diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py index 72edbcbb8d45ac079391155f8fa0a6de04e1704d..f16a1e46d4407ee4c2d8eae0175ea8c388cad91e 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 5c9c9ece7e677b5b0943572a2c87fe0c18f8a76c..327974a830342e0cef8281f22ec380bb54ccf18a 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 7f48d2256c3ffbf5844b764dcea6d56d90471150..2beb829592f6b5e9b78c442cc0167b456ba4fc49 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 9234a3a52ed22f18a562472de750a36034df2a46..760395b930d641b3f4a826dd3f47fd85a11b8317 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 ce16fd5e56b6de781adbcdc75f083cb1de74152e..34b8a3947f208026622f762b526d853dac03007d 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 22fee825bea948ce345d30ef58d2038d94d123a0..694a63335a23a46e26c9f96653440e482cec99b6 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']) + 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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 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 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 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 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 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 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 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 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 0000000000000000000000000000000000000000..ebdc774e318b4b15b79a10ae3f161f5de7db612e --- /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 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 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 0000000000000000000000000000000000000000..aa0ed894c9b4f6453ef7a8c256908c1cf03065c5 --- /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 0000000000000000000000000000000000000000..8b4ad09df67a9b7bdbd06f55b6ec79f84b2930d4 --- /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 0000000000000000000000000000000000000000..eb50ff095962df1fd70acab21b6077762ee4107d --- /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 0000000000000000000000000000000000000000..88ced8d18c3897e4634464f1f99dd89340395413 --- /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 0000000000000000000000000000000000000000..30acdc74275045d5c84bb4f6ca6fa5e810fbb316 --- /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 0000000000000000000000000000000000000000..a7327f60d08dd19011de55f78d51cfc32dd18667 --- /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 0000000000000000000000000000000000000000..37edfd4e3c65d6ab39155bc9c5dc05239054f5e3 --- /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 0000000000000000000000000000000000000000..19ca60e17c2da24bd42c9cbe8f0e0822a93d13b5 --- /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 0000000000000000000000000000000000000000..4ab39e60819c51d7fd0e67480ca95fe4738c61af --- /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 0000000000000000000000000000000000000000..7590aec74eb1e4fea496d8e10b5e5bf5f30b739d --- /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 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 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 0000000000000000000000000000000000000000..66463c675d8870b9a97788ff03abc8565c04ff6f --- /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 0000000000000000000000000000000000000000..4014d5d72dd5e20c33e3776df7e7efd8728e0b32 --- /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 0000000000000000000000000000000000000000..941338b7cab97792843a9aa07c29414c383a125a --- /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 0000000000000000000000000000000000000000..9994c27e0bc8e678717543ee5ba4218222d400e6 --- /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 0000000000000000000000000000000000000000..1220eb92189ecea8dfcaa9cfd1a0bbd28cdd5689 --- /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 0000000000000000000000000000000000000000..9e3ed8862fdd8b430bc7a47c4390cc92ad8ff1a3 --- /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 0000000000000000000000000000000000000000..fdea905a8e6f171422b087e131cfc8c2e1fce247 --- /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 0000000000000000000000000000000000000000..7603a96f59cd7b5eaf09dda69ecbe30faf13382b --- /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 0000000000000000000000000000000000000000..8cf562783d49f1b7aecb869ea9f6121897d98907 --- /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 0000000000000000000000000000000000000000..2b537539d28af2ccf7b92654ede096d355c7d8ce --- /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 0000000000000000000000000000000000000000..e69ff30808dda1586715fbdad38e3521606d7389 --- /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 0000000000000000000000000000000000000000..a8ff8c7db6bd6de6bb3bfa40ca1672f2bba997fa --- /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 0000000000000000000000000000000000000000..4de72ebd13898009e826c82f233fae0f6f56d686 --- /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 0000000000000000000000000000000000000000..4473af4f3411d848f0409fab074c59a1235f5087 --- /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 0000000000000000000000000000000000000000..063ea1d68e512b337662dbecf88bbfd48c3a4100 --- /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 0000000000000000000000000000000000000000..a5fd967d9bdc981bad2a0a0e1c3276c14e3da102 --- /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 0000000000000000000000000000000000000000..1adc63a68e680da1cfe5bfcf2306c76c23f39862 --- /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 0000000000000000000000000000000000000000..7ebb6036ab265350110bb542ce0472e0f9a082c6 --- /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 0000000000000000000000000000000000000000..cea8682b458dfb75e9cf08f0077d246f7c632a76 --- /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 0000000000000000000000000000000000000000..ad4e7d8ffaded3a8b4e79289fb4bcec7214e411d --- /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 0000000000000000000000000000000000000000..22b59d7c3cbeef9a0d70236bed2c9b40760fd5d2 --- /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 0000000000000000000000000000000000000000..2691e9a25fe0dcbe691e51ccbedd6b97a05053c7 --- /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 0000000000000000000000000000000000000000..fda8b6186f064e71e64be58600f2fbb2e97b9097 --- /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 0000000000000000000000000000000000000000..163070a3948a2cb35db5f37e7583b212df10f4e0 --- /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 0000000000000000000000000000000000000000..2ddea1ed09bbe8a1dc35ab1a02675d23ef294cf3 --- /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 0000000000000000000000000000000000000000..2bc723220b57ff5c204d6843666886ea634ec020 --- /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 0000000000000000000000000000000000000000..e13671066660a8ea2d08c6d5b820dc451861ae2e --- /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 0000000000000000000000000000000000000000..85e7156f0e3160b8328d4f158c1d47fac9b1b035 --- /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 0000000000000000000000000000000000000000..2597674fd90930ceae768243ae291b23c148a80a --- /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 0000000000000000000000000000000000000000..8036455a4a53192eca5b4e5d0c71f9d629cdad50 --- /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 0000000000000000000000000000000000000000..d322756bb2b644cba78cee781aa42e31270df75d --- /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 0000000000000000000000000000000000000000..b981a309945069e72f387099e6994097a52beb15 --- /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 0000000000000000000000000000000000000000..252f0e94ea0b9be3ea39bbd838d1c73f5a68a70b --- /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 0000000000000000000000000000000000000000..174aa6603fd8eae1989c8c1336b27c68c4d9b150 --- /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 0000000000000000000000000000000000000000..af6975a04b3c8866d4b793c0ede37766ed6ed742 --- /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 0000000000000000000000000000000000000000..3087752093ff36a80392cce0b02599937e620006 --- /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 0000000000000000000000000000000000000000..bec8cc0862c89b0e4f0f25882dd179bce688f501 --- /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 0000000000000000000000000000000000000000..df467afc6976eefdeec3c48ed4aa3a04c1d46a52 --- /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 0000000000000000000000000000000000000000..d772dc2ba86cbbeeecfbe5955cfc3c2459718183 --- /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 0000000000000000000000000000000000000000..c06147077e3c23ec83992b905609d4b683de2ead --- /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 0000000000000000000000000000000000000000..2f337c0be5efb18ce58874136036d4ba0a05c6f5 --- /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 0000000000000000000000000000000000000000..238dc8697aed190eba3cd93bd1a55ecb684b4dc2 --- /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 0000000000000000000000000000000000000000..3143bf320e8cee4b75194d82baef6bb63ddc6658 --- /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 0000000000000000000000000000000000000000..37307630be68eac29547453fa6a512ab36ec4eb1 --- /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 0000000000000000000000000000000000000000..b0d3c7d24d81c9fb2ad2e83588678c99c2c826fb --- /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 0000000000000000000000000000000000000000..fdaf6a0347d373aa2cdb136256a3b275cf060fdf --- /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 0000000000000000000000000000000000000000..c8044b8eaa970cc134e90d39e620fbbd6bdad923 --- /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 0000000000000000000000000000000000000000..2fdde68dc0ce535fc52cde202f3fe8f7dff374dd --- /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 0000000000000000000000000000000000000000..a429bde0fd6a898ff1e84b169dd29aa3dc78feb8 --- /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 0000000000000000000000000000000000000000..60f938447b531f3a1173002804c477f8934e01e6 --- /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 0000000000000000000000000000000000000000..ad610a49e941c3f49d9f54f71786e23ecf600a41 --- /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 0000000000000000000000000000000000000000..f556f1c15a023a170c0a39592b499e1a4c22ae6c --- /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 0000000000000000000000000000000000000000..8e5e9f9f7cae847550600a606b5d725c3a74ddc0 --- /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 0000000000000000000000000000000000000000..957fabe5357e0bfbbf1a0e352b5a9c7eb6c16e9e --- /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 0000000000000000000000000000000000000000..2bec4a8def2bc27bcc726ddc2adf8e91abf1898e --- /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 0000000000000000000000000000000000000000..c893a067cf8ef8502fe4bf1b3c3ed4fd43c8a947 --- /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 0000000000000000000000000000000000000000..5078d6723792a9af7167ecff62de4f10c4951ada --- /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 0000000000000000000000000000000000000000..443ccb0ce1bb093974bfe2ff30c4ac2e8ffee6be --- /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 0000000000000000000000000000000000000000..104c1623ea0bcee0b611accb93b95e8dcb953163 --- /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 0000000000000000000000000000000000000000..a7f7aeba8963cd98453801fe86103e04c0236874 --- /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 0000000000000000000000000000000000000000..c4a70d202a545d07c6fdce1efd6fed77739d842c --- /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 0000000000000000000000000000000000000000..2352e31006253683b2281add5ec32dacdd70fe69 --- /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 0000000000000000000000000000000000000000..9872a6e3f23d08e15d59871a38895aa2e30ad802 --- /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 0000000000000000000000000000000000000000..6b10db3d83bb74e7cfb902f4dd19e129e8ce0d83 --- /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 0000000000000000000000000000000000000000..492c2a10e1888fc539c8bbfe93a868cb6dc05acd --- /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 0000000000000000000000000000000000000000..00fc11ce3307aaeba1da3ac9ec2dc13317502e96 --- /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 0000000000000000000000000000000000000000..6d14326a9ff38a6ac30bb25b80072ee017f73304 --- /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 0000000000000000000000000000000000000000..369a70642b47273c76e4a5f7abc2029445ad1e2f --- /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 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 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 0000000000000000000000000000000000000000..b60ea211f7c09304ace03422f0778bcddaf62399 --- /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 0000000000000000000000000000000000000000..cbca8eb0039aab2ed51517c5f2899d0cc9652543 --- /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 0000000000000000000000000000000000000000..df58139f902b5ee57e2507cea7cd188b2fadeb3e --- /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 0000000000000000000000000000000000000000..f33428bee0049106dc8485fdc823b2a8a843ce3b --- /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 0000000000000000000000000000000000000000..ea53ff6e44db8a791af25e9f81555aaf4cebc691 --- /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 0000000000000000000000000000000000000000..fba9a251c21095b8794afa57bff632eade083afb --- /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 0000000000000000000000000000000000000000..57fff15057cbbb315bc37de05a652b560901043e --- /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 0000000000000000000000000000000000000000..98a63989b19cb8c8ccf05274956f2f0eaa381f72 --- /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 0000000000000000000000000000000000000000..c856d9f14cf3591a0f30fb98a5360817ae7d4bf9 --- /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 0000000000000000000000000000000000000000..ad68396cb2727c8fdb96f5434a4de601bcd3b1a4 --- /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 0000000000000000000000000000000000000000..6940dd204b2733325fa7b710e3ff15b27d3ba670 --- /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 0000000000000000000000000000000000000000..f350a5c9a73fc440912a823c1d656819e161af12 --- /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 0000000000000000000000000000000000000000..363b24df73804c5b6ebf0d288124efac4e3b4234 --- /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 0000000000000000000000000000000000000000..ffd0593fdc9869471ff26de614fffab2d036563f --- /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 0000000000000000000000000000000000000000..8abf7311fc9ba911de1a3d720d9bb7d3ec4340f0 --- /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 0000000000000000000000000000000000000000..f036adb2923d8b461e66be5ffe8ff16415218575 --- /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 0000000000000000000000000000000000000000..ca4e04562a49d8946a094e3f2b046a2d3ac8ba83 --- /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 0000000000000000000000000000000000000000..135a21712c64c604f1e6db87592eb50f36d5af05 --- /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 0000000000000000000000000000000000000000..196f8f53667482d2e82344555806ead45e58f73e --- /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 0000000000000000000000000000000000000000..ee9baaf6a6799346420efc11c08ffa1d0684fbbb --- /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 0000000000000000000000000000000000000000..64fcc3c0272aab61ac6b4189cfb7bef371923fb6 --- /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 0000000000000000000000000000000000000000..12724270091c037eda746c53f5d97644a4422d15 --- /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 0000000000000000000000000000000000000000..0941dfb69e9d34295a880d2cf26bd7c6477fee3e --- /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 0000000000000000000000000000000000000000..f045236d8295a82f7da57daef336372812831217 --- /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 0000000000000000000000000000000000000000..87f5dbcbd36a09ecf6b67589e8e12155c6985ed0 --- /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 0000000000000000000000000000000000000000..7706e28244ed7cc4e555ccdfc0ab79763835f5cc --- /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 0000000000000000000000000000000000000000..1f4c6cacaf7562b347ca5d334222e17e268ef447 --- /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 0000000000000000000000000000000000000000..7608ef41729a9b5cb1f30814d0d67f5f16c88dc8 --- /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 0000000000000000000000000000000000000000..f038f6bca8cdd4eaf8f2e8a6dc6f7fbcac9a7677 --- /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 0000000000000000000000000000000000000000..3ac2ede53200fc943b3ea0632b604bb68f25cf82 --- /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 0000000000000000000000000000000000000000..7b164726c270ad9037a1896468b9c6723a4223cd --- /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 0000000000000000000000000000000000000000..ba68d3312b6263024dccf42dbffa2af4a97eb3da --- /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 0000000000000000000000000000000000000000..1c74268d44b35b508f5b116dd00b0718b744de24 --- /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 0000000000000000000000000000000000000000..a4c42eba1466075a04e0d1a16c6a92a1f22e83af --- /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 0000000000000000000000000000000000000000..fb0580168af5ffc042bf8a18d7bf3cf5e1b99493 --- /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 0000000000000000000000000000000000000000..d61330a48f3890bbf63a307fc82e12c9c070de9a --- /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 0000000000000000000000000000000000000000..b1e591eb3ba30434ad09ecc65e666a3bbde051f1 --- /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 0000000000000000000000000000000000000000..2c74ef60de92164d54e92672d73bdd5fecc19eb8 --- /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 0000000000000000000000000000000000000000..24bda0ff6f91298220202c880ba3053a2bcca8a7 --- /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 0000000000000000000000000000000000000000..94ded252d6cbc6566ca021c18a625e0c06e0f4bc --- /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 0000000000000000000000000000000000000000..de562ec3dc459832a73193df7d839ddbdcada760 --- /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 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--git a/Code/MonoMutliViewClassifiers/Results/20170922-151359-awaexp-zero_one_loss.png b/Code/MonoMutliViewClassifiers/Results/20170922-151359-awaexp-zero_one_loss.png new file mode 100644 index 0000000000000000000000000000000000000000..c61b0ebd9f523545e183281a50b04653bb093595 Binary files /dev/null and b/Code/MonoMutliViewClassifiers/Results/20170922-151359-awaexp-zero_one_loss.png differ 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 0000000000000000000000000000000000000000..364e159c0975c460436e8bdb3d720049c63dd944 --- /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 0000000000000000000000000000000000000000..f64a02e2b4f6fd609971c0bf2367c3efcc964932 --- /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 0000000000000000000000000000000000000000..536417bfcaa772818d3ecd639be956c098fbec44 --- /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 0000000000000000000000000000000000000000..4df3007c0bcf6659171381d5b00679aa3a1f1cbe --- /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 0000000000000000000000000000000000000000..b29fcd5c887d061dc5226a43564993123066fc21 --- /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 0000000000000000000000000000000000000000..4389a81b5aec87bcdb419779b96d32b199e3665b --- /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 0000000000000000000000000000000000000000..f1b32bf5fd6beaee8bd28c4b79edddcd91da9faa --- /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 0000000000000000000000000000000000000000..fbb90c04657217b2541ccfdd91db7a6429e15c93 --- /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 0000000000000000000000000000000000000000..e32ae43c8d5c7c2f46b0d360dd065b57807e52ce --- /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 0000000000000000000000000000000000000000..911eef465fed28718d6810317df5f5dcbdc708bd --- /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 0000000000000000000000000000000000000000..cef95c168b08fd80c2d4d148e0fd886c3a0c6a3e --- /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 0000000000000000000000000000000000000000..39427408c3b62148b3b8313b16c0bad3e4167a84 --- /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 0000000000000000000000000000000000000000..079b234025a5c1987fd239f413176840eba2b1d1 --- /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 0000000000000000000000000000000000000000..c9467f475bea04465afe24ef3a06d8d554fb70b2 --- /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 0000000000000000000000000000000000000000..a7b4314b4d420d155e9a52baea7588966bf60426 --- /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 0000000000000000000000000000000000000000..6d2c843eb78d52eafdd1131727de1562a68c19cb --- /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 0000000000000000000000000000000000000000..a863164a3904816ea4d34367be985de975ee06fa --- /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 0000000000000000000000000000000000000000..3bc3e5720fc2ecc7b40677fdf1c81374dde83838 --- /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 0000000000000000000000000000000000000000..b3b064ddff608e6913028ab3c643ad715c4812c8 --- /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 0000000000000000000000000000000000000000..cb5b38c4289ce102ef3ca0196677715b5235df69 --- /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 0000000000000000000000000000000000000000..f84279f87d4a434a991a55ae5840558756fc238a --- /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 0000000000000000000000000000000000000000..636410423076baddf5050f759adbfdc26ff0fe14 --- /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 0000000000000000000000000000000000000000..500b55af635bd237499f9f9c1d7ff0aa2d3a0adc --- /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 0000000000000000000000000000000000000000..e6c95e8ccde38d7c76c576d6caa5bb48dafad0f9 --- /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 0000000000000000000000000000000000000000..a24b4bc1a383221f1f37b6e3cb911ca20af05247 --- /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 0000000000000000000000000000000000000000..8376d5783ff259e1a3c3e8f187e05287187d7923 --- /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 0000000000000000000000000000000000000000..7b0b079cdf963b9638ded2072efdfdf58289c8f5 --- /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 0000000000000000000000000000000000000000..3c1b7f1c103f6fa41fe90421bfad6d480ae2e962 --- /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 0000000000000000000000000000000000000000..97cb43d591f767152918d059d87ff5d8efb9dad5 --- /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 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 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 0000000000000000000000000000000000000000..b724a44a48684b7c76837477fe9f8a290ecb4647 --- /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 0000000000000000000000000000000000000000..a5b7a000e53e5b70e812f8ae4f07376f9f488ed6 --- /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 0000000000000000000000000000000000000000..0715d5a8804a074bf7e1c2801a8635919d632a86 --- /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 0000000000000000000000000000000000000000..c2c14404ed0d42aa1b35df442537ed0cb0376434 --- /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 0000000000000000000000000000000000000000..1f112726d1e13ca2f31e55ef3436ba474672d74f --- /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 0000000000000000000000000000000000000000..d1e5592f3f7cd9285bdf81c09ae63510b43e1f2f --- /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 0000000000000000000000000000000000000000..bbbcc61285d7cb5d0caca33f53d4d6d1b622fa50 --- /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 0000000000000000000000000000000000000000..189c73af46763ae61ea12896342e2ba8d3c69841 --- /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 0000000000000000000000000000000000000000..9d8ea6da3bad1972026d7f26e154e85dd23eeced --- /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 diff --git a/Code/MonoMutliViewClassifiers/temp_scm_fusion b/Code/MonoMutliViewClassifiers/temp_scm_fusion new file mode 100644 index 0000000000000000000000000000000000000000..66b3af8c7ec1b1e1f18574af5d8f52ffdc70d7f8 Binary files /dev/null and b/Code/MonoMutliViewClassifiers/temp_scm_fusion differ diff --git a/Code/MonoMutliViewClassifiers/test.png b/Code/MonoMutliViewClassifiers/test.png new file mode 100644 index 0000000000000000000000000000000000000000..055bd6e66204e8a87e195ca1b4819000b3b87c3f Binary files /dev/null and b/Code/MonoMutliViewClassifiers/test.png differ