139
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.496083550914
+ - Score on test : 0.39263803681
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.513054830287
+ - Score on test : 0.407975460123
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.486945169713
+ - Score on test : 0.592024539877
+
+
+ Classification took 0:00:08
+2017-09-22 15:09:15,027 INFO: Done: Result Analysis
+2017-09-22 15:09:18,175 DEBUG: Done: Training
+2017-09-22 15:09:18,175 DEBUG: Start: Predicting
+2017-09-22 15:09:21,715 DEBUG: Done: Predicting
+2017-09-22 15:09:21,715 DEBUG: Info: Time for training and predicting: 14.9248487949[s]
+2017-09-22 15:09:21,716 DEBUG: Start: Getting Results
+2017-09-22 15:09:21,742 DEBUG: Done: Getting Results
+2017-09-22 15:09:21,742 INFO: Classification on awaexp database for lss-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.530674846626, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 2527
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.530674846626
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670967741935
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670967741935
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.469325153374
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.530674846626
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.117460246434
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.516556291391
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.957055214724
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.530674846626
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.469325153374
+
+
+ Classification took 0:00:14
+2017-09-22 15:09:21,742 INFO: Done: Result Analysis
+2017-09-22 15:09:21,885 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:09:21,886 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:09:21,886 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:09:21,887 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:09:21,887 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:09:21,888 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:09:21,888 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:09:21,888 INFO: Done: Read Database Files
+2017-09-22 15:09:21,888 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:09:21,889 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:09:21,889 INFO: Done: Read Database Files
+2017-09-22 15:09:21,889 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:09:21,911 INFO: Done: Determine validation split
+2017-09-22 15:09:21,912 INFO: Start: Determine 2 folds
+2017-09-22 15:09:21,914 INFO: Done: Determine validation split
+2017-09-22 15:09:21,914 INFO: Start: Determine 2 folds
+2017-09-22 15:09:25,241 INFO: Done: Classification
+2017-09-22 15:09:25,541 INFO: Done: Classification
+2017-09-22 15:09:25,841 INFO: Done: Classification
+2017-09-22 15:09:25,842 INFO: Info: Time for Classification: 3[s]
+2017-09-22 15:09:25,842 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:09:26,111 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.926240208877
+ -On Test : 0.774539877301
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.441046660592, 0.558953339408
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l1
+ - SGDClassifier with loss : log, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.926240208877 with STD : 0.00456919060052
+ - Score on test : 0.774539877301 with STD : 0.00153374233129
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.927666429118 with STD : 0.00323317974772
+ - Score on test : 0.776252872711 with STD : 0.00186262880866
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.927666429118 with STD : 0.00323317974772
+ - Score on test : 0.776252872711 with STD : 0.00186262880866
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0737597911227 with STD : 0.00456919060052
+ - Score on test : 0.225460122699 with STD : 0.00153374233129
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.926240208877 with STD : 0.00456919060052
+ - Score on test : 0.774539877301 with STD : 0.00153374233129
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.853613263379 with STD : 0.00800847248104
+ - Score on test : 0.549147078209 with STD : 0.00309370217429
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.911315769465 with STD : 0.0183717305353
+ - Score on test : 0.770390653523 with STD : 0.000693683826214
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.945169712794 with STD : 0.0130548302872
+ - Score on test : 0.782208588957 with STD : 0.00306748466258
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.926240208877 with STD : 0.00456919060052
+ - Score on test : 0.774539877301 with STD : 0.00153374233129
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0737597911227 with STD : 0.00456919060052
+ - Score on test : 0.225460122699 with STD : 0.00153374233129
+
+
+2017-09-22 15:09:26,113 INFO: Done: Result Analysis
+2017-09-22 15:09:26,222 INFO: Done: Classification
+2017-09-22 15:09:26,222 INFO: Info: Time for Classification: 4[s]
+2017-09-22 15:09:26,222 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:09:26,400 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.916449086162
+ -On Test : 0.751533742331
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Majority Voting
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l1
+ - SGDClassifier with loss : log, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.916449086162 with STD : 0.0117493472585
+ - Score on test : 0.751533742331 with STD : 0.0153374233129
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.915544159287 with STD : 0.0127478876491
+ - Score on test : 0.732724817009 with STD : 0.0156195538515
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.915544159287 with STD : 0.0127478876491
+ - Score on test : 0.732724817009 with STD : 0.0156195538515
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0835509138381 with STD : 0.0117493472585
+ - Score on test : 0.248466257669 with STD : 0.0153374233129
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.916449086162 with STD : 0.0117493472585
+ - Score on test : 0.751533742331 with STD : 0.0153374233129
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.833210365221 with STD : 0.023189420725
+ - Score on test : 0.508189397466 with STD : 0.0314343445442
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.924139492754 with STD : 0.00294384057971
+ - Score on test : 0.792999642839 with STD : 0.0199499974488
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.907310704961 with STD : 0.0221932114883
+ - Score on test : 0.680981595092 with STD : 0.0122699386503
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.916449086162 with STD : 0.0117493472585
+ - Score on test : 0.751533742331 with STD : 0.0153374233129
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0835509138381 with STD : 0.0117493472585
+ - Score on test : 0.248466257669 with STD : 0.0153374233129
+
+
+2017-09-22 15:09:26,400 INFO: Done: Result Analysis
+2017-09-22 15:09:26,539 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:09:26,540 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:09:26,540 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:09:26,540 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:09:26,541 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:09:26,541 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:09:26,542 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:09:26,542 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:09:26,542 INFO: Done: Read Database Files
+2017-09-22 15:09:26,542 INFO: Done: Read Database Files
+2017-09-22 15:09:26,542 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:09:26,542 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:09:26,584 INFO: Done: Determine validation split
+2017-09-22 15:09:26,584 INFO: Start: Determine 2 folds
+2017-09-22 15:09:26,584 INFO: Done: Determine validation split
+2017-09-22 15:09:26,584 INFO: Start: Determine 2 folds
+2017-09-22 15:09:30,774 INFO: Done: Classification
+2017-09-22 15:09:31,496 INFO: Done: Classification
+2017-09-22 15:09:31,496 INFO: Info: Time for Classification: 4[s]
+2017-09-22 15:09:31,496 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:09:31,622 INFO: Done: Classification
+2017-09-22 15:09:31,788 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.848563968668
+ -On Test : 0.739263803681
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with SVM for linear
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l1
+ - SGDClassifier with loss : log, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.848563968668 with STD : 0.0
+ - Score on test : 0.739263803681 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.865740740741 with STD : 0.0
+ - Score on test : 0.768392370572 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.865740740741 with STD : 0.0
+ - Score on test : 0.768392370572 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.151436031332 with STD : 0.0
+ - Score on test : 0.260736196319 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.848563968668 with STD : 0.0
+ - Score on test : 0.739263803681 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.72113453306 with STD : 0.0
+ - Score on test : 0.494424068096 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.777546777547 with STD : 0.0
+ - Score on test : 0.691176470588 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.976501305483 with STD : 0.0
+ - Score on test : 0.865030674847 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.848563968668 with STD : 0.0
+ - Score on test : 0.739263803681 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.151436031332 with STD : 0.0
+ - Score on test : 0.260736196319 with STD : 0.0
+
+
+2017-09-22 15:09:31,793 INFO: Done: Result Analysis
+2017-09-22 15:09:32,441 INFO: Done: Classification
+2017-09-22 15:09:32,441 INFO: Info: Time for Classification: 5[s]
+2017-09-22 15:09:32,442 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:09:32,617 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.946475195822
+ -On Test : 0.763803680982
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with SCM for linear with max_attributes : 17, p : 0.153950583132 model_type : conjunction has chosen 1 rule(s)
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l1
+ - SGDClassifier with loss : log, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822 with STD : 0.0
+ - Score on test : 0.763803680982 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.948685857322 with STD : 0.0
+ - Score on test : 0.772861356932 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.948685857322 with STD : 0.0
+ - Score on test : 0.772861356932 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0535248041775 with STD : 0.0
+ - Score on test : 0.236196319018 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822 with STD : 0.0
+ - Score on test : 0.763803680982 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.896283535397 with STD : 0.0
+ - Score on test : 0.529293411211 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.911057692308 with STD : 0.0
+ - Score on test : 0.744318181818 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.98955613577 with STD : 0.0
+ - Score on test : 0.803680981595 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.946475195822 with STD : 0.0
+ - Score on test : 0.763803680982 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0535248041775 with STD : 0.0
+ - Score on test : 0.236196319018 with STD : 0.0
+
+
+2017-09-22 15:09:32,618 INFO: Done: Result Analysis
+2017-09-22 15:09:32,730 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:09:32,730 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:09:32,730 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:09:32,730 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:09:32,732 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:09:32,732 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:09:32,733 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:09:32,733 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:09:32,733 INFO: Done: Read Database Files
+2017-09-22 15:09:32,733 INFO: Done: Read Database Files
+2017-09-22 15:09:32,733 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:09:32,733 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:09:32,773 INFO: Done: Determine validation split
+2017-09-22 15:09:32,773 INFO: Done: Determine validation split
+2017-09-22 15:09:32,773 INFO: Start: Determine 2 folds
+2017-09-22 15:09:32,773 INFO: Start: Determine 2 folds
+2017-09-22 15:09:36,126 INFO: Done: Classification
+2017-09-22 15:09:36,734 INFO: Done: Classification
+2017-09-22 15:09:36,734 INFO: Info: Time for Classification: 4[s]
+2017-09-22 15:09:36,734 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:09:37,017 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.5
+ -On Test : 0.5
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Weighted linear using a weight for each view : 0.789058101091, 1.0
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l1
+ - SGDClassifier with loss : log, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.666666666667 with STD : 0.0
+ - Score on test : 0.666666666667 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.666666666667 with STD : 0.0
+ - Score on test : 0.666666666667 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 1.0 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+
+2017-09-22 15:09:37,017 INFO: Done: Result Analysis
+2017-09-22 15:09:39,975 INFO: Done: Classification
+2017-09-22 15:09:41,416 INFO: Done: Classification
+2017-09-22 15:09:41,416 INFO: Info: Time for Classification: 8[s]
+2017-09-22 15:09:41,416 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:09:41,568 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.716257668712
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.716257668712 with STD : 0.0138036809816
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.710462382445 with STD : 0.0145376175549
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.710462382445 with STD : 0.0145376175549
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.283742331288 with STD : 0.0138036809816
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.716257668712 with STD : 0.0138036809816
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.43285830522 with STD : 0.027576435819
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.725195982362 with STD : 0.0136575208231
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.696319018405 with STD : 0.0153374233129
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.716257668712 with STD : 0.0138036809816
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.283742331288 with STD : 0.0138036809816
+
+
+2017-09-22 15:09:41,569 INFO: Done: Result Analysis
+2017-09-22 15:09:41,711 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:09:41,711 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:09:41,712 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:09:41,713 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:09:41,714 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:09:41,715 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:09:41,715 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:09:41,716 INFO: Done: Read Database Files
+2017-09-22 15:09:41,716 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:09:41,716 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:09:41,716 INFO: Done: Read Database Files
+2017-09-22 15:09:41,716 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:09:41,756 INFO: Done: Determine validation split
+2017-09-22 15:09:41,756 INFO: Start: Determine 2 folds
+2017-09-22 15:09:41,757 INFO: Done: Determine validation split
+2017-09-22 15:09:41,757 INFO: Start: Determine 2 folds
+2017-09-22 15:09:47,210 INFO: Done: Classification
+2017-09-22 15:09:48,077 INFO: Done: Classification
+2017-09-22 15:09:48,077 INFO: Info: Time for Classification: 6[s]
+2017-09-22 15:09:48,077 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:09:48,243 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.81592689295
+ -On Test : 0.742331288344
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - Decision Tree with max_depth : 3
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.742331288344 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.80971659919 with STD : 0.0
+ - Score on test : 0.727272727273 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.80971659919 with STD : 0.0
+ - Score on test : 0.727272727273 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.18407310705 with STD : 0.0
+ - Score on test : 0.257668711656 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.742331288344 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.633204177063 with STD : 0.0
+ - Score on test : 0.487645030476 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.837988826816 with STD : 0.0
+ - Score on test : 0.772413793103 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.783289817232 with STD : 0.0
+ - Score on test : 0.687116564417 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.742331288344 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.18407310705 with STD : 0.0
+ - Score on test : 0.257668711656 with STD : 0.0
+
+
+2017-09-22 15:09:48,243 INFO: Done: Result Analysis
+2017-09-22 15:10:01,353 INFO: Done: Classification
+2017-09-22 15:10:06,701 INFO: Done: Classification
+2017-09-22 15:10:06,702 INFO: Info: Time for Classification: 24[s]
+2017-09-22 15:10:06,702 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:10:06,854 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.613496932515
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - K nearest Neighbors with n_neighbors: 1.0
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.613496932515 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.590909090909 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.590909090909 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.386503067485 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.613496932515 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.228390710476 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.627586206897 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.558282208589 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.613496932515 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.386503067485 with STD : 0.0
+
+
+2017-09-22 15:10:06,854 INFO: Done: Result Analysis
+2017-09-22 15:10:07,017 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:10:07,017 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:10:07,017 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:10:07,017 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:10:07,020 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:10:07,020 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:10:07,021 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:10:07,021 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:10:07,022 INFO: Done: Read Database Files
+2017-09-22 15:10:07,022 INFO: Done: Read Database Files
+2017-09-22 15:10:07,022 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:10:07,022 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:10:07,061 INFO: Done: Determine validation split
+2017-09-22 15:10:07,061 INFO: Start: Determine 2 folds
+2017-09-22 15:10:07,061 INFO: Done: Determine validation split
+2017-09-22 15:10:07,061 INFO: Start: Determine 2 folds
+2017-09-22 15:10:10,817 INFO: Done: Classification
+2017-09-22 15:10:10,958 INFO: Done: Classification
+2017-09-22 15:10:11,574 INFO: Done: Classification
+2017-09-22 15:10:11,574 INFO: Info: Time for Classification: 4[s]
+2017-09-22 15:10:11,574 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:10:11,657 INFO: Done: Classification
+2017-09-22 15:10:11,657 INFO: Info: Time for Classification: 4[s]
+2017-09-22 15:10:11,657 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:10:11,794 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.5
+ -On Test : 0.5
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - SCM with max_attributes : 1
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+
+2017-09-22 15:10:11,794 INFO: Done: Result Analysis
+2017-09-22 15:10:11,831 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.92362924282
+ -On Test : 0.75
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - Random Forest with num_esimators : 25, max_depth : 5
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.92362924282 with STD : 0.0032637075718
+ - Score on test : 0.75 with STD : 0.0138036809816
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.920665563523 with STD : 0.00365876080162
+ - Score on test : 0.74323439546 with STD : 0.0153862941947
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.920665563523 with STD : 0.00365876080162
+ - Score on test : 0.74323439546 with STD : 0.0153862941947
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0763707571802 with STD : 0.0032637075718
+ - Score on test : 0.25 with STD : 0.0138036809816
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.92362924282 with STD : 0.0032637075718
+ - Score on test : 0.75 with STD : 0.0138036809816
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.849629310887 with STD : 0.00613070449175
+ - Score on test : 0.500689352322 with STD : 0.0274052047766
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.957684778457 with STD : 0.000298414820474
+ - Score on test : 0.763637506285 with STD : 0.0120035193565
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.886422976501 with STD : 0.0065274151436
+ - Score on test : 0.723926380368 with STD : 0.0184049079755
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.92362924282 with STD : 0.0032637075718
+ - Score on test : 0.75 with STD : 0.0138036809816
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0763707571802 with STD : 0.0032637075718
+ - Score on test : 0.25 with STD : 0.0138036809816
+
+
+2017-09-22 15:10:11,831 INFO: Done: Result Analysis
+2017-09-22 15:10:11,994 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:10:11,994 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:10:11,994 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:10:11,994 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:10:11,996 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:10:11,996 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:10:11,996 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:10:11,996 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:10:11,997 INFO: Done: Read Database Files
+2017-09-22 15:10:11,997 INFO: Done: Read Database Files
+2017-09-22 15:10:11,997 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:10:11,997 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:10:12,039 INFO: Done: Determine validation split
+2017-09-22 15:10:12,039 INFO: Done: Determine validation split
+2017-09-22 15:10:12,040 INFO: Start: Determine 2 folds
+2017-09-22 15:10:12,040 INFO: Start: Determine 2 folds
+2017-09-22 15:10:14,368 INFO: Done: Classification
+2017-09-22 15:10:14,889 INFO: Done: Classification
+2017-09-22 15:10:14,889 INFO: Info: Time for Classification: 2[s]
+2017-09-22 15:10:14,889 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:10:15,167 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.885770234987
+ -On Test : 0.693251533742
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - SGDClassifier with loss : log, penalty : l2
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.885770234987 with STD : 0.0319843342037
+ - Score on test : 0.693251533742 with STD : 0.021472392638
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.875370404976 with STD : 0.0455831709336
+ - Score on test : 0.672814010822 with STD : 0.0291358499023
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.875370404976 with STD : 0.0455831709336
+ - Score on test : 0.672814010822 with STD : 0.0291358499023
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.114229765013 with STD : 0.0319843342037
+ - Score on test : 0.306748466258 with STD : 0.021472392638
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.885770234987 with STD : 0.0319843342037
+ - Score on test : 0.693251533742 with STD : 0.021472392638
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.787879889409 with STD : 0.0503795952072
+ - Score on test : 0.409558056757 with STD : 0.058734849034
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.939600351339 with STD : 0.0531269213878
+ - Score on test : 0.75 with STD : 0.107142857143
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.835509138381 with STD : 0.1227154047
+ - Score on test : 0.644171779141 with STD : 0.128834355828
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.885770234987 with STD : 0.0319843342037
+ - Score on test : 0.693251533742 with STD : 0.021472392638
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.114229765013 with STD : 0.0319843342037
+ - Score on test : 0.306748466258 with STD : 0.021472392638
+
+
+2017-09-22 15:10:15,167 INFO: Done: Result Analysis
+2017-09-22 15:10:58,738 INFO: Done: Classification
+2017-09-22 15:11:12,864 INFO: Done: Classification
+2017-09-22 15:11:12,865 INFO: Info: Time for Classification: 60[s]
+2017-09-22 15:11:12,865 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:11:13,016 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.998694516971
+ -On Test : 0.782208588957
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - SVM Linear with C : 1
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458 with STD : 0.0
+ - Score on test : 0.784194528875 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458 with STD : 0.0
+ - Score on test : 0.784194528875 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872 with STD : 0.0
+ - Score on test : 0.217791411043 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632 with STD : 0.0
+ - Score on test : 0.564512797725 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.777108433735 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943 with STD : 0.0
+ - Score on test : 0.791411042945 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872 with STD : 0.0
+ - Score on test : 0.217791411043 with STD : 0.0
+
+
+2017-09-22 15:11:13,016 INFO: Done: Result Analysis
+2017-09-22 15:11:13,084 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:11:13,084 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:11:13,085 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:11:13,085 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:11:13,086 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:11:13,087 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:11:13,087 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:11:13,088 INFO: Done: Read Database Files
+2017-09-22 15:11:13,088 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:11:13,088 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:11:13,089 INFO: Done: Read Database Files
+2017-09-22 15:11:13,089 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:11:13,127 INFO: Done: Determine validation split
+2017-09-22 15:11:13,127 INFO: Start: Determine 2 folds
+2017-09-22 15:11:13,127 INFO: Done: Determine validation split
+2017-09-22 15:11:13,127 INFO: Start: Determine 2 folds
+2017-09-22 15:12:40,984 INFO: Done: Classification
+2017-09-22 15:13:07,285 INFO: Done: Classification
+2017-09-22 15:13:07,285 INFO: Info: Time for Classification: 114[s]
+2017-09-22 15:13:07,285 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:13:07,521 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.782208588957
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - SVM Poly with C : 1
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.781538461538 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.781538461538 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.217791411043 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.564427799938 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.783950617284 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.779141104294 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.217791411043 with STD : 0.0
+
+
+2017-09-22 15:13:07,539 INFO: Done: Result Analysis
+2017-09-22 15:13:08,268 INFO: Done: Classification
+2017-09-22 15:13:31,009 INFO: Done: Classification
+2017-09-22 15:13:31,009 INFO: Info: Time for Classification: 137[s]
+2017-09-22 15:13:31,009 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:13:31,162 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.549079754601
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - SVM RBF with C : 1
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.549079754601 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.669662921348 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.669662921348 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.450920245399 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.549079754601 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.143637914281 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.528368794326 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.914110429448 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.549079754601 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.450920245399 with STD : 0.0
+
+
+2017-09-22 15:13:31,184 INFO: Done: Result Analysis
+2017-09-22 15:13:31,229 DEBUG: Start: Deleting 2 temporary datasets for multiprocessing
+2017-09-22 15:13:31,288 DEBUG: Start: Deleting datasets for multiprocessing
+2017-09-22 15:13:33,167 DEBUG: Start: Analyze Global Results
+2017-09-22 15:14:02,028 INFO: Extraction time : 9.24384999275s, Monoview time : 81.18699193s, Multiview Time : 249.405200005s
+2017-09-22 15:14:02,029 DEBUG: Done: Analyze Global Results
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150802Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150802Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..f350a5c9
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150802Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.891644908616, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.891644908616
+ - Score on test : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.890932982917
+ - Score on test : 0.658385093168
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.890932982917
+ - Score on test : 0.658385093168
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.108355091384
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.891644908616
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.783356573256
+ - Score on test : 0.325251323062
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.896825396825
+ - Score on test : 0.666666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.885117493473
+ - Score on test : 0.650306748466
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.891644908616
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.108355091384
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150803Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150803Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..363b24df
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150803Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for cq-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.658536585366
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.658536585366
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.312906990761
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.654545454545
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150805Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150805Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..ffd0593f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150805Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.993472584856, with STD : 0.0
+accuracy_score on test : 0.693251533742, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 13, 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 : 0.993472584856
+ - Score on test : 0.693251533742
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.993481095176
+ - Score on test : 0.685534591195
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.993481095176
+ - Score on test : 0.685534591195
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.306748466258
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.693251533742
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.986948533804
+ - Score on test : 0.386969418778
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.9921875
+ - Score on test : 0.703225806452
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.994778067885
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.693251533742
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.306748466258
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150814Results-KNN--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150814Results-KNN--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..8abf7311
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150814Results-KNN--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.661879895561, with STD : 0.0
+accuracy_score on test : 0.610429447853, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 31
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.610429447853
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.715071507151
+ - Score on test : 0.661333333333
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.715071507151
+ - Score on test : 0.661333333333
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.338120104439
+ - Score on test : 0.389570552147
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.610429447853
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.348998204171
+ - Score on test : 0.231569919477
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.617870722433
+ - Score on test : 0.584905660377
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.848563968668
+ - Score on test : 0.760736196319
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.661879895561
+ - Score on test : 0.610429447853
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.338120104439
+ - Score on test : 0.389570552147
+
+
+ Classification took 0:00:10
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150815Results-SGD--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150815Results-SGD--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..f036adb2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150815Results-SGD--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.5, with STD : 0.0
+accuracy_score on test : 0.5, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SGDClassifier with loss : log, penalty : l1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.666666666667
+ - Score on test : 0.666666666667
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.666666666667
+ - Score on test : 0.666666666667
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0
+ - Score on test : 0.0
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 1.0
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.5
+ - Score on test : 0.5
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150827Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150827Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..ca4e0456
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150827Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 2527
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.68023255814
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.68023255814
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.327154260952
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.646408839779
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.717791411043
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:12
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150845Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150845Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..135a2171
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150845Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 0.835509138381, with STD : 0.0
+accuracy_score on test : 0.720858895706, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 580
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.835509138381
+ - Score on test : 0.720858895706
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.831550802139
+ - Score on test : 0.723404255319
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.831550802139
+ - Score on test : 0.723404255319
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.164490861619
+ - Score on test : 0.279141104294
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.835509138381
+ - Score on test : 0.720858895706
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.671760563981
+ - Score on test : 0.441792624306
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.852054794521
+ - Score on test : 0.71686746988
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.812010443864
+ - Score on test : 0.730061349693
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.835509138381
+ - Score on test : 0.720858895706
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.164490861619
+ - Score on test : 0.279141104294
+
+
+ Classification took 0:00:18
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150848Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150848Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..196f8f53
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150848Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.93864229765, with STD : 0.0
+accuracy_score on test : 0.539877300613, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 8352
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.93864229765
+ - Score on test : 0.539877300613
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.93657219973
+ - Score on test : 0.385245901639
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.93657219973
+ - Score on test : 0.385245901639
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0613577023499
+ - Score on test : 0.460122699387
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.93864229765
+ - Score on test : 0.539877300613
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.879159518567
+ - Score on test : 0.0922821705
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.969273743017
+ - Score on test : 0.58024691358
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.906005221932
+ - Score on test : 0.288343558282
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.93864229765
+ - Score on test : 0.539877300613
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0613577023499
+ - Score on test : 0.460122699387
+
+
+ Classification took 0:00:21
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150850Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150850Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..ee9baaf6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150850Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for lss-hist with Adaboost, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.671779141104, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, 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 : 0.671779141104
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662460567823
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662460567823
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.328220858896
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.671779141104
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.344083179874
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.681818181818
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.644171779141
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.671779141104
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.328220858896
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150850Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150850Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..64fcc3c0
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150850Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with DecisionTree, and 2 statistical iterations
+
+accuracy_score on train : 0.881201044386, with STD : 0.0
+accuracy_score on test : 0.736196319018, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 5
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.881201044386
+ - Score on test : 0.736196319018
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.873082287308
+ - Score on test : 0.727848101266
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.873082287308
+ - Score on test : 0.727848101266
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.118798955614
+ - Score on test : 0.263803680982
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.881201044386
+ - Score on test : 0.736196319018
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.768719228721
+ - Score on test : 0.473284147545
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.937125748503
+ - Score on test : 0.751633986928
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.817232375979
+ - Score on test : 0.705521472393
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.881201044386
+ - Score on test : 0.736196319018
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.118798955614
+ - Score on test : 0.263803680982
+
+
+ Classification took 0:00:01
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150851Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150851Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..12724270
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150851Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with RandomForest, and 2 statistical iterations
+
+accuracy_score on train : 0.996083550914, with STD : 0.0
+accuracy_score on test : 0.71472392638, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 13, 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 : 0.996083550914
+ - Score on test : 0.71472392638
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.996068152031
+ - Score on test : 0.710280373832
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.996068152031
+ - Score on test : 0.710280373832
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00391644908616
+ - Score on test : 0.28527607362
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.996083550914
+ - Score on test : 0.71472392638
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.992197540084
+ - Score on test : 0.429650039123
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.721518987342
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.699386503067
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.996083550914
+ - Score on test : 0.71472392638
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00391644908616
+ - Score on test : 0.28527607362
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150858Results-KNN--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150858Results-KNN--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..0941dfb6
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150858Results-KNN--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with KNN, and 2 statistical iterations
+
+accuracy_score on train : 0.674934725849, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 45
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.674934725849
+ - Score on test : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.627802690583
+ - Score on test : 0.625407166124
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.627802690583
+ - Score on test : 0.625407166124
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.325065274151
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.674934725849
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.361660570561
+ - Score on test : 0.296499726664
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.734265734266
+ - Score on test : 0.666666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.548302872063
+ - Score on test : 0.588957055215
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.674934725849
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.325065274151
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:07
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150858Results-SGD--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150858Results-SGD--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..f045236d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150858Results-SGD--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SGD, and 2 statistical iterations
+
+accuracy_score on train : 0.8772845953, with STD : 0.0
+accuracy_score on test : 0.711656441718, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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 : 0.8772845953
+ - Score on test : 0.711656441718
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.887290167866
+ - Score on test : 0.744565217391
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.887290167866
+ - Score on test : 0.744565217391
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.1227154047
+ - Score on test : 0.288343558282
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.8772845953
+ - Score on test : 0.711656441718
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.766750900883
+ - Score on test : 0.438106276437
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.820399113082
+ - Score on test : 0.668292682927
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.966057441253
+ - Score on test : 0.840490797546
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.8772845953
+ - Score on test : 0.711656441718
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.1227154047
+ - Score on test : 0.288343558282
+
+
+ Classification took 0:00:00
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150906Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150906Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..87f5dbcb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150906Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMLinear, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.779141104294, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 2527
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.77358490566
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.77358490566
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.220858895706
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.558955827124
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.793548387097
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.754601226994
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779141104294
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.220858895706
+
+
+ Classification took 0:00:08
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150915Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150915Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..7706e282
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150915Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMPoly, and 2 statistical iterations
+
+accuracy_score on train : 0.513054830287, with STD : 0.0
+accuracy_score on test : 0.407975460123, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 8352
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.513054830287
+ - Score on test : 0.407975460123
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.504648074369
+ - Score on test : 0.398753894081
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.504648074369
+ - Score on test : 0.398753894081
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.486945169713
+ - Score on test : 0.592024539877
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.513054830287
+ - Score on test : 0.407975460123
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0261247140176
+ - Score on test : -0.184135731053
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.513513513514
+ - Score on test : 0.405063291139
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.496083550914
+ - Score on test : 0.39263803681
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.513054830287
+ - Score on test : 0.407975460123
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.486945169713
+ - Score on test : 0.592024539877
+
+
+ Classification took 0:00:08
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150921Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150921Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..1f4c6cac
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150921Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMRBF, and 2 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.530674846626, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 2527
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 2 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.530674846626
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670967741935
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.670967741935
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.469325153374
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.530674846626
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.117460246434
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.516556291391
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.957055214724
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.530674846626
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.469325153374
+
+
+ Classification took 0:00:14
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150926Results-Fusion-LateFusion-BayesianInference-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150926Results-Fusion-LateFusion-BayesianInference-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..7608ef41
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150926Results-Fusion-LateFusion-BayesianInference-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,58 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.926240208877
+ -On Test : 0.774539877301
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.441046660592, 0.558953339408
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l1
+ - SGDClassifier with loss : log, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.926240208877 with STD : 0.00456919060052
+ - Score on test : 0.774539877301 with STD : 0.00153374233129
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.927666429118 with STD : 0.00323317974772
+ - Score on test : 0.776252872711 with STD : 0.00186262880866
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.927666429118 with STD : 0.00323317974772
+ - Score on test : 0.776252872711 with STD : 0.00186262880866
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0737597911227 with STD : 0.00456919060052
+ - Score on test : 0.225460122699 with STD : 0.00153374233129
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.926240208877 with STD : 0.00456919060052
+ - Score on test : 0.774539877301 with STD : 0.00153374233129
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.853613263379 with STD : 0.00800847248104
+ - Score on test : 0.549147078209 with STD : 0.00309370217429
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.911315769465 with STD : 0.0183717305353
+ - Score on test : 0.770390653523 with STD : 0.000693683826214
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.945169712794 with STD : 0.0130548302872
+ - Score on test : 0.782208588957 with STD : 0.00306748466258
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.926240208877 with STD : 0.00456919060052
+ - Score on test : 0.774539877301 with STD : 0.00153374233129
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0737597911227 with STD : 0.00456919060052
+ - Score on test : 0.225460122699 with STD : 0.00153374233129
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150926Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150926Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..f038f6bc
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150926Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,58 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.916449086162
+ -On Test : 0.751533742331
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Majority Voting
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l1
+ - SGDClassifier with loss : log, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.916449086162 with STD : 0.0117493472585
+ - Score on test : 0.751533742331 with STD : 0.0153374233129
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.915544159287 with STD : 0.0127478876491
+ - Score on test : 0.732724817009 with STD : 0.0156195538515
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.915544159287 with STD : 0.0127478876491
+ - Score on test : 0.732724817009 with STD : 0.0156195538515
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0835509138381 with STD : 0.0117493472585
+ - Score on test : 0.248466257669 with STD : 0.0153374233129
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.916449086162 with STD : 0.0117493472585
+ - Score on test : 0.751533742331 with STD : 0.0153374233129
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.833210365221 with STD : 0.023189420725
+ - Score on test : 0.508189397466 with STD : 0.0314343445442
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.924139492754 with STD : 0.00294384057971
+ - Score on test : 0.792999642839 with STD : 0.0199499974488
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.907310704961 with STD : 0.0221932114883
+ - Score on test : 0.680981595092 with STD : 0.0122699386503
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.916449086162 with STD : 0.0117493472585
+ - Score on test : 0.751533742331 with STD : 0.0153374233129
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0835509138381 with STD : 0.0117493472585
+ - Score on test : 0.248466257669 with STD : 0.0153374233129
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150931Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150931Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..3ac2ede5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150931Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,58 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.848563968668
+ -On Test : 0.739263803681
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with SVM for linear
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l1
+ - SGDClassifier with loss : log, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.848563968668 with STD : 0.0
+ - Score on test : 0.739263803681 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.865740740741 with STD : 0.0
+ - Score on test : 0.768392370572 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.865740740741 with STD : 0.0
+ - Score on test : 0.768392370572 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.151436031332 with STD : 0.0
+ - Score on test : 0.260736196319 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.848563968668 with STD : 0.0
+ - Score on test : 0.739263803681 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.72113453306 with STD : 0.0
+ - Score on test : 0.494424068096 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.777546777547 with STD : 0.0
+ - Score on test : 0.691176470588 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.976501305483 with STD : 0.0
+ - Score on test : 0.865030674847 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.848563968668 with STD : 0.0
+ - Score on test : 0.739263803681 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.151436031332 with STD : 0.0
+ - Score on test : 0.260736196319 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150932Results-Fusion-LateFusion-SCMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150932Results-Fusion-LateFusion-SCMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..7b164726
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150932Results-Fusion-LateFusion-SCMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,58 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.946475195822
+ -On Test : 0.763803680982
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with SCM for linear with max_attributes : 17, p : 0.153950583132 model_type : conjunction has chosen 1 rule(s)
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l1
+ - SGDClassifier with loss : log, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822 with STD : 0.0
+ - Score on test : 0.763803680982 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.948685857322 with STD : 0.0
+ - Score on test : 0.772861356932 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.948685857322 with STD : 0.0
+ - Score on test : 0.772861356932 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0535248041775 with STD : 0.0
+ - Score on test : 0.236196319018 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822 with STD : 0.0
+ - Score on test : 0.763803680982 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.896283535397 with STD : 0.0
+ - Score on test : 0.529293411211 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.911057692308 with STD : 0.0
+ - Score on test : 0.744318181818 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.98955613577 with STD : 0.0
+ - Score on test : 0.803680981595 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.946475195822 with STD : 0.0
+ - Score on test : 0.763803680982 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0535248041775 with STD : 0.0
+ - Score on test : 0.236196319018 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150937Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150937Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..ba68d331
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150937Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,58 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.5
+ -On Test : 0.5
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Weighted linear using a weight for each view : 0.789058101091, 1.0
+ -With monoview classifiers :
+ - SGDClassifier with loss : log, penalty : l1
+ - SGDClassifier with loss : log, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.666666666667 with STD : 0.0
+ - Score on test : 0.666666666667 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.666666666667 with STD : 0.0
+ - Score on test : 0.666666666667 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 1.0 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150941Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150941Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..1c74268d
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150941Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.716257668712
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.716257668712 with STD : 0.0138036809816
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.710462382445 with STD : 0.0145376175549
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.710462382445 with STD : 0.0145376175549
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.283742331288 with STD : 0.0138036809816
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.716257668712 with STD : 0.0138036809816
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.43285830522 with STD : 0.027576435819
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.725195982362 with STD : 0.0136575208231
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.696319018405 with STD : 0.0153374233129
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.716257668712 with STD : 0.0138036809816
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.283742331288 with STD : 0.0138036809816
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-150948Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-150948Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..a4c42eba
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-150948Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.81592689295
+ -On Test : 0.742331288344
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - Decision Tree with max_depth : 3
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.742331288344 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.80971659919 with STD : 0.0
+ - Score on test : 0.727272727273 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.80971659919 with STD : 0.0
+ - Score on test : 0.727272727273 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.18407310705 with STD : 0.0
+ - Score on test : 0.257668711656 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.742331288344 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.633204177063 with STD : 0.0
+ - Score on test : 0.487645030476 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.837988826816 with STD : 0.0
+ - Score on test : 0.772413793103 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.783289817232 with STD : 0.0
+ - Score on test : 0.687116564417 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.742331288344 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.18407310705 with STD : 0.0
+ - Score on test : 0.257668711656 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151006Results-Fusion-EarlyFusion-WeightedLinear-KNN-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151006Results-Fusion-EarlyFusion-WeightedLinear-KNN-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..fb058016
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151006Results-Fusion-EarlyFusion-WeightedLinear-KNN-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.613496932515
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - K nearest Neighbors with n_neighbors: 1.0
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.613496932515 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.590909090909 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.590909090909 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.386503067485 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.613496932515 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.228390710476 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.627586206897 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.558282208589 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.613496932515 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.386503067485 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151011Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151011Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..d61330a4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151011Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.92362924282
+ -On Test : 0.75
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - Random Forest with num_esimators : 25, max_depth : 5
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.92362924282 with STD : 0.0032637075718
+ - Score on test : 0.75 with STD : 0.0138036809816
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.920665563523 with STD : 0.00365876080162
+ - Score on test : 0.74323439546 with STD : 0.0153862941947
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.920665563523 with STD : 0.00365876080162
+ - Score on test : 0.74323439546 with STD : 0.0153862941947
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0763707571802 with STD : 0.0032637075718
+ - Score on test : 0.25 with STD : 0.0138036809816
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.92362924282 with STD : 0.0032637075718
+ - Score on test : 0.75 with STD : 0.0138036809816
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.849629310887 with STD : 0.00613070449175
+ - Score on test : 0.500689352322 with STD : 0.0274052047766
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.957684778457 with STD : 0.000298414820474
+ - Score on test : 0.763637506285 with STD : 0.0120035193565
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.886422976501 with STD : 0.0065274151436
+ - Score on test : 0.723926380368 with STD : 0.0184049079755
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.92362924282 with STD : 0.0032637075718
+ - Score on test : 0.75 with STD : 0.0138036809816
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0763707571802 with STD : 0.0032637075718
+ - Score on test : 0.25 with STD : 0.0138036809816
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151011Results-Fusion-EarlyFusion-WeightedLinear-SCM-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151011Results-Fusion-EarlyFusion-WeightedLinear-SCM-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..b1e591eb
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151011Results-Fusion-EarlyFusion-WeightedLinear-SCM-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.5
+ -On Test : 0.5
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - SCM with max_attributes : 1
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151015Results-Fusion-EarlyFusion-WeightedLinear-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151015Results-Fusion-EarlyFusion-WeightedLinear-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..2c74ef60
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151015Results-Fusion-EarlyFusion-WeightedLinear-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.885770234987
+ -On Test : 0.693251533742
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - SGDClassifier with loss : log, penalty : l2
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.885770234987 with STD : 0.0319843342037
+ - Score on test : 0.693251533742 with STD : 0.021472392638
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.875370404976 with STD : 0.0455831709336
+ - Score on test : 0.672814010822 with STD : 0.0291358499023
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.875370404976 with STD : 0.0455831709336
+ - Score on test : 0.672814010822 with STD : 0.0291358499023
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.114229765013 with STD : 0.0319843342037
+ - Score on test : 0.306748466258 with STD : 0.021472392638
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.885770234987 with STD : 0.0319843342037
+ - Score on test : 0.693251533742 with STD : 0.021472392638
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.787879889409 with STD : 0.0503795952072
+ - Score on test : 0.409558056757 with STD : 0.058734849034
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.939600351339 with STD : 0.0531269213878
+ - Score on test : 0.75 with STD : 0.107142857143
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.835509138381 with STD : 0.1227154047
+ - Score on test : 0.644171779141 with STD : 0.128834355828
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.885770234987 with STD : 0.0319843342037
+ - Score on test : 0.693251533742 with STD : 0.021472392638
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.114229765013 with STD : 0.0319843342037
+ - Score on test : 0.306748466258 with STD : 0.021472392638
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151113Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151113Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..24bda0ff
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151113Results-Fusion-EarlyFusion-WeightedLinear-SVMLinear-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.998694516971
+ -On Test : 0.782208588957
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - SVM Linear with C : 1
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458 with STD : 0.0
+ - Score on test : 0.784194528875 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458 with STD : 0.0
+ - Score on test : 0.784194528875 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872 with STD : 0.0
+ - Score on test : 0.217791411043 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632 with STD : 0.0
+ - Score on test : 0.564512797725 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.777108433735 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943 with STD : 0.0
+ - Score on test : 0.791411042945 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872 with STD : 0.0
+ - Score on test : 0.217791411043 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151307Results-Fusion-EarlyFusion-WeightedLinear-SVMPoly-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151307Results-Fusion-EarlyFusion-WeightedLinear-SVMPoly-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..94ded252
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151307Results-Fusion-EarlyFusion-WeightedLinear-SVMPoly-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.782208588957
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - SVM Poly with C : 1
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.781538461538 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.781538461538 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.217791411043 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.564427799938 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.783950617284 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.779141104294 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.782208588957 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.217791411043 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151331Results-Fusion-EarlyFusion-WeightedLinear-SVMRBF-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151331Results-Fusion-EarlyFusion-WeightedLinear-SVMRBF-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..de562ec3
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151331Results-Fusion-EarlyFusion-WeightedLinear-SVMRBF-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.549079754601
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.789058101091, 1.0 with monoview classifier :
+ - SVM RBF with C : 1
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.549079754601 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.669662921348 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.669662921348 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.450920245399 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.549079754601 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.143637914281 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.528368794326 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.914110429448 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.549079754601 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.450920245399 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151331error_analysis.png b/Code/MonoMutliViewClassifiers/Results/20170922-151331error_analysis.png
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diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151342-awaexp-hamming_loss.png b/Code/MonoMutliViewClassifiers/Results/20170922-151342-awaexp-hamming_loss.png
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diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151344-awaexp-jaccard_similarity_score.png b/Code/MonoMutliViewClassifiers/Results/20170922-151344-awaexp-jaccard_similarity_score.png
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diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151807-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-151807-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..364e159c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151807-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,2 @@
+2017-09-22 15:18:13,994 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2017-09-22 15:18:13,994 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.08002225 Gbytes /!\
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151842-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-151842-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
new file mode 100644
index 00000000..f64a02e2
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151842-CMultiV-Benchmark-cq-hist_lss-hist-awaexp-LOG.log
@@ -0,0 +1,1921 @@
+2017-09-22 15:18:48,417 DEBUG: Start: Creating 4 temporary datasets for multiprocessing
+2017-09-22 15:18:48,417 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.1600445 Gbytes /!\
+2017-09-22 15:18:50,304 DEBUG: Start: Creating datasets for multiprocessing
+2017-09-22 15:18:50,306 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 15:18:50,380 DEBUG: Start: Loading data
+2017-09-22 15:18:50,380 DEBUG: Start: Loading data
+2017-09-22 15:18:50,382 DEBUG: Start: Loading data
+2017-09-22 15:18:50,383 DEBUG: Start: Loading data
+2017-09-22 15:18:50,396 DEBUG: Done: Loading data
+2017-09-22 15:18:50,397 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 15:18:50,397 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:18:50,397 DEBUG: Done: Loading data
+2017-09-22 15:18:50,397 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 15:18:50,397 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:18:50,404 DEBUG: Done: Loading data
+2017-09-22 15:18:50,404 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 15:18:50,404 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:18:50,404 DEBUG: Done: Loading data
+2017-09-22 15:18:50,404 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 15:18:50,405 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:18:50,424 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:18:50,424 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:18:50,424 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:18:50,424 DEBUG: Start: RandomSearch best settings with 20 iterations for DecisionTree
+2017-09-22 15:18:50,435 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:18:50,436 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:18:50,436 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:18:50,436 DEBUG: Start: RandomSearch best settings with 20 iterations for Adaboost
+2017-09-22 15:18:51,033 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:18:51,033 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:18:51,039 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:18:51,068 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:18:51,068 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:18:51,068 DEBUG: Start: RandomSearch best settings with 20 iterations for RandomForest
+2017-09-22 15:18:51,068 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:18:51,068 DEBUG: Start: RandomSearch best settings with 20 iterations for KNN
+2017-09-22 15:18:57,193 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:18:57,194 DEBUG: Start: Training
+2017-09-22 15:18:57,606 DEBUG: Done: Training
+2017-09-22 15:18:57,606 DEBUG: Start: Predicting
+2017-09-22 15:18:57,694 DEBUG: Done: Predicting
+2017-09-22 15:18:57,694 DEBUG: Info: Time for training and predicting: 7.31078577042[s]
+2017-09-22 15:18:57,694 DEBUG: Start: Getting Results
+2017-09-22 15:18:57,726 DEBUG: Done: Getting Results
+2017-09-22 15:18:57,726 INFO: Classification on awaexp database for cq-hist with RandomForest, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.760736196319, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 25, max_depth : 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.751592356688
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.751592356688
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.239263803681
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522891314133
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.781456953642
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.723926380368
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.239263803681
+
+
+ Classification took 0:00:07
+2017-09-22 15:18:57,727 INFO: Done: Result Analysis
+2017-09-22 15:19:04,853 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:19:04,854 DEBUG: Start: Training
+2017-09-22 15:19:05,109 DEBUG: Done: Training
+2017-09-22 15:19:05,110 DEBUG: Start: Predicting
+2017-09-22 15:19:05,126 DEBUG: Done: Predicting
+2017-09-22 15:19:05,126 DEBUG: Info: Time for training and predicting: 14.7456841469[s]
+2017-09-22 15:19:05,126 DEBUG: Start: Getting Results
+2017-09-22 15:19:05,158 DEBUG: Done: Getting Results
+2017-09-22 15:19:05,158 INFO: Classification on awaexp database for cq-hist with DecisionTree, and 5 statistical iterations
+
+accuracy_score on train : 0.720626631854, with STD : 0.0
+accuracy_score on test : 0.653374233129, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.720626631854
+ - Score on test : 0.653374233129
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.743405275779
+ - Score on test : 0.678062678063
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.743405275779
+ - Score on test : 0.678062678063
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.279373368146
+ - Score on test : 0.346625766871
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.720626631854
+ - Score on test : 0.653374233129
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.448376824392
+ - Score on test : 0.310421316554
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.687361419069
+ - Score on test : 0.632978723404
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.809399477807
+ - Score on test : 0.730061349693
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.720626631854
+ - Score on test : 0.653374233129
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.279373368146
+ - Score on test : 0.346625766871
+
+
+ Classification took 0:00:14
+2017-09-22 15:19:05,159 INFO: Done: Result Analysis
+2017-09-22 15:19:09,721 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:19:09,721 DEBUG: Start: Training
+2017-09-22 15:19:10,625 DEBUG: Done: Training
+2017-09-22 15:19:10,625 DEBUG: Start: Predicting
+2017-09-22 15:19:10,644 DEBUG: Done: Predicting
+2017-09-22 15:19:10,644 DEBUG: Info: Time for training and predicting: 20.2636079788[s]
+2017-09-22 15:19:10,644 DEBUG: Start: Getting Results
+2017-09-22 15:19:10,673 DEBUG: Done: Getting Results
+2017-09-22 15:19:10,673 INFO: Classification on awaexp database for cq-hist with Adaboost, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.693251533742, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.693251533742
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.683544303797
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.683544303797
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.306748466258
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.693251533742
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.387232484355
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.705882352941
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.693251533742
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.306748466258
+
+
+ Classification took 0:00:20
+2017-09-22 15:19:10,673 INFO: Done: Result Analysis
+2017-09-22 15:19:19,163 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:19:19,163 DEBUG: Start: Training
+2017-09-22 15:19:19,221 DEBUG: Done: Training
+2017-09-22 15:19:19,221 DEBUG: Start: Predicting
+2017-09-22 15:19:26,334 DEBUG: Done: Predicting
+2017-09-22 15:19:26,335 DEBUG: Info: Time for training and predicting: 35.9515879154[s]
+2017-09-22 15:19:26,335 DEBUG: Start: Getting Results
+2017-09-22 15:19:26,361 DEBUG: Done: Getting Results
+2017-09-22 15:19:26,362 INFO: Classification on awaexp database for cq-hist with KNN, and 5 statistical iterations
+
+accuracy_score on train : 0.778067885117, with STD : 0.0
+accuracy_score on test : 0.638036809816, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 6
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.778067885117
+ - Score on test : 0.638036809816
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.783715012723
+ - Score on test : 0.64880952381
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.783715012723
+ - Score on test : 0.64880952381
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.221932114883
+ - Score on test : 0.361963190184
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.778067885117
+ - Score on test : 0.638036809816
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.556895575998
+ - Score on test : 0.276594631682
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.764267990074
+ - Score on test : 0.630057803468
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.804177545692
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.778067885117
+ - Score on test : 0.638036809816
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.221932114883
+ - Score on test : 0.361963190184
+
+
+ Classification took 0:00:35
+2017-09-22 15:19:26,362 INFO: Done: Result Analysis
+2017-09-22 15:19:26,518 DEBUG: Start: Loading data
+2017-09-22 15:19:26,518 DEBUG: Start: Loading data
+2017-09-22 15:19:26,519 DEBUG: Start: Loading data
+2017-09-22 15:19:26,519 DEBUG: Start: Loading data
+2017-09-22 15:19:26,542 DEBUG: Done: Loading data
+2017-09-22 15:19:26,542 DEBUG: Done: Loading data
+2017-09-22 15:19:26,542 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 15:19:26,542 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 15:19:26,542 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:19:26,542 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:19:26,543 DEBUG: Done: Loading data
+2017-09-22 15:19:26,543 DEBUG: Done: Loading data
+2017-09-22 15:19:26,543 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 15:19:26,543 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 15:19:26,544 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:19:26,544 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:19:26,590 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:19:26,590 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:19:26,590 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:19:26,590 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMLinear
+2017-09-22 15:19:26,595 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:19:26,595 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:19:26,595 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:19:26,595 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:19:26,595 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:19:26,595 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:19:26,596 DEBUG: Start: RandomSearch best settings with 20 iterations for SGD
+2017-09-22 15:19:26,596 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMPoly
+2017-09-22 15:19:26,607 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:19:26,607 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:19:26,607 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:19:26,607 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMRBF
+2017-09-22 15:19:30,317 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:19:30,317 DEBUG: Start: Training
+2017-09-22 15:19:30,394 DEBUG: Done: Training
+2017-09-22 15:19:30,395 DEBUG: Start: Predicting
+2017-09-22 15:19:30,410 DEBUG: Done: Predicting
+2017-09-22 15:19:30,410 DEBUG: Info: Time for training and predicting: 3.89180397987[s]
+2017-09-22 15:19:30,410 DEBUG: Start: Getting Results
+2017-09-22 15:19:30,461 DEBUG: Done: Getting Results
+2017-09-22 15:19:30,462 INFO: Classification on awaexp database for cq-hist with SGD, and 5 statistical iterations
+
+accuracy_score on train : 0.671018276762, with STD : 0.0
+accuracy_score on test : 0.650306748466, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.671018276762
+ - Score on test : 0.650306748466
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.688118811881
+ - Score on test : 0.666666666667
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.688118811881
+ - Score on test : 0.666666666667
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.328981723238
+ - Score on test : 0.349693251534
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.671018276762
+ - Score on test : 0.650306748466
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.34411186005
+ - Score on test : 0.302072296401
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.654117647059
+ - Score on test : 0.63687150838
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.725848563969
+ - Score on test : 0.699386503067
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.671018276762
+ - Score on test : 0.650306748466
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.328981723238
+ - Score on test : 0.349693251534
+
+
+ Classification took 0:00:03
+2017-09-22 15:19:30,464 INFO: Done: Result Analysis
+2017-09-22 15:20:05,341 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:20:05,341 DEBUG: Start: Training
+2017-09-22 15:20:08,383 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:20:08,383 DEBUG: Start: Training
+2017-09-22 15:20:13,364 DEBUG: Done: Training
+2017-09-22 15:20:13,364 DEBUG: Start: Predicting
+2017-09-22 15:20:17,509 DEBUG: Done: Predicting
+2017-09-22 15:20:17,509 DEBUG: Info: Time for training and predicting: 50.9905340672[s]
+2017-09-22 15:20:17,509 DEBUG: Start: Getting Results
+2017-09-22 15:20:17,543 DEBUG: Done: Getting Results
+2017-09-22 15:20:17,543 INFO: Classification on awaexp database for cq-hist with SVMLinear, and 5 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.644171779141, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 4431
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.644171779141
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.646341463415
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.646341463415
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.355828220859
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.644171779141
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.288365265996
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.642424242424
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.650306748466
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.644171779141
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.355828220859
+
+
+ Classification took 0:00:50
+2017-09-22 15:20:17,543 INFO: Done: Result Analysis
+2017-09-22 15:20:17,862 DEBUG: Done: Training
+2017-09-22 15:20:17,862 DEBUG: Start: Predicting
+2017-09-22 15:20:19,009 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:20:19,010 DEBUG: Start: Training
+2017-09-22 15:20:22,118 DEBUG: Done: Predicting
+2017-09-22 15:20:22,118 DEBUG: Info: Time for training and predicting: 55.5985689163[s]
+2017-09-22 15:20:22,118 DEBUG: Start: Getting Results
+2017-09-22 15:20:22,147 DEBUG: Done: Getting Results
+2017-09-22 15:20:22,147 INFO: Classification on awaexp database for cq-hist with SVMRBF, and 5 statistical iterations
+
+accuracy_score on train : 0.912532637076, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 2076
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.912532637076
+ - Score on test : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.911957950066
+ - Score on test : 0.664634146341
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.911957950066
+ - Score on test : 0.664634146341
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0874673629243
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.912532637076
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.825135590497
+ - Score on test : 0.325177853144
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.917989417989
+ - Score on test : 0.660606060606
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.906005221932
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.912532637076
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0874673629243
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:55
+2017-09-22 15:20:22,148 INFO: Done: Result Analysis
+2017-09-22 15:20:26,330 DEBUG: Done: Training
+2017-09-22 15:20:26,330 DEBUG: Start: Predicting
+2017-09-22 15:20:29,941 DEBUG: Done: Predicting
+2017-09-22 15:20:29,941 DEBUG: Info: Time for training and predicting: 63.4220380783[s]
+2017-09-22 15:20:29,941 DEBUG: Start: Getting Results
+2017-09-22 15:20:29,969 DEBUG: Done: Getting Results
+2017-09-22 15:20:29,969 INFO: Classification on awaexp database for cq-hist with SVMPoly, and 5 statistical iterations
+
+accuracy_score on train : 0.882506527415, with STD : 0.0
+accuracy_score on test : 0.69018404908, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 2640
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.882506527415
+ - Score on test : 0.69018404908
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.880636604775
+ - Score on test : 0.691131498471
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.880636604775
+ - Score on test : 0.691131498471
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.117493472585
+ - Score on test : 0.30981595092
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.882506527415
+ - Score on test : 0.69018404908
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.765388826201
+ - Score on test : 0.38037525648
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.894878706199
+ - Score on test : 0.689024390244
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.86684073107
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.882506527415
+ - Score on test : 0.69018404908
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.117493472585
+ - Score on test : 0.30981595092
+
+
+ Classification took 0:01:03
+2017-09-22 15:20:29,969 INFO: Done: Result Analysis
+2017-09-22 15:20:30,084 DEBUG: Start: Loading data
+2017-09-22 15:20:30,084 DEBUG: Start: Loading data
+2017-09-22 15:20:30,086 DEBUG: Start: Loading data
+2017-09-22 15:20:30,086 DEBUG: Start: Loading data
+2017-09-22 15:20:30,099 DEBUG: Done: Loading data
+2017-09-22 15:20:30,100 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
+2017-09-22 15:20:30,100 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:20:30,101 DEBUG: Done: Loading data
+2017-09-22 15:20:30,101 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
+2017-09-22 15:20:30,101 DEBUG: Done: Loading data
+2017-09-22 15:20:30,101 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:20:30,101 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
+2017-09-22 15:20:30,101 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:20:30,103 DEBUG: Done: Loading data
+2017-09-22 15:20:30,103 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
+2017-09-22 15:20:30,104 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:20:30,118 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:20:30,118 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:20:30,118 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:20:30,119 DEBUG: Start: RandomSearch best settings with 20 iterations for RandomForest
+2017-09-22 15:20:30,122 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:20:30,122 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:20:30,122 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:20:30,123 DEBUG: Start: RandomSearch best settings with 20 iterations for KNN
+2017-09-22 15:20:30,127 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:20:30,127 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:20:30,127 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:20:30,128 DEBUG: Start: RandomSearch best settings with 20 iterations for Adaboost
+2017-09-22 15:20:30,132 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:20:30,132 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:20:30,132 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:20:30,133 DEBUG: Start: RandomSearch best settings with 20 iterations for DecisionTree
+2017-09-22 15:20:35,522 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:20:35,523 DEBUG: Start: Training
+2017-09-22 15:20:35,789 DEBUG: Done: Training
+2017-09-22 15:20:35,789 DEBUG: Start: Predicting
+2017-09-22 15:20:35,859 DEBUG: Done: Predicting
+2017-09-22 15:20:35,859 DEBUG: Info: Time for training and predicting: 5.77298998833[s]
+2017-09-22 15:20:35,859 DEBUG: Start: Getting Results
+2017-09-22 15:20:35,891 DEBUG: Done: Getting Results
+2017-09-22 15:20:35,891 INFO: Classification on awaexp database for lss-hist with RandomForest, and 5 statistical iterations
+
+accuracy_score on train : 0.994778067885, with STD : 0.0
+accuracy_score on test : 0.696319018405, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 23
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.994778067885
+ - Score on test : 0.696319018405
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.994764397906
+ - Score on test : 0.683706070288
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.994764397906
+ - Score on test : 0.683706070288
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00522193211488
+ - Score on test : 0.303680981595
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.994778067885
+ - Score on test : 0.696319018405
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.989569627939
+ - Score on test : 0.393892771134
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997375328084
+ - Score on test : 0.713333333333
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.656441717791
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.994778067885
+ - Score on test : 0.696319018405
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00522193211488
+ - Score on test : 0.303680981595
+
+
+ Classification took 0:00:05
+2017-09-22 15:20:35,891 INFO: Done: Result Analysis
+2017-09-22 15:20:38,421 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:20:38,422 DEBUG: Start: Training
+2017-09-22 15:20:38,492 DEBUG: Done: Training
+2017-09-22 15:20:38,492 DEBUG: Start: Predicting
+2017-09-22 15:20:38,504 DEBUG: Done: Predicting
+2017-09-22 15:20:38,505 DEBUG: Info: Time for training and predicting: 8.41998815536[s]
+2017-09-22 15:20:38,505 DEBUG: Start: Getting Results
+2017-09-22 15:20:38,536 DEBUG: Done: Getting Results
+2017-09-22 15:20:38,537 INFO: Classification on awaexp database for lss-hist with DecisionTree, and 5 statistical iterations
+
+accuracy_score on train : 0.737597911227, with STD : 0.0
+accuracy_score on test : 0.696319018405, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.737597911227
+ - Score on test : 0.696319018405
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.715700141443
+ - Score on test : 0.64
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.715700141443
+ - Score on test : 0.64
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.262402088773
+ - Score on test : 0.303680981595
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.737597911227
+ - Score on test : 0.696319018405
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.480936510756
+ - Score on test : 0.413393911463
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.780864197531
+ - Score on test : 0.785714285714
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.660574412533
+ - Score on test : 0.539877300613
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.737597911227
+ - Score on test : 0.696319018405
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.262402088773
+ - Score on test : 0.303680981595
+
+
+ Classification took 0:00:08
+2017-09-22 15:20:38,537 INFO: Done: Result Analysis
+2017-09-22 15:20:41,155 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:20:41,155 DEBUG: Start: Training
+2017-09-22 15:20:41,697 DEBUG: Done: Training
+2017-09-22 15:20:41,698 DEBUG: Start: Predicting
+2017-09-22 15:20:41,714 DEBUG: Done: Predicting
+2017-09-22 15:20:41,714 DEBUG: Info: Time for training and predicting: 11.6299190521[s]
+2017-09-22 15:20:41,714 DEBUG: Start: Getting Results
+2017-09-22 15:20:41,746 DEBUG: Done: Getting Results
+2017-09-22 15:20:41,747 INFO: Classification on awaexp database for lss-hist with Adaboost, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.645569620253
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.645569620253
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.313473915907
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:00:11
+2017-09-22 15:20:41,747 INFO: Done: Result Analysis
+2017-09-22 15:20:50,095 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:20:50,095 DEBUG: Start: Training
+2017-09-22 15:20:50,125 DEBUG: Done: Training
+2017-09-22 15:20:50,125 DEBUG: Start: Predicting
+2017-09-22 15:20:55,347 DEBUG: Done: Predicting
+2017-09-22 15:20:55,347 DEBUG: Info: Time for training and predicting: 25.260666132[s]
+2017-09-22 15:20:55,348 DEBUG: Start: Getting Results
+2017-09-22 15:20:55,374 DEBUG: Done: Getting Results
+2017-09-22 15:20:55,374 INFO: Classification on awaexp database for lss-hist with KNN, and 5 statistical iterations
+
+accuracy_score on train : 0.659268929504, with STD : 0.0
+accuracy_score on test : 0.659509202454, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 43
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.659268929504
+ - Score on test : 0.659509202454
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.607518796992
+ - Score on test : 0.621160409556
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.607518796992
+ - Score on test : 0.621160409556
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.340731070496
+ - Score on test : 0.340490797546
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.659268929504
+ - Score on test : 0.659509202454
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.330227012588
+ - Score on test : 0.325764407171
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.716312056738
+ - Score on test : 0.7
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.527415143603
+ - Score on test : 0.558282208589
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.659268929504
+ - Score on test : 0.659509202454
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.340731070496
+ - Score on test : 0.340490797546
+
+
+ Classification took 0:00:25
+2017-09-22 15:20:55,375 INFO: Done: Result Analysis
+2017-09-22 15:20:55,508 DEBUG: Start: Loading data
+2017-09-22 15:20:55,508 DEBUG: Start: Loading data
+2017-09-22 15:20:55,509 DEBUG: Start: Loading data
+2017-09-22 15:20:55,509 DEBUG: Start: Loading data
+2017-09-22 15:20:55,527 DEBUG: Done: Loading data
+2017-09-22 15:20:55,528 DEBUG: Done: Loading data
+2017-09-22 15:20:55,528 DEBUG: Done: Loading data
+2017-09-22 15:20:55,528 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
+2017-09-22 15:20:55,528 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
+2017-09-22 15:20:55,528 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
+2017-09-22 15:20:55,528 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:20:55,528 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:20:55,528 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:20:55,529 DEBUG: Done: Loading data
+2017-09-22 15:20:55,529 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
+2017-09-22 15:20:55,529 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:20:55,564 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:20:55,564 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:20:55,565 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:20:55,565 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:20:55,565 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:20:55,565 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:20:55,565 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMPoly
+2017-09-22 15:20:55,565 DEBUG: Start: RandomSearch best settings with 20 iterations for SGD
+2017-09-22 15:20:55,565 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:20:55,566 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:20:55,566 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:20:55,566 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMRBF
+2017-09-22 15:20:55,568 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:20:55,568 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:20:55,568 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:20:55,568 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMLinear
+2017-09-22 15:20:58,849 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:20:58,849 DEBUG: Start: Training
+2017-09-22 15:20:58,986 DEBUG: Done: Training
+2017-09-22 15:20:58,986 DEBUG: Start: Predicting
+2017-09-22 15:20:58,998 DEBUG: Done: Predicting
+2017-09-22 15:20:58,998 DEBUG: Info: Time for training and predicting: 3.48953294754[s]
+2017-09-22 15:20:58,998 DEBUG: Start: Getting Results
+2017-09-22 15:20:59,057 DEBUG: Done: Getting Results
+2017-09-22 15:20:59,057 INFO: Classification on awaexp database for lss-hist with SGD, and 5 statistical iterations
+
+accuracy_score on train : 0.90861618799, with STD : 0.0
+accuracy_score on test : 0.766871165644, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.90861618799
+ - Score on test : 0.766871165644
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.915254237288
+ - Score on test : 0.788888888889
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.915254237288
+ - Score on test : 0.788888888889
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0913838120104
+ - Score on test : 0.233128834356
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.90861618799
+ - Score on test : 0.766871165644
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.827448957764
+ - Score on test : 0.545746908693
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.853273137698
+ - Score on test : 0.720812182741
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.986945169713
+ - Score on test : 0.871165644172
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.90861618799
+ - Score on test : 0.766871165644
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0913838120104
+ - Score on test : 0.233128834356
+
+
+ Classification took 0:00:03
+2017-09-22 15:20:59,058 INFO: Done: Result Analysis
+2017-09-22 15:21:12,034 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:21:12,035 DEBUG: Start: Training
+2017-09-22 15:21:17,556 DEBUG: Done: Training
+2017-09-22 15:21:17,557 DEBUG: Start: Predicting
+2017-09-22 15:21:20,345 DEBUG: Done: Predicting
+2017-09-22 15:21:20,346 DEBUG: Info: Time for training and predicting: 24.8365252018[s]
+2017-09-22 15:21:20,346 DEBUG: Start: Getting Results
+2017-09-22 15:21:20,379 DEBUG: Done: Getting Results
+2017-09-22 15:21:20,379 INFO: Classification on awaexp database for lss-hist with SVMPoly, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.763803680982, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 2640
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.763803680982
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757097791798
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757097791798
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.236196319018
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.763803680982
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.528413454807
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779220779221
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.736196319018
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.763803680982
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.236196319018
+
+
+ Classification took 0:00:24
+2017-09-22 15:21:20,395 INFO: Done: Result Analysis
+2017-09-22 15:21:21,321 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:21:21,322 DEBUG: Start: Training
+2017-09-22 15:21:25,872 DEBUG: Done: Training
+2017-09-22 15:21:25,872 DEBUG: Start: Predicting
+2017-09-22 15:21:28,131 DEBUG: Done: Predicting
+2017-09-22 15:21:28,131 DEBUG: Info: Time for training and predicting: 32.6222882271[s]
+2017-09-22 15:21:28,131 DEBUG: Start: Getting Results
+2017-09-22 15:21:28,159 DEBUG: Done: Getting Results
+2017-09-22 15:21:28,159 INFO: Classification on awaexp database for lss-hist with SVMLinear, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.751533742331, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 4431
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.751533742331
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.744479495268
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.744479495268
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.248466257669
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.751533742331
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.503836084816
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.766233766234
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.723926380368
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.751533742331
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.248466257669
+
+
+ Classification took 0:00:32
+2017-09-22 15:21:28,160 INFO: Done: Result Analysis
+2017-09-22 15:21:31,268 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:21:31,268 DEBUG: Start: Training
+2017-09-22 15:21:37,402 DEBUG: Done: Training
+2017-09-22 15:21:37,402 DEBUG: Start: Predicting
+2017-09-22 15:21:40,890 DEBUG: Done: Predicting
+2017-09-22 15:21:40,890 DEBUG: Info: Time for training and predicting: 45.38053298[s]
+2017-09-22 15:21:40,890 DEBUG: Start: Getting Results
+2017-09-22 15:21:40,917 DEBUG: Done: Getting Results
+2017-09-22 15:21:40,917 INFO: Classification on awaexp database for lss-hist with SVMRBF, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.536809815951, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 4431
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.536809815951
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.678038379531
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.678038379531
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.463190184049
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.536809815951
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.153392997769
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.519607843137
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.975460122699
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.536809815951
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.463190184049
+
+
+ Classification took 0:00:45
+2017-09-22 15:21:40,917 INFO: Done: Result Analysis
+2017-09-22 15:21:41,069 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:21:41,070 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:21:41,070 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:21:41,070 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:21:41,071 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:21:41,071 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:21:41,071 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:21:41,072 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:21:41,072 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:21:41,072 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:21:41,072 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:21:41,072 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:21:41,072 INFO: Done: Read Database Files
+2017-09-22 15:21:41,072 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:21:41,072 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:21:41,073 INFO: Done: Read Database Files
+2017-09-22 15:21:41,073 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:21:41,073 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:21:41,073 INFO: Done: Read Database Files
+2017-09-22 15:21:41,073 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:21:41,073 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:21:41,074 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:21:41,074 INFO: Done: Read Database Files
+2017-09-22 15:21:41,074 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:21:41,175 INFO: Done: Determine validation split
+2017-09-22 15:21:41,175 INFO: Start: Determine 2 folds
+2017-09-22 15:21:41,176 INFO: Done: Determine validation split
+2017-09-22 15:21:41,176 INFO: Start: Determine 2 folds
+2017-09-22 15:21:41,176 INFO: Done: Determine validation split
+2017-09-22 15:21:41,176 INFO: Done: Determine validation split
+2017-09-22 15:21:41,176 INFO: Start: Determine 2 folds
+2017-09-22 15:21:41,176 INFO: Start: Determine 2 folds
+2017-09-22 15:23:20,892 INFO: Done: Classification
+2017-09-22 15:23:21,861 INFO: Done: Classification
+2017-09-22 15:23:22,796 INFO: Done: Classification
+2017-09-22 15:23:23,579 INFO: Done: Classification
+2017-09-22 15:23:24,525 INFO: Done: Classification
+2017-09-22 15:23:24,526 INFO: Info: Time for Classification: 103[s]
+2017-09-22 15:23:24,526 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:23:25,723 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.935770234987
+ -On Test : 0.765644171779
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.468344038717, 0.531655961283
+ -With monoview classifiers :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.935770234987 with STD : 0.0100919833369
+ - Score on test : 0.765644171779 with STD : 0.0205864561173
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.935715958222 with STD : 0.0104056406106
+ - Score on test : 0.774713716177 with STD : 0.0119980053689
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.935715958222 with STD : 0.0104056406106
+ - Score on test : 0.774713716177 with STD : 0.0119980053689
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0642297650131 with STD : 0.0100919833369
+ - Score on test : 0.234355828221 with STD : 0.0205864561173
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.935770234987 with STD : 0.0100919833369
+ - Score on test : 0.765644171779 with STD : 0.0205864561173
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.873353087698 with STD : 0.0208530817938
+ - Score on test : 0.536583295537 with STD : 0.0376442422002
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.937028186848 with STD : 0.0288106065084
+ - Score on test : 0.750864226564 with STD : 0.0439764143916
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.936292428198 with STD : 0.0343458666369
+ - Score on test : 0.80490797546 with STD : 0.043966804582
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.935770234987 with STD : 0.0100919833369
+ - Score on test : 0.765644171779 with STD : 0.0205864561173
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0642297650131 with STD : 0.0100919833369
+ - Score on test : 0.234355828221 with STD : 0.0205864561173
+
+
+2017-09-22 15:23:25,729 INFO: Done: Result Analysis
+2017-09-22 15:23:30,891 INFO: Done: Classification
+2017-09-22 15:23:31,604 INFO: Done: Classification
+2017-09-22 15:23:32,737 INFO: Done: Classification
+2017-09-22 15:23:33,428 INFO: Done: Classification
+2017-09-22 15:23:34,238 INFO: Done: Classification
+2017-09-22 15:23:34,239 INFO: Info: Time for Classification: 113[s]
+2017-09-22 15:23:34,239 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:23:35,008 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.921409921671
+ -On Test : 0.771165644172
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Majority Voting
+ -With monoview classifiers :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.921409921671 with STD : 0.010746274479
+ - Score on test : 0.771165644172 with STD : 0.0120533022726
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.920511234097 with STD : 0.0103233115923
+ - Score on test : 0.769568689392 with STD : 0.0219732543554
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.920511234097 with STD : 0.0103233115923
+ - Score on test : 0.769568689392 with STD : 0.0219732543554
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.078590078329 with STD : 0.010746274479
+ - Score on test : 0.228834355828 with STD : 0.0120533022726
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.921409921671 with STD : 0.010746274479
+ - Score on test : 0.771165644172 with STD : 0.0120533022726
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.848090681947 with STD : 0.0198951734938
+ - Score on test : 0.55154560512 with STD : 0.0269686279704
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.936431809467 with STD : 0.0523933339542
+ - Score on test : 0.780105771849 with STD : 0.0530162883504
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.910704960836 with STD : 0.0519942932131
+ - Score on test : 0.771779141104 with STD : 0.0899479401625
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.921409921671 with STD : 0.010746274479
+ - Score on test : 0.771165644172 with STD : 0.0120533022726
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.078590078329 with STD : 0.010746274479
+ - Score on test : 0.228834355828 with STD : 0.0120533022726
+
+
+2017-09-22 15:23:35,013 INFO: Done: Result Analysis
+2017-09-22 15:23:37,771 INFO: Done: Classification
+2017-09-22 15:23:38,394 INFO: Done: Classification
+2017-09-22 15:23:39,013 INFO: Done: Classification
+2017-09-22 15:23:39,626 INFO: Done: Classification
+2017-09-22 15:23:40,246 INFO: Done: Classification
+2017-09-22 15:23:40,246 INFO: Info: Time for Classification: 119[s]
+2017-09-22 15:23:40,247 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:23:40,772 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.925587467363
+ -On Test : 0.720858895706
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with SVM for linear
+ -With monoview classifiers :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.925587467363 with STD : 0.0
+ - Score on test : 0.720858895706 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.921595598349 with STD : 0.0
+ - Score on test : 0.693602693603 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.921595598349 with STD : 0.0
+ - Score on test : 0.693602693603 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0744125326371 with STD : 0.0
+ - Score on test : 0.279141104294 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.925587467363 with STD : 0.0
+ - Score on test : 0.720858895706 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.85562241482 with STD : 0.0
+ - Score on test : 0.448879201248 with STD : 5.55111512313e-17
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.973837209302 with STD : 0.0
+ - Score on test : 0.768656716418 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.874673629243 with STD : 0.0
+ - Score on test : 0.631901840491 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.925587467363 with STD : 0.0
+ - Score on test : 0.720858895706 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0744125326371 with STD : 0.0
+ - Score on test : 0.279141104294 with STD : 0.0
+
+
+2017-09-22 15:23:40,773 INFO: Done: Result Analysis
+2017-09-22 15:23:56,413 INFO: Done: Classification
+2017-09-22 15:23:57,189 INFO: Done: Classification
+2017-09-22 15:23:57,923 INFO: Done: Classification
+2017-09-22 15:23:58,681 INFO: Done: Classification
+2017-09-22 15:23:59,440 INFO: Done: Classification
+2017-09-22 15:23:59,440 INFO: Info: Time for Classification: 138[s]
+2017-09-22 15:23:59,440 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:23:59,908 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.960835509138
+ -On Test : 0.794478527607
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with SCM for linear with max_attributes : 12, p : 0.310533606766 model_type : disjunction has chosen 1 rule(s)
+ -With monoview classifiers :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.960835509138 with STD : 0.0
+ - Score on test : 0.794478527607 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.961038961039 with STD : 1.11022302463e-16
+ - Score on test : 0.804664723032 with STD : 1.11022302463e-16
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.961038961039 with STD : 1.11022302463e-16
+ - Score on test : 0.804664723032 with STD : 1.11022302463e-16
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0391644908616 with STD : 0.0
+ - Score on test : 0.205521472393 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.960835509138 with STD : 0.0
+ - Score on test : 0.794478527607 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.9217212877 with STD : 0.0
+ - Score on test : 0.592186568158 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.956072351421 with STD : 0.0
+ - Score on test : 0.766666666667 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.966057441253 with STD : 0.0
+ - Score on test : 0.846625766871 with STD : 1.11022302463e-16
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.960835509138 with STD : 0.0
+ - Score on test : 0.794478527607 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0391644908616 with STD : 0.0
+ - Score on test : 0.205521472393 with STD : 2.77555756156e-17
+
+
+2017-09-22 15:23:59,908 INFO: Done: Result Analysis
+2017-09-22 15:24:00,001 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:24:00,002 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:24:00,002 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:24:00,002 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:24:00,003 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:24:00,003 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:24:00,003 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:24:00,004 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:24:00,004 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:24:00,004 INFO: Done: Read Database Files
+2017-09-22 15:24:00,004 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:24:00,004 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:24:00,005 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:24:00,005 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:24:00,005 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:24:00,005 INFO: Done: Read Database Files
+2017-09-22 15:24:00,005 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:24:00,006 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:24:00,006 INFO: Done: Read Database Files
+2017-09-22 15:24:00,006 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:24:00,006 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:24:00,007 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:24:00,007 INFO: Done: Read Database Files
+2017-09-22 15:24:00,008 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:24:00,104 INFO: Done: Determine validation split
+2017-09-22 15:24:00,104 INFO: Start: Determine 2 folds
+2017-09-22 15:24:00,105 INFO: Done: Determine validation split
+2017-09-22 15:24:00,105 INFO: Start: Determine 2 folds
+2017-09-22 15:24:00,106 INFO: Done: Determine validation split
+2017-09-22 15:24:00,106 INFO: Start: Determine 2 folds
+2017-09-22 15:24:00,107 INFO: Done: Determine validation split
+2017-09-22 15:24:00,107 INFO: Start: Determine 2 folds
+2017-09-22 15:24:53,056 INFO: Done: Classification
+2017-09-22 15:24:53,479 INFO: Done: Classification
+2017-09-22 15:24:53,905 INFO: Done: Classification
+2017-09-22 15:24:54,332 INFO: Done: Classification
+2017-09-22 15:24:54,768 INFO: Done: Classification
+2017-09-22 15:24:54,768 INFO: Info: Time for Classification: 54[s]
+2017-09-22 15:24:54,768 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:24:55,254 INFO: Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.5
+ -On Test : 0.5
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Weighted linear using a weight for each view : 0.880915616157, 1.0
+ -With monoview classifiers :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.666666666667 with STD : 0.0
+ - Score on test : 0.666666666667 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.666666666667 with STD : 0.0
+ - Score on test : 0.666666666667 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 1.0 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+
+2017-09-22 15:24:55,254 INFO: Done: Result Analysis
+2017-09-22 15:25:40,130 INFO: Done: Classification
+2017-09-22 15:25:40,969 INFO: Done: Classification
+2017-09-22 15:25:41,807 INFO: Done: Classification
+2017-09-22 15:25:42,648 INFO: Done: Classification
+2017-09-22 15:25:43,496 INFO: Done: Classification
+2017-09-22 15:25:43,497 INFO: Info: Time for Classification: 103[s]
+2017-09-22 15:25:43,497 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:25:43,942 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.81592689295
+ -On Test : 0.761349693252
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - Decision Tree with max_depth : 3
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.761349693252 with STD : 0.00122699386503
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.814229249012 with STD : 0.0
+ - Score on test : 0.763814289686 with STD : 0.000355852098971
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.814229249012 with STD : 0.0
+ - Score on test : 0.763814289686 with STD : 0.000355852098971
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.18407310705 with STD : 0.0
+ - Score on test : 0.238650306748 with STD : 0.00122699386503
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.761349693252 with STD : 0.00122699386503
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.63195934458 with STD : 0.0
+ - Score on test : 0.522827042555 with STD : 0.0023951243455
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.821808510638 with STD : 1.11022302463e-16
+ - Score on test : 0.756031838762 with STD : 0.00308164159486
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.806788511749 with STD : 0.0
+ - Score on test : 0.771779141104 with STD : 0.00245398773006
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.761349693252 with STD : 0.00122699386503
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.18407310705 with STD : 0.0
+ - Score on test : 0.238650306748 with STD : 0.00122699386503
+
+
+2017-09-22 15:25:43,942 INFO: Done: Result Analysis
+2017-09-22 15:26:49,465 INFO: Done: Classification
+2017-09-22 15:26:50,915 INFO: Done: Classification
+2017-09-22 15:26:52,405 INFO: Done: Classification
+2017-09-22 15:26:53,885 INFO: Done: Classification
+2017-09-22 15:26:55,337 INFO: Done: Classification
+2017-09-22 15:26:55,337 INFO: Info: Time for Classification: 175[s]
+2017-09-22 15:26:55,337 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:26:55,730 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.676073619632
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.676073619632 with STD : 0.0103751745554
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.670346314584 with STD : 0.0115934576486
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.670346314584 with STD : 0.0115934576486
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.323926380368 with STD : 0.0103751745554
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.676073619632 with STD : 0.0103751745554
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.352483888102 with STD : 0.020663312867
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.682484956007 with STD : 0.0117935360735
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.658895705521 with STD : 0.0171779141104
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.676073619632 with STD : 0.0103751745554
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.323926380368 with STD : 0.0103751745554
+
+
+2017-09-22 15:26:55,730 INFO: Done: Result Analysis
+2017-09-22 15:29:27,698 INFO: Done: Classification
+2017-09-22 15:29:32,746 INFO: Done: Classification
+2017-09-22 15:29:37,762 INFO: Done: Classification
+2017-09-22 15:29:42,797 INFO: Done: Classification
+2017-09-22 15:29:47,805 INFO: Done: Classification
+2017-09-22 15:29:47,805 INFO: Info: Time for Classification: 347[s]
+2017-09-22 15:29:47,805 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:29:48,173 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.592024539877
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - K nearest Neighbors with n_neighbors: 1.0
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.592024539877 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.583072100313 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.583072100313 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.407975460123 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.592024539877 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.184219031546 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.596153846154 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.570552147239 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.592024539877 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.407975460123 with STD : 0.0
+
+
+2017-09-22 15:29:48,173 INFO: Done: Result Analysis
+2017-09-22 15:29:48,255 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:29:48,255 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:29:48,256 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:29:48,256 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:29:48,256 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:29:48,256 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:29:48,257 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:29:48,257 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:29:48,257 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:29:48,257 INFO: Done: Read Database Files
+2017-09-22 15:29:48,257 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:29:48,258 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:29:48,258 INFO: ### Main Programm for Multiview Classification
+2017-09-22 15:29:48,258 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:29:48,258 INFO: Done: Read Database Files
+2017-09-22 15:29:48,258 INFO: ### Classification - Database : awaexp ; Views : cq-hist, lss-hist ; Algorithm : Fusion ; Cores : 1
+2017-09-22 15:29:48,258 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:29:48,258 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:29:48,259 INFO: Done: Read Database Files
+2017-09-22 15:29:48,259 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:29:48,260 INFO: Info: Shape of cq-hist :(1092, 2688)
+2017-09-22 15:29:48,260 INFO: Info: Shape of lss-hist :(1092, 2000)
+2017-09-22 15:29:48,261 INFO: Done: Read Database Files
+2017-09-22 15:29:48,261 INFO: Start: Determine validation split for ratio 0.7
+2017-09-22 15:29:48,317 INFO: Done: Determine validation split
+2017-09-22 15:29:48,317 INFO: Start: Determine 2 folds
+2017-09-22 15:29:48,319 INFO: Done: Determine validation split
+2017-09-22 15:29:48,319 INFO: Start: Determine 2 folds
+2017-09-22 15:29:48,366 INFO: Done: Determine validation split
+2017-09-22 15:29:48,366 INFO: Start: Determine 2 folds
+2017-09-22 15:29:48,368 INFO: Done: Determine validation split
+2017-09-22 15:29:48,368 INFO: Start: Determine 2 folds
+2017-09-22 15:30:45,224 INFO: Done: Classification
+2017-09-22 15:30:45,775 INFO: Done: Classification
+2017-09-22 15:30:46,324 INFO: Done: Classification
+2017-09-22 15:30:46,863 INFO: Done: Classification
+2017-09-22 15:30:47,410 INFO: Done: Classification
+2017-09-22 15:30:47,410 INFO: Info: Time for Classification: 59[s]
+2017-09-22 15:30:47,410 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:30:48,065 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.898955613577
+ -On Test : 0.753987730061
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - SGDClassifier with loss : log, penalty : l2
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.898955613577 with STD : 0.0718210275075
+ - Score on test : 0.753987730061 with STD : 0.037084861987
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.908951683797 with STD : 0.0540728241158
+ - Score on test : 0.763621260416 with STD : 0.00772558921165
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.908951683797 with STD : 0.0540728241158
+ - Score on test : 0.763621260416 with STD : 0.00772558921165
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.101044386423 with STD : 0.0718210275075
+ - Score on test : 0.246012269939 with STD : 0.037084861987
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.898955613577 with STD : 0.0718210275075
+ - Score on test : 0.753987730061 with STD : 0.037084861987
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.813276317483 with STD : 0.116263516032
+ - Score on test : 0.524172372511 with STD : 0.0514608194751
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.878849832215 with STD : 0.107924714973
+ - Score on test : 0.753947637281 with STD : 0.0775135641749
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.952480417755 with STD : 0.0242918085632
+ - Score on test : 0.791411042945 with STD : 0.0870214636883
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.898955613577 with STD : 0.0718210275075
+ - Score on test : 0.753987730061 with STD : 0.037084861987
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.101044386423 with STD : 0.0718210275075
+ - Score on test : 0.246012269939 with STD : 0.037084861987
+
+
+2017-09-22 15:30:48,065 INFO: Done: Result Analysis
+2017-09-22 15:31:23,834 INFO: Done: Classification
+2017-09-22 15:31:24,749 INFO: Done: Classification
+2017-09-22 15:31:25,649 INFO: Done: Classification
+2017-09-22 15:31:26,560 INFO: Done: Classification
+2017-09-22 15:31:27,480 INFO: Done: Classification
+2017-09-22 15:31:27,481 INFO: Info: Time for Classification: 99[s]
+2017-09-22 15:31:27,481 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:31:27,949 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.5
+ -On Test : 0.5
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - SCM with max_attributes : 1
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+
+2017-09-22 15:31:27,950 INFO: Done: Result Analysis
+2017-09-22 15:31:33,986 INFO: Done: Classification
+2017-09-22 15:31:34,595 INFO: Done: Classification
+2017-09-22 15:31:35,243 INFO: Done: Classification
+2017-09-22 15:31:35,854 INFO: Done: Classification
+2017-09-22 15:31:36,474 INFO: Done: Classification
+2017-09-22 15:31:36,474 INFO: Info: Time for Classification: 108[s]
+2017-09-22 15:31:36,474 INFO: Start: Result Analysis for Fusion
+2017-09-22 15:31:36,878 INFO: Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.915143603133
+ -On Test : 0.741717791411
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - Random Forest with num_esimators : 25, max_depth : 5
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.915143603133 with STD : 0.00340428324031
+ - Score on test : 0.741717791411 with STD : 0.00785659415636
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.911464433058 with STD : 0.00359680668862
+ - Score on test : 0.738297988431 with STD : 0.00916250860792
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.911464433058 with STD : 0.00359680668862
+ - Score on test : 0.738297988431 with STD : 0.00916250860792
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0848563968668 with STD : 0.00340428324031
+ - Score on test : 0.258282208589 with STD : 0.00785659415636
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.915143603133 with STD : 0.00340428324031
+ - Score on test : 0.741717791411 with STD : 0.00785659415636
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.833242709103 with STD : 0.00688042213647
+ - Score on test : 0.483653710409 with STD : 0.0156462607003
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.952834664301 with STD : 0.00775427616681
+ - Score on test : 0.7481099822 with STD : 0.00693626746348
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.87362924282 with STD : 0.00749488255583
+ - Score on test : 0.728834355828 with STD : 0.0136632254303
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.915143603133 with STD : 0.00340428324031
+ - Score on test : 0.741717791411 with STD : 0.00785659415636
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0848563968668 with STD : 0.00340428324031
+ - Score on test : 0.258282208589 with STD : 0.00785659415636
+
+
+2017-09-22 15:31:36,878 INFO: Done: Result Analysis
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151857Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151857Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..536417bf
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151857Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with RandomForest, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.760736196319, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 25, max_depth : 15
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.751592356688
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.751592356688
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.239263803681
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.522891314133
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.781456953642
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.723926380368
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.760736196319
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.239263803681
+
+
+ Classification took 0:00:07
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151905Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151905Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..4df3007c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151905Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with DecisionTree, and 5 statistical iterations
+
+accuracy_score on train : 0.720626631854, with STD : 0.0
+accuracy_score on test : 0.653374233129, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.720626631854
+ - Score on test : 0.653374233129
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.743405275779
+ - Score on test : 0.678062678063
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.743405275779
+ - Score on test : 0.678062678063
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.279373368146
+ - Score on test : 0.346625766871
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.720626631854
+ - Score on test : 0.653374233129
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.448376824392
+ - Score on test : 0.310421316554
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.687361419069
+ - Score on test : 0.632978723404
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.809399477807
+ - Score on test : 0.730061349693
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.720626631854
+ - Score on test : 0.653374233129
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.279373368146
+ - Score on test : 0.346625766871
+
+
+ Classification took 0:00:14
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151910Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151910Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..b29fcd5c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151910Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for cq-hist with Adaboost, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.693251533742, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.693251533742
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.683544303797
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.683544303797
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.306748466258
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.693251533742
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.387232484355
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.705882352941
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.693251533742
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.306748466258
+
+
+ Classification took 0:00:20
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151926Results-KNN--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151926Results-KNN--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..4389a81b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151926Results-KNN--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with KNN, and 5 statistical iterations
+
+accuracy_score on train : 0.778067885117, with STD : 0.0
+accuracy_score on test : 0.638036809816, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 6
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.778067885117
+ - Score on test : 0.638036809816
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.783715012723
+ - Score on test : 0.64880952381
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.783715012723
+ - Score on test : 0.64880952381
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.221932114883
+ - Score on test : 0.361963190184
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.778067885117
+ - Score on test : 0.638036809816
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.556895575998
+ - Score on test : 0.276594631682
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.764267990074
+ - Score on test : 0.630057803468
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.804177545692
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.778067885117
+ - Score on test : 0.638036809816
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.221932114883
+ - Score on test : 0.361963190184
+
+
+ Classification took 0:00:35
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-151930Results-SGD--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-151930Results-SGD--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..f1b32bf5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-151930Results-SGD--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SGD, and 5 statistical iterations
+
+accuracy_score on train : 0.671018276762, with STD : 0.0
+accuracy_score on test : 0.650306748466, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.671018276762
+ - Score on test : 0.650306748466
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.688118811881
+ - Score on test : 0.666666666667
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.688118811881
+ - Score on test : 0.666666666667
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.328981723238
+ - Score on test : 0.349693251534
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.671018276762
+ - Score on test : 0.650306748466
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.34411186005
+ - Score on test : 0.302072296401
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.654117647059
+ - Score on test : 0.63687150838
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.725848563969
+ - Score on test : 0.699386503067
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.671018276762
+ - Score on test : 0.650306748466
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.328981723238
+ - Score on test : 0.349693251534
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152017Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152017Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..fbb90c04
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152017Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMLinear, and 5 statistical iterations
+
+accuracy_score on train : 0.998694516971, with STD : 0.0
+accuracy_score on test : 0.644171779141, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 4431
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.644171779141
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.646341463415
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.998692810458
+ - Score on test : 0.646341463415
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.355828220859
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.644171779141
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.997392433632
+ - Score on test : 0.288365265996
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.642424242424
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.650306748466
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.998694516971
+ - Score on test : 0.644171779141
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00130548302872
+ - Score on test : 0.355828220859
+
+
+ Classification took 0:00:50
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152022Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152022Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..e32ae43c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152022Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMRBF, and 5 statistical iterations
+
+accuracy_score on train : 0.912532637076, with STD : 0.0
+accuracy_score on test : 0.662576687117, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 2076
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.912532637076
+ - Score on test : 0.662576687117
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.911957950066
+ - Score on test : 0.664634146341
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.911957950066
+ - Score on test : 0.664634146341
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0874673629243
+ - Score on test : 0.337423312883
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.912532637076
+ - Score on test : 0.662576687117
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.825135590497
+ - Score on test : 0.325177853144
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.917989417989
+ - Score on test : 0.660606060606
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.906005221932
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.912532637076
+ - Score on test : 0.662576687117
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0874673629243
+ - Score on test : 0.337423312883
+
+
+ Classification took 0:00:55
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152029Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152029Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..911eef46
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152029Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMPoly, and 5 statistical iterations
+
+accuracy_score on train : 0.882506527415, with STD : 0.0
+accuracy_score on test : 0.69018404908, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 2640
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.882506527415
+ - Score on test : 0.69018404908
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.880636604775
+ - Score on test : 0.691131498471
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.880636604775
+ - Score on test : 0.691131498471
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.117493472585
+ - Score on test : 0.30981595092
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.882506527415
+ - Score on test : 0.69018404908
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.765388826201
+ - Score on test : 0.38037525648
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.894878706199
+ - Score on test : 0.689024390244
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.86684073107
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.882506527415
+ - Score on test : 0.69018404908
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.117493472585
+ - Score on test : 0.30981595092
+
+
+ Classification took 0:01:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152035Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152035Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..cef95c16
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152035Results-RandomForest--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with RandomForest, and 5 statistical iterations
+
+accuracy_score on train : 0.994778067885, with STD : 0.0
+accuracy_score on test : 0.696319018405, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 23
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.994778067885
+ - Score on test : 0.696319018405
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.994764397906
+ - Score on test : 0.683706070288
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.994764397906
+ - Score on test : 0.683706070288
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00522193211488
+ - Score on test : 0.303680981595
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.994778067885
+ - Score on test : 0.696319018405
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.989569627939
+ - Score on test : 0.393892771134
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997375328084
+ - Score on test : 0.713333333333
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.992167101828
+ - Score on test : 0.656441717791
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.994778067885
+ - Score on test : 0.696319018405
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00522193211488
+ - Score on test : 0.303680981595
+
+
+ Classification took 0:00:05
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152038Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152038Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..39427408
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152038Results-DecisionTree--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with DecisionTree, and 5 statistical iterations
+
+accuracy_score on train : 0.737597911227, with STD : 0.0
+accuracy_score on test : 0.696319018405, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - Decision Tree with max_depth : 1
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.737597911227
+ - Score on test : 0.696319018405
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.715700141443
+ - Score on test : 0.64
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.715700141443
+ - Score on test : 0.64
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.262402088773
+ - Score on test : 0.303680981595
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.737597911227
+ - Score on test : 0.696319018405
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.480936510756
+ - Score on test : 0.413393911463
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.780864197531
+ - Score on test : 0.785714285714
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.660574412533
+ - Score on test : 0.539877300613
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.737597911227
+ - Score on test : 0.696319018405
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.262402088773
+ - Score on test : 0.303680981595
+
+
+ Classification took 0:00:08
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152041Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152041Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..079b2340
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152041Results-Adaboost--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for lss-hist with Adaboost, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.645569620253
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.645569620253
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.313473915907
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.666666666667
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.625766871166
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:00:11
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152055Results-KNN--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152055Results-KNN--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..c9467f47
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152055Results-KNN--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with KNN, and 5 statistical iterations
+
+accuracy_score on train : 0.659268929504, with STD : 0.0
+accuracy_score on test : 0.659509202454, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - K nearest Neighbors with n_neighbors: 43
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.659268929504
+ - Score on test : 0.659509202454
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.607518796992
+ - Score on test : 0.621160409556
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.607518796992
+ - Score on test : 0.621160409556
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.340731070496
+ - Score on test : 0.340490797546
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.659268929504
+ - Score on test : 0.659509202454
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.330227012588
+ - Score on test : 0.325764407171
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.716312056738
+ - Score on test : 0.7
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.527415143603
+ - Score on test : 0.558282208589
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.659268929504
+ - Score on test : 0.659509202454
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.340731070496
+ - Score on test : 0.340490797546
+
+
+ Classification took 0:00:25
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152059Results-SGD--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152059Results-SGD--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..a7b4314b
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152059Results-SGD--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SGD, and 5 statistical iterations
+
+accuracy_score on train : 0.90861618799, with STD : 0.0
+accuracy_score on test : 0.766871165644, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.90861618799
+ - Score on test : 0.766871165644
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.915254237288
+ - Score on test : 0.788888888889
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.915254237288
+ - Score on test : 0.788888888889
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0913838120104
+ - Score on test : 0.233128834356
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.90861618799
+ - Score on test : 0.766871165644
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.827448957764
+ - Score on test : 0.545746908693
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.853273137698
+ - Score on test : 0.720812182741
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.986945169713
+ - Score on test : 0.871165644172
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.90861618799
+ - Score on test : 0.766871165644
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0913838120104
+ - Score on test : 0.233128834356
+
+
+ Classification took 0:00:03
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152120Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152120Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..6d2c843e
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152120Results-SVMPoly--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMPoly, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.763803680982, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Poly with C : 2640
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.763803680982
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757097791798
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.757097791798
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.236196319018
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.763803680982
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.528413454807
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.779220779221
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.736196319018
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.763803680982
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.236196319018
+
+
+ Classification took 0:00:24
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152128Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152128Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..a863164a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152128Results-SVMLinear--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMLinear, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.751533742331, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM Linear with C : 4431
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.751533742331
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.744479495268
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.744479495268
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.248466257669
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.751533742331
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.503836084816
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.766233766234
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.723926380368
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.751533742331
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.248466257669
+
+
+ Classification took 0:00:32
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152140Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152140Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
new file mode 100644
index 00000000..3bc3e572
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152140Results-SVMRBF--learnRate0.7-awaexp-lss-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for lss-hist with SVMRBF, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.536809815951, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 2
+
+Classifier configuration :
+ - SVM RBF with C : 4431
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.536809815951
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.678038379531
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.678038379531
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.463190184049
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.536809815951
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.153392997769
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.519607843137
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.975460122699
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.536809815951
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.463190184049
+
+
+ Classification took 0:00:45
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152325Results-Fusion-LateFusion-BayesianInference-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152325Results-Fusion-LateFusion-BayesianInference-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..b3b064dd
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152325Results-Fusion-LateFusion-BayesianInference-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,58 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.935770234987
+ -On Test : 0.765644171779
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Bayesian Inference using a weight for each view : 0.468344038717, 0.531655961283
+ -With monoview classifiers :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.935770234987 with STD : 0.0100919833369
+ - Score on test : 0.765644171779 with STD : 0.0205864561173
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.935715958222 with STD : 0.0104056406106
+ - Score on test : 0.774713716177 with STD : 0.0119980053689
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.935715958222 with STD : 0.0104056406106
+ - Score on test : 0.774713716177 with STD : 0.0119980053689
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0642297650131 with STD : 0.0100919833369
+ - Score on test : 0.234355828221 with STD : 0.0205864561173
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.935770234987 with STD : 0.0100919833369
+ - Score on test : 0.765644171779 with STD : 0.0205864561173
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.873353087698 with STD : 0.0208530817938
+ - Score on test : 0.536583295537 with STD : 0.0376442422002
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.937028186848 with STD : 0.0288106065084
+ - Score on test : 0.750864226564 with STD : 0.0439764143916
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.936292428198 with STD : 0.0343458666369
+ - Score on test : 0.80490797546 with STD : 0.043966804582
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.935770234987 with STD : 0.0100919833369
+ - Score on test : 0.765644171779 with STD : 0.0205864561173
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0642297650131 with STD : 0.0100919833369
+ - Score on test : 0.234355828221 with STD : 0.0205864561173
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152335Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152335Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..cb5b38c4
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152335Results-Fusion-LateFusion-MajorityVoting-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,58 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.921409921671
+ -On Test : 0.771165644172
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Majority Voting
+ -With monoview classifiers :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.921409921671 with STD : 0.010746274479
+ - Score on test : 0.771165644172 with STD : 0.0120533022726
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.920511234097 with STD : 0.0103233115923
+ - Score on test : 0.769568689392 with STD : 0.0219732543554
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.920511234097 with STD : 0.0103233115923
+ - Score on test : 0.769568689392 with STD : 0.0219732543554
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.078590078329 with STD : 0.010746274479
+ - Score on test : 0.228834355828 with STD : 0.0120533022726
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.921409921671 with STD : 0.010746274479
+ - Score on test : 0.771165644172 with STD : 0.0120533022726
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.848090681947 with STD : 0.0198951734938
+ - Score on test : 0.55154560512 with STD : 0.0269686279704
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.936431809467 with STD : 0.0523933339542
+ - Score on test : 0.780105771849 with STD : 0.0530162883504
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.910704960836 with STD : 0.0519942932131
+ - Score on test : 0.771779141104 with STD : 0.0899479401625
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.921409921671 with STD : 0.010746274479
+ - Score on test : 0.771165644172 with STD : 0.0120533022726
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.078590078329 with STD : 0.010746274479
+ - Score on test : 0.228834355828 with STD : 0.0120533022726
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152340Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152340Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..f84279f8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152340Results-Fusion-LateFusion-SVMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,58 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.925587467363
+ -On Test : 0.720858895706
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with SVM for linear
+ -With monoview classifiers :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.925587467363 with STD : 0.0
+ - Score on test : 0.720858895706 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.921595598349 with STD : 0.0
+ - Score on test : 0.693602693603 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.921595598349 with STD : 0.0
+ - Score on test : 0.693602693603 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0744125326371 with STD : 0.0
+ - Score on test : 0.279141104294 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.925587467363 with STD : 0.0
+ - Score on test : 0.720858895706 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.85562241482 with STD : 0.0
+ - Score on test : 0.448879201248 with STD : 5.55111512313e-17
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.973837209302 with STD : 0.0
+ - Score on test : 0.768656716418 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.874673629243 with STD : 0.0
+ - Score on test : 0.631901840491 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.925587467363 with STD : 0.0
+ - Score on test : 0.720858895706 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0744125326371 with STD : 0.0
+ - Score on test : 0.279141104294 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152359Results-Fusion-LateFusion-SCMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152359Results-Fusion-LateFusion-SCMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..63641042
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152359Results-Fusion-LateFusion-SCMForLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,58 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.960835509138
+ -On Test : 0.794478527607
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with SCM for linear with max_attributes : 12, p : 0.310533606766 model_type : disjunction has chosen 1 rule(s)
+ -With monoview classifiers :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.960835509138 with STD : 0.0
+ - Score on test : 0.794478527607 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.961038961039 with STD : 1.11022302463e-16
+ - Score on test : 0.804664723032 with STD : 1.11022302463e-16
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.961038961039 with STD : 1.11022302463e-16
+ - Score on test : 0.804664723032 with STD : 1.11022302463e-16
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0391644908616 with STD : 0.0
+ - Score on test : 0.205521472393 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.960835509138 with STD : 0.0
+ - Score on test : 0.794478527607 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.9217212877 with STD : 0.0
+ - Score on test : 0.592186568158 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.956072351421 with STD : 0.0
+ - Score on test : 0.766666666667 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.966057441253 with STD : 0.0
+ - Score on test : 0.846625766871 with STD : 1.11022302463e-16
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.960835509138 with STD : 0.0
+ - Score on test : 0.794478527607 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0391644908616 with STD : 0.0
+ - Score on test : 0.205521472393 with STD : 2.77555756156e-17
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152455Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152455Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..500b55af
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152455Results-Fusion-LateFusion-WeightedLinear-SGD-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,58 @@
+ Result for Multiview classification with LateFusion
+
+Average accuracy_score :
+ -On Train : 0.5
+ -On Test : 0.5
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : LateFusion with Weighted linear using a weight for each view : 0.880915616157, 1.0
+ -With monoview classifiers :
+ - SGDClassifier with loss : modified_huber, penalty : l2
+ - SGDClassifier with loss : modified_huber, penalty : elasticnet
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.666666666667 with STD : 0.0
+ - Score on test : 0.666666666667 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.666666666667 with STD : 0.0
+ - Score on test : 0.666666666667 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 1.0 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152543Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152543Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..e6c95e8c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152543Results-Fusion-EarlyFusion-WeightedLinear-DecisionTree-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.81592689295
+ -On Test : 0.761349693252
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - Decision Tree with max_depth : 3
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.761349693252 with STD : 0.00122699386503
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.814229249012 with STD : 0.0
+ - Score on test : 0.763814289686 with STD : 0.000355852098971
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.814229249012 with STD : 0.0
+ - Score on test : 0.763814289686 with STD : 0.000355852098971
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.18407310705 with STD : 0.0
+ - Score on test : 0.238650306748 with STD : 0.00122699386503
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.761349693252 with STD : 0.00122699386503
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.63195934458 with STD : 0.0
+ - Score on test : 0.522827042555 with STD : 0.0023951243455
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.821808510638 with STD : 1.11022302463e-16
+ - Score on test : 0.756031838762 with STD : 0.00308164159486
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.806788511749 with STD : 0.0
+ - Score on test : 0.771779141104 with STD : 0.00245398773006
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.81592689295 with STD : 0.0
+ - Score on test : 0.761349693252 with STD : 0.00122699386503
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.18407310705 with STD : 0.0
+ - Score on test : 0.238650306748 with STD : 0.00122699386503
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152655Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152655Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..a24b4bc1
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152655Results-Fusion-EarlyFusion-WeightedLinear-Adaboost-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.676073619632
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - Adaboost with num_esimators : 2, base_estimators : DecisionTreeClassifier
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.676073619632 with STD : 0.0103751745554
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.670346314584 with STD : 0.0115934576486
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.670346314584 with STD : 0.0115934576486
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.323926380368 with STD : 0.0103751745554
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.676073619632 with STD : 0.0103751745554
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.352483888102 with STD : 0.020663312867
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.682484956007 with STD : 0.0117935360735
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.658895705521 with STD : 0.0171779141104
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.676073619632 with STD : 0.0103751745554
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.323926380368 with STD : 0.0103751745554
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-152948Results-Fusion-EarlyFusion-WeightedLinear-KNN-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-152948Results-Fusion-EarlyFusion-WeightedLinear-KNN-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..8376d578
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-152948Results-Fusion-EarlyFusion-WeightedLinear-KNN-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 1.0
+ -On Test : 0.592024539877
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - K nearest Neighbors with n_neighbors: 1.0
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.592024539877 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.583072100313 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.583072100313 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.407975460123 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.592024539877 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.184219031546 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.596153846154 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.570552147239 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0 with STD : 0.0
+ - Score on test : 0.592024539877 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.407975460123 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-153048Results-Fusion-EarlyFusion-WeightedLinear-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-153048Results-Fusion-EarlyFusion-WeightedLinear-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..7b0b079c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-153048Results-Fusion-EarlyFusion-WeightedLinear-SGD-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.898955613577
+ -On Test : 0.753987730061
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - SGDClassifier with loss : log, penalty : l2
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.898955613577 with STD : 0.0718210275075
+ - Score on test : 0.753987730061 with STD : 0.037084861987
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.908951683797 with STD : 0.0540728241158
+ - Score on test : 0.763621260416 with STD : 0.00772558921165
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.908951683797 with STD : 0.0540728241158
+ - Score on test : 0.763621260416 with STD : 0.00772558921165
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.101044386423 with STD : 0.0718210275075
+ - Score on test : 0.246012269939 with STD : 0.037084861987
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.898955613577 with STD : 0.0718210275075
+ - Score on test : 0.753987730061 with STD : 0.037084861987
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.813276317483 with STD : 0.116263516032
+ - Score on test : 0.524172372511 with STD : 0.0514608194751
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.878849832215 with STD : 0.107924714973
+ - Score on test : 0.753947637281 with STD : 0.0775135641749
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.952480417755 with STD : 0.0242918085632
+ - Score on test : 0.791411042945 with STD : 0.0870214636883
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.898955613577 with STD : 0.0718210275075
+ - Score on test : 0.753987730061 with STD : 0.037084861987
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.101044386423 with STD : 0.0718210275075
+ - Score on test : 0.246012269939 with STD : 0.037084861987
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-153127Results-Fusion-EarlyFusion-WeightedLinear-SCM-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-153127Results-Fusion-EarlyFusion-WeightedLinear-SCM-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..3c1b7f1c
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-153127Results-Fusion-EarlyFusion-WeightedLinear-SCM-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.5
+ -On Test : 0.5
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - SCM with max_attributes : 1
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.0 with STD : 0.0
+ - Score on test : 0.0 with STD : 0.0
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.5 with STD : 0.0
+ - Score on test : 0.5 with STD : 0.0
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-153136Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-cq-hist-lss-hist--learnRate0.7-awaexp.txt b/Code/MonoMutliViewClassifiers/Results/20170922-153136Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-cq-hist-lss-hist--learnRate0.7-awaexp.txt
new file mode 100644
index 00000000..97cb43d5
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-153136Results-Fusion-EarlyFusion-WeightedLinear-RandomForest-cq-hist-lss-hist--learnRate0.7-awaexp.txt
@@ -0,0 +1,56 @@
+ Result for Multiview classification with EarlyFusion
+
+Average accuracy_score :
+ -On Train : 0.915143603133
+ -On Test : 0.741717791411
+
+Dataset info :
+ -Database name : awaexp
+ -Labels :
+ -Views : cq-hist, lss-hist
+ -2 folds
+
+Classification configuration :
+ -Algorithm used : EarlyFusion with weighted concatenation, using weights : 0.880915616157, 1.0 with monoview classifier :
+ - Random Forest with num_esimators : 25, max_depth : 5
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.915143603133 with STD : 0.00340428324031
+ - Score on test : 0.741717791411 with STD : 0.00785659415636
+
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.911464433058 with STD : 0.00359680668862
+ - Score on test : 0.738297988431 with STD : 0.00916250860792
+
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.911464433058 with STD : 0.00359680668862
+ - Score on test : 0.738297988431 with STD : 0.00916250860792
+
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0848563968668 with STD : 0.00340428324031
+ - Score on test : 0.258282208589 with STD : 0.00785659415636
+
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.915143603133 with STD : 0.00340428324031
+ - Score on test : 0.741717791411 with STD : 0.00785659415636
+
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.833242709103 with STD : 0.00688042213647
+ - Score on test : 0.483653710409 with STD : 0.0156462607003
+
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.952834664301 with STD : 0.00775427616681
+ - Score on test : 0.7481099822 with STD : 0.00693626746348
+
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.87362924282 with STD : 0.00749488255583
+ - Score on test : 0.728834355828 with STD : 0.0136632254303
+
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.915143603133 with STD : 0.00340428324031
+ - Score on test : 0.741717791411 with STD : 0.00785659415636
+
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0848563968668 with STD : 0.00340428324031
+ - Score on test : 0.258282208589 with STD : 0.00785659415636
+
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154229-CMultiV-Benchmark-cq-hist_lss-hist_phog-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-154229-CMultiV-Benchmark-cq-hist_lss-hist_phog-hist-awaexp-LOG.log
new file mode 100644
index 00000000..e69de29b
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154253-CMultiV-Benchmark-cq-hist_lss-hist_phog-hist-awaexp-LOG.log b/Code/MonoMutliViewClassifiers/Results/20170922-154253-CMultiV-Benchmark-cq-hist_lss-hist_phog-hist-awaexp-LOG.log
new file mode 100644
index 00000000..b724a44a
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-154253-CMultiV-Benchmark-cq-hist_lss-hist_phog-hist-awaexp-LOG.log
@@ -0,0 +1,708 @@
+2017-09-22 15:42:59,585 DEBUG: Start: Creating 2 temporary datasets for multiprocessing
+2017-09-22 15:42:59,585 WARNING: WARNING : /!\ This may use a lot of HDD storage space : 0.084322 Gbytes /!\
+2017-09-22 15:43:03,867 DEBUG: Start: Creating datasets for multiprocessing
+2017-09-22 15:43:03,870 INFO: Start: Finding all available mono- & multiview algorithms
+2017-09-22 15:43:03,948 DEBUG: Start: Loading data
+2017-09-22 15:43:03,948 DEBUG: Start: Loading data
+2017-09-22 15:43:03,963 DEBUG: Done: Loading data
+2017-09-22 15:43:03,963 DEBUG: Done: Loading data
+2017-09-22 15:43:03,964 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2017-09-22 15:43:03,964 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2017-09-22 15:43:03,964 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:43:03,964 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:43:03,995 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:43:03,995 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:43:03,995 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:43:03,995 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:43:03,995 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:43:03,995 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:43:03,995 DEBUG: Start: RandomSearch best settings with 20 iterations for Adaboost
+2017-09-22 15:43:03,995 DEBUG: Start: RandomSearch best settings with 20 iterations for DecisionTree
+2017-09-22 15:43:56,722 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:43:56,723 DEBUG: Start: Training
+2017-09-22 15:43:57,025 DEBUG: Done: Training
+2017-09-22 15:43:57,025 DEBUG: Start: Predicting
+2017-09-22 15:43:57,038 DEBUG: Done: Predicting
+2017-09-22 15:43:57,038 DEBUG: Info: Time for training and predicting: 53.0896990299[s]
+2017-09-22 15:43:57,038 DEBUG: Start: Getting Results
+2017-09-22 15:43:57,066 DEBUG: Done: Getting Results
+2017-09-22 15:43:57,067 INFO: Classification on awaexp database for cq-hist with DecisionTree, and 5 statistical iterations
+
+accuracy_score on train : 0.779373368146, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - 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 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.779373368146
+ - Score on test : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.784713375796
+ - Score on test : 0.66275659824
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.784713375796
+ - Score on test : 0.66275659824
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.220626631854
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.779373368146
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.559435542669
+ - Score on test : 0.295733401498
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.766169154229
+ - Score on test : 0.634831460674
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.804177545692
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.779373368146
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.220626631854
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:53
+2017-09-22 15:43:57,067 INFO: Done: Result Analysis
+2017-09-22 15:44:05,595 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:44:05,596 DEBUG: Start: Training
+2017-09-22 15:44:06,345 DEBUG: Done: Training
+2017-09-22 15:44:06,345 DEBUG: Start: Predicting
+2017-09-22 15:44:06,361 DEBUG: Done: Predicting
+2017-09-22 15:44:06,361 DEBUG: Info: Time for training and predicting: 62.412541151[s]
+2017-09-22 15:44:06,361 DEBUG: Start: Getting Results
+2017-09-22 15:44:06,388 DEBUG: Done: Getting Results
+2017-09-22 15:44:06,388 INFO: Classification on awaexp database for cq-hist with Adaboost, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.644171779141, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 5, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None,
+ min_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.644171779141
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.652694610778
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.652694610778
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.355828220859
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.644171779141
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.288691471152
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.637426900585
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.644171779141
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.355828220859
+
+
+ Classification took 0:01:02
+2017-09-22 15:44:06,389 INFO: Done: Result Analysis
+2017-09-22 15:44:06,544 DEBUG: Start: Loading data
+2017-09-22 15:44:06,544 DEBUG: Start: Loading data
+2017-09-22 15:44:06,560 DEBUG: Done: Loading data
+2017-09-22 15:44:06,560 DEBUG: Done: Loading data
+2017-09-22 15:44:06,561 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2017-09-22 15:44:06,561 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2017-09-22 15:44:06,561 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:44:06,561 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:44:06,590 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:44:06,590 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:44:06,591 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:44:06,591 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:44:06,591 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:44:06,591 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:44:06,591 DEBUG: Start: RandomSearch best settings with 20 iterations for RandomForest
+2017-09-22 15:44:06,591 DEBUG: Start: RandomSearch best settings with 20 iterations for KNN
+2017-09-22 15:44:28,388 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:44:28,388 DEBUG: Start: Training
+2017-09-22 15:44:28,716 DEBUG: Done: Training
+2017-09-22 15:44:28,716 DEBUG: Start: Predicting
+2017-09-22 15:44:28,789 DEBUG: Done: Predicting
+2017-09-22 15:44:28,789 DEBUG: Info: Time for training and predicting: 22.2440979481[s]
+2017-09-22 15:44:28,789 DEBUG: Start: Getting Results
+2017-09-22 15:44:28,818 DEBUG: Done: Getting Results
+2017-09-22 15:44:28,818 INFO: Classification on awaexp database for cq-hist with RandomForest, and 5 statistical iterations
+
+accuracy_score on train : 0.993472584856, with STD : 0.0
+accuracy_score on test : 0.766871165644, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 24
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.766871165644
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.993429697766
+ - Score on test : 0.748344370861
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.993429697766
+ - Score on test : 0.748344370861
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.233128834356
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.766871165644
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.987029282303
+ - Score on test : 0.539623742011
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.812949640288
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.986945169713
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.766871165644
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.233128834356
+
+
+ Classification took 0:00:22
+2017-09-22 15:44:28,818 INFO: Done: Result Analysis
+2017-09-22 15:44:57,971 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:44:57,971 DEBUG: Start: Training
+2017-09-22 15:44:58,019 DEBUG: Done: Training
+2017-09-22 15:44:58,019 DEBUG: Start: Predicting
+2017-09-22 15:45:05,408 DEBUG: Done: Predicting
+2017-09-22 15:45:05,408 DEBUG: Info: Time for training and predicting: 58.8636100292[s]
+2017-09-22 15:45:05,408 DEBUG: Start: Getting Results
+2017-09-22 15:45:05,436 DEBUG: Done: Getting Results
+2017-09-22 15:45:05,436 INFO: Classification on awaexp database for cq-hist with KNN, and 5 statistical iterations
+
+accuracy_score on train : 0.668407310705, with STD : 0.0
+accuracy_score on test : 0.622699386503, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - 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 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.668407310705
+ - Score on test : 0.622699386503
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.700471698113
+ - Score on test : 0.672
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.700471698113
+ - Score on test : 0.672
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.331592689295
+ - Score on test : 0.377300613497
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.668407310705
+ - Score on test : 0.622699386503
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.344810106024
+ - Score on test : 0.25729991053
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.638709677419
+ - Score on test : 0.594339622642
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.77545691906
+ - Score on test : 0.773006134969
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.668407310705
+ - Score on test : 0.622699386503
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.331592689295
+ - Score on test : 0.377300613497
+
+
+ Classification took 0:00:58
+2017-09-22 15:45:05,436 INFO: Done: Result Analysis
+2017-09-22 15:45:05,529 DEBUG: Start: Loading data
+2017-09-22 15:45:05,529 DEBUG: Start: Loading data
+2017-09-22 15:45:05,541 DEBUG: Done: Loading data
+2017-09-22 15:45:05,542 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMLinear
+2017-09-22 15:45:05,542 DEBUG: Done: Loading data
+2017-09-22 15:45:05,542 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:45:05,542 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SGD
+2017-09-22 15:45:05,542 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:45:05,562 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:45:05,562 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:45:05,562 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:45:05,563 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMLinear
+2017-09-22 15:45:05,563 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:45:05,563 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:45:05,563 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:45:05,563 DEBUG: Start: RandomSearch best settings with 20 iterations for SGD
+2017-09-22 15:45:16,351 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:45:16,352 DEBUG: Start: Training
+2017-09-22 15:45:16,406 DEBUG: Done: Training
+2017-09-22 15:45:16,406 DEBUG: Start: Predicting
+2017-09-22 15:45:16,416 DEBUG: Done: Predicting
+2017-09-22 15:45:16,416 DEBUG: Info: Time for training and predicting: 10.8862061501[s]
+2017-09-22 15:45:16,416 DEBUG: Start: Getting Results
+2017-09-22 15:45:16,448 DEBUG: Done: Getting Results
+2017-09-22 15:45:16,449 INFO: Classification on awaexp database for cq-hist with SGD, and 5 statistical iterations
+
+accuracy_score on train : 0.697127937337, with STD : 0.0
+accuracy_score on test : 0.644171779141, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - 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 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.697127937337
+ - Score on test : 0.644171779141
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.693121693122
+ - Score on test : 0.656804733728
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.693121693122
+ - Score on test : 0.656804733728
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.302872062663
+ - Score on test : 0.355828220859
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.697127937337
+ - Score on test : 0.644171779141
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.39439032837
+ - Score on test : 0.289128138403
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.702412868633
+ - Score on test : 0.634285714286
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.68407310705
+ - Score on test : 0.680981595092
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.697127937337
+ - Score on test : 0.644171779141
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.302872062663
+ - Score on test : 0.355828220859
+
+
+ Classification took 0:00:10
+2017-09-22 15:45:16,449 INFO: Done: Result Analysis
+2017-09-22 15:46:38,006 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:46:38,006 DEBUG: Start: Training
+2017-09-22 15:46:43,548 DEBUG: Done: Training
+2017-09-22 15:46:43,549 DEBUG: Start: Predicting
+2017-09-22 15:46:46,457 DEBUG: Done: Predicting
+2017-09-22 15:46:46,457 DEBUG: Info: Time for training and predicting: 100.927749872[s]
+2017-09-22 15:46:46,457 DEBUG: Start: Getting Results
+2017-09-22 15:46:46,485 DEBUG: Done: Getting Results
+2017-09-22 15:46:46,485 INFO: Classification on awaexp database for cq-hist with SVMLinear, and 5 statistical iterations
+
+accuracy_score on train : 0.997389033943, with STD : 0.0
+accuracy_score on test : 0.604294478528, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3750
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.604294478528
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997382198953
+ - Score on test : 0.605504587156
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.997382198953
+ - Score on test : 0.605504587156
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00261096605744
+ - Score on test : 0.395705521472
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.604294478528
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.994791631253
+ - Score on test : 0.208592882586
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.603658536585
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.994778067885
+ - Score on test : 0.60736196319
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.604294478528
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00261096605744
+ - Score on test : 0.395705521472
+
+
+ Classification took 0:01:40
+2017-09-22 15:46:46,485 INFO: Done: Result Analysis
+2017-09-22 15:46:46,613 DEBUG: Start: Loading data
+2017-09-22 15:46:46,613 DEBUG: Start: Loading data
+2017-09-22 15:46:46,629 DEBUG: Done: Loading data
+2017-09-22 15:46:46,629 DEBUG: Done: Loading data
+2017-09-22 15:46:46,629 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMPoly
+2017-09-22 15:46:46,629 DEBUG: Info: Classification - Database:awaexp Feature:cq-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : SVMRBF
+2017-09-22 15:46:46,630 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:46:46,630 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:46:46,663 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:46:46,663 DEBUG: Info: Shape X_train:(766, 2688), Length of y_train:766
+2017-09-22 15:46:46,663 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:46:46,663 DEBUG: Info: Shape X_test:(326, 2688), Length of y_test:326
+2017-09-22 15:46:46,663 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:46:46,663 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:46:46,663 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMRBF
+2017-09-22 15:46:46,664 DEBUG: Start: RandomSearch best settings with 20 iterations for SVMPoly
+2017-09-22 15:48:48,464 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:48:48,465 DEBUG: Start: Training
+2017-09-22 15:48:56,540 DEBUG: Done: Training
+2017-09-22 15:48:56,540 DEBUG: Start: Predicting
+2017-09-22 15:49:00,828 DEBUG: Done: Predicting
+2017-09-22 15:49:00,829 DEBUG: Info: Time for training and predicting: 134.21481204[s]
+2017-09-22 15:49:00,829 DEBUG: Start: Getting Results
+2017-09-22 15:49:00,858 DEBUG: Done: Getting Results
+2017-09-22 15:49:00,858 INFO: Classification on awaexp database for cq-hist with SVMRBF, and 5 statistical iterations
+
+accuracy_score on train : 0.83681462141, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM RBF with C : 633
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.83681462141
+ - Score on test : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.835309617918
+ - Score on test : 0.652173913043
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.835309617918
+ - Score on test : 0.652173913043
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.16318537859
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.83681462141
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.673741780586
+ - Score on test : 0.31297768823
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.843085106383
+ - Score on test : 0.660377358491
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.827676240209
+ - Score on test : 0.644171779141
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.83681462141
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.16318537859
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:02:14
+2017-09-22 15:49:00,858 INFO: Done: Result Analysis
+2017-09-22 15:49:19,904 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:49:19,904 DEBUG: Start: Training
+2017-09-22 15:49:25,768 DEBUG: Done: Training
+2017-09-22 15:49:25,769 DEBUG: Start: Predicting
+2017-09-22 15:49:28,889 DEBUG: Done: Predicting
+2017-09-22 15:49:28,890 DEBUG: Info: Time for training and predicting: 162.275981188[s]
+2017-09-22 15:49:28,890 DEBUG: Start: Getting Results
+2017-09-22 15:49:28,917 DEBUG: Done: Getting Results
+2017-09-22 15:49:28,917 INFO: Classification on awaexp database for cq-hist with SVMPoly, and 5 statistical iterations
+
+accuracy_score on train : 0.946475195822, with STD : 0.0
+accuracy_score on test : 0.650306748466, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Poly with C : 7894
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.650306748466
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.945695364238
+ - Score on test : 0.654545454545
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.945695364238
+ - Score on test : 0.654545454545
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.349693251534
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.650306748466
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.893318905601
+ - Score on test : 0.300704053397
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.959677419355
+ - Score on test : 0.646706586826
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.932114882507
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.650306748466
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.349693251534
+
+
+ Classification took 0:02:42
+2017-09-22 15:49:28,917 INFO: Done: Result Analysis
+2017-09-22 15:49:29,086 DEBUG: Start: Loading data
+2017-09-22 15:49:29,086 DEBUG: Start: Loading data
+2017-09-22 15:49:29,099 DEBUG: Done: Loading data
+2017-09-22 15:49:29,099 DEBUG: Done: Loading data
+2017-09-22 15:49:29,099 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : DecisionTree
+2017-09-22 15:49:29,099 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : Adaboost
+2017-09-22 15:49:29,099 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:49:29,099 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:49:29,129 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:49:29,129 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:49:29,129 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:49:29,129 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:49:29,129 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:49:29,129 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:49:29,129 DEBUG: Start: RandomSearch best settings with 20 iterations for Adaboost
+2017-09-22 15:49:29,129 DEBUG: Start: RandomSearch best settings with 20 iterations for DecisionTree
+2017-09-22 15:50:02,008 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:50:02,008 DEBUG: Start: Training
+2017-09-22 15:50:02,169 DEBUG: Done: Training
+2017-09-22 15:50:02,169 DEBUG: Start: Predicting
+2017-09-22 15:50:02,179 DEBUG: Done: Predicting
+2017-09-22 15:50:02,179 DEBUG: Info: Time for training and predicting: 33.0920088291[s]
+2017-09-22 15:50:02,179 DEBUG: Start: Getting Results
+2017-09-22 15:50:02,208 DEBUG: Done: Getting Results
+2017-09-22 15:50:02,208 INFO: Classification on awaexp database for lss-hist with DecisionTree, and 5 statistical iterations
+
+accuracy_score on train : 0.793733681462, with STD : 0.0
+accuracy_score on test : 0.711656441718, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - 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 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.793733681462
+ - Score on test : 0.711656441718
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.784741144414
+ - Score on test : 0.686666666667
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.784741144414
+ - Score on test : 0.686666666667
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.206266318538
+ - Score on test : 0.288343558282
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.793733681462
+ - Score on test : 0.711656441718
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.589528644124
+ - Score on test : 0.42880308882
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.820512820513
+ - Score on test : 0.751824817518
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.751958224543
+ - Score on test : 0.631901840491
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.793733681462
+ - Score on test : 0.711656441718
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.206266318538
+ - Score on test : 0.288343558282
+
+
+ Classification took 0:00:33
+2017-09-22 15:50:02,208 INFO: Done: Result Analysis
+2017-09-22 15:50:06,998 DEBUG: Done: RandomSearch best settings
+2017-09-22 15:50:06,998 DEBUG: Start: Training
+2017-09-22 15:50:07,412 DEBUG: Done: Training
+2017-09-22 15:50:07,413 DEBUG: Start: Predicting
+2017-09-22 15:50:07,424 DEBUG: Done: Predicting
+2017-09-22 15:50:07,425 DEBUG: Info: Time for training and predicting: 38.3376348019[s]
+2017-09-22 15:50:07,425 DEBUG: Start: Getting Results
+2017-09-22 15:50:07,452 DEBUG: Done: Getting Results
+2017-09-22 15:50:07,452 INFO: Classification on awaexp database for lss-hist with Adaboost, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.699386503067, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : lss-hist View shape : (1092, 2000)
+ - Learning Rate : 0.7
+ - Labels used :
+ - 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_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.699386503067
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.706586826347
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.706586826347
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.300613496933
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.699386503067
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.399254162232
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.690058479532
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.723926380368
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.699386503067
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.300613496933
+
+
+ Classification took 0:00:38
+2017-09-22 15:50:07,453 INFO: Done: Result Analysis
+2017-09-22 15:50:07,522 DEBUG: Start: Loading data
+2017-09-22 15:50:07,522 DEBUG: Start: Loading data
+2017-09-22 15:50:07,533 DEBUG: Done: Loading data
+2017-09-22 15:50:07,533 DEBUG: Done: Loading data
+2017-09-22 15:50:07,533 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : KNN
+2017-09-22 15:50:07,533 DEBUG: Info: Classification - Database:awaexp Feature:lss-hist train_size:0.7, CrossValidation k-folds:5, cores:1, algorithm : RandomForest
+2017-09-22 15:50:07,533 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:50:07,533 DEBUG: Start: Determine Train/Test split for iteration 1
+2017-09-22 15:50:07,562 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:50:07,562 DEBUG: Info: Shape X_train:(766, 2000), Length of y_train:766
+2017-09-22 15:50:07,562 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:50:07,562 DEBUG: Info: Shape X_test:(326, 2000), Length of y_test:326
+2017-09-22 15:50:07,562 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:50:07,562 DEBUG: Done: Determine Train/Test split
+2017-09-22 15:50:07,562 DEBUG: Start: RandomSearch best settings with 20 iterations for RandomForest
+2017-09-22 15:50:07,562 DEBUG: Start: RandomSearch best settings with 20 iterations for KNN
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154357Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-154357Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..a5b7a000
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-154357Results-DecisionTree--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with DecisionTree, and 5 statistical iterations
+
+accuracy_score on train : 0.779373368146, with STD : 0.0
+accuracy_score on test : 0.647239263804, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - 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 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.779373368146
+ - Score on test : 0.647239263804
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.784713375796
+ - Score on test : 0.66275659824
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.784713375796
+ - Score on test : 0.66275659824
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.220626631854
+ - Score on test : 0.352760736196
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.779373368146
+ - Score on test : 0.647239263804
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.559435542669
+ - Score on test : 0.295733401498
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.766169154229
+ - Score on test : 0.634831460674
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.804177545692
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.779373368146
+ - Score on test : 0.647239263804
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.220626631854
+ - Score on test : 0.352760736196
+
+
+ Classification took 0:00:53
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154406Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-154406Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..0715d5a8
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-154406Results-Adaboost--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,55 @@
+Classification on awaexp database for cq-hist with Adaboost, and 5 statistical iterations
+
+accuracy_score on train : 1.0, with STD : 0.0
+accuracy_score on test : 0.644171779141, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Adaboost with num_esimators : 5, base_estimators : DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
+ max_features=None, max_leaf_nodes=None,
+ min_impurity_split=1e-07, min_samples_leaf=1,
+ min_samples_split=2, min_weight_fraction_leaf=0.0,
+ presort=False, random_state=None, splitter='best')
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.644171779141
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.652694610778
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.652694610778
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.355828220859
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.644171779141
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.288691471152
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.637426900585
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.668711656442
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.644171779141
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0
+ - Score on test : 0.355828220859
+
+
+ Classification took 0:01:02
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154428Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-154428Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..c2c14404
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-154428Results-RandomForest--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with RandomForest, and 5 statistical iterations
+
+accuracy_score on train : 0.993472584856, with STD : 0.0
+accuracy_score on test : 0.766871165644, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - Random Forest with num_esimators : 20, max_depth : 24
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.766871165644
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.993429697766
+ - Score on test : 0.748344370861
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.993429697766
+ - Score on test : 0.748344370861
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.233128834356
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.766871165644
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.987029282303
+ - Score on test : 0.539623742011
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.812949640288
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.986945169713
+ - Score on test : 0.693251533742
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.993472584856
+ - Score on test : 0.766871165644
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0065274151436
+ - Score on test : 0.233128834356
+
+
+ Classification took 0:00:22
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154505Results-KNN--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-154505Results-KNN--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..1f112726
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-154505Results-KNN--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with KNN, and 5 statistical iterations
+
+accuracy_score on train : 0.668407310705, with STD : 0.0
+accuracy_score on test : 0.622699386503, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - 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 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.668407310705
+ - Score on test : 0.622699386503
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.700471698113
+ - Score on test : 0.672
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.700471698113
+ - Score on test : 0.672
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.331592689295
+ - Score on test : 0.377300613497
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.668407310705
+ - Score on test : 0.622699386503
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.344810106024
+ - Score on test : 0.25729991053
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.638709677419
+ - Score on test : 0.594339622642
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.77545691906
+ - Score on test : 0.773006134969
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.668407310705
+ - Score on test : 0.622699386503
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.331592689295
+ - Score on test : 0.377300613497
+
+
+ Classification took 0:00:58
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154516Results-SGD--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-154516Results-SGD--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..d1e5592f
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-154516Results-SGD--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SGD, and 5 statistical iterations
+
+accuracy_score on train : 0.697127937337, with STD : 0.0
+accuracy_score on test : 0.644171779141, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - 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 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.697127937337
+ - Score on test : 0.644171779141
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.693121693122
+ - Score on test : 0.656804733728
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.693121693122
+ - Score on test : 0.656804733728
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.302872062663
+ - Score on test : 0.355828220859
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.697127937337
+ - Score on test : 0.644171779141
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.39439032837
+ - Score on test : 0.289128138403
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.702412868633
+ - Score on test : 0.634285714286
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.68407310705
+ - Score on test : 0.680981595092
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.697127937337
+ - Score on test : 0.644171779141
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.302872062663
+ - Score on test : 0.355828220859
+
+
+ Classification took 0:00:10
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154646Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-154646Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..bbbcc612
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-154646Results-SVMLinear--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMLinear, and 5 statistical iterations
+
+accuracy_score on train : 0.997389033943, with STD : 0.0
+accuracy_score on test : 0.604294478528, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Linear with C : 3750
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.604294478528
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.997382198953
+ - Score on test : 0.605504587156
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.997382198953
+ - Score on test : 0.605504587156
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.00261096605744
+ - Score on test : 0.395705521472
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.604294478528
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.994791631253
+ - Score on test : 0.208592882586
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 1.0
+ - Score on test : 0.603658536585
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.994778067885
+ - Score on test : 0.60736196319
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.997389033943
+ - Score on test : 0.604294478528
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.00261096605744
+ - Score on test : 0.395705521472
+
+
+ Classification took 0:01:40
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154900Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-154900Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..189c73af
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-154900Results-SVMRBF--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMRBF, and 5 statistical iterations
+
+accuracy_score on train : 0.83681462141, with STD : 0.0
+accuracy_score on test : 0.656441717791, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM RBF with C : 633
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.83681462141
+ - Score on test : 0.656441717791
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.835309617918
+ - Score on test : 0.652173913043
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.835309617918
+ - Score on test : 0.652173913043
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.16318537859
+ - Score on test : 0.343558282209
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.83681462141
+ - Score on test : 0.656441717791
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.673741780586
+ - Score on test : 0.31297768823
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.843085106383
+ - Score on test : 0.660377358491
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.827676240209
+ - Score on test : 0.644171779141
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.83681462141
+ - Score on test : 0.656441717791
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.16318537859
+ - Score on test : 0.343558282209
+
+
+ Classification took 0:02:14
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/Results/20170922-154928Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt b/Code/MonoMutliViewClassifiers/Results/20170922-154928Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
new file mode 100644
index 00000000..9d8ea6da
--- /dev/null
+++ b/Code/MonoMutliViewClassifiers/Results/20170922-154928Results-SVMPoly--learnRate0.7-awaexp-cq-hist.txt
@@ -0,0 +1,51 @@
+Classification on awaexp database for cq-hist with SVMPoly, and 5 statistical iterations
+
+accuracy_score on train : 0.946475195822, with STD : 0.0
+accuracy_score on test : 0.650306748466, with STD : 0.0
+
+Database configuration :
+ - Database name : awaexp
+ - View name : cq-hist View shape : (1092, 2688)
+ - Learning Rate : 0.7
+ - Labels used :
+ - Number of cross validation folds : 5
+
+Classifier configuration :
+ - SVM Poly with C : 7894
+ - Executed on 1 core(s)
+ - Got configuration using randomized search with 20 iterations
+
+
+ For Accuracy score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.650306748466
+ For F1 score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.945695364238
+ - Score on test : 0.654545454545
+ For F-beta score using None as sample_weights, None as labels, 1 as pos_label, binary as average, 1.0 as beta (higher is better) :
+ - Score on train : 0.945695364238
+ - Score on test : 0.654545454545
+ For Hamming loss using None as classes (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.349693251534
+ For Jaccard similarity score using None as sample_weights (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.650306748466
+ For Matthews correlation coefficient (higher is better) :
+ - Score on train : 0.893318905601
+ - Score on test : 0.300704053397
+ For Precision score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.959677419355
+ - Score on test : 0.646706586826
+ For Recall score using None as sample_weights, None as labels, 1 as pos_label, binary as average (higher is better) :
+ - Score on train : 0.932114882507
+ - Score on test : 0.662576687117
+ For ROC AUC score using None as sample_weights, micro as average (higher is better) :
+ - Score on train : 0.946475195822
+ - Score on test : 0.650306748466
+ For Zero one loss using None as sample_weights (lower is better) :
+ - Score on train : 0.0535248041775
+ - Score on test : 0.349693251534
+
+
+ Classification took 0:02:42
\ No newline at end of file
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--
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