Skip to content
Snippets Groups Projects
Commit 30614dd6 authored by bbauvin's avatar bbauvin
Browse files

Added Brnaseq, need to improve it

parent 2855895c
Branches
Tags
No related merge requests found
Expand all Show whitespace changes
Inline Side-by-side
Showing
with 3909 additions and 69 deletions
Code/MonoMutliViewClassifiers/ExecClassif.py
+ 59
− 58
View file @ 30614dd6
......@@ -21,7 +21,7 @@ parser = argparse.ArgumentParser(
groupStandard = parser.add_argument_group('Standard arguments')
groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
groupStandard.add_argument('--name', metavar='STRING', action='store', help='Name of Database (default: %(default)s)',
default='MultiOmic')
default='ModifiedMultiOmic')
groupStandard.add_argument('--type', metavar='STRING', action='store', help='Type of database : .hdf5 or .csv',
default='.hdf5')
groupStandard.add_argument('--views', metavar='STRING', action='store',help='Name of the views selected for learning',
......@@ -39,9 +39,9 @@ groupStandard.add_argument('--fileFeat', metavar='STRING', action='store',
groupClass = parser.add_argument_group('Classification arguments')
groupClass.add_argument('--CL_split', metavar='FLOAT', action='store',
help='Determine the learning rate if > 1.0, number of fold for cross validation', type=float,
default=0.9)
default=0.7)
groupClass.add_argument('--CL_nbFolds', metavar='INT', action='store', help='Number of folds in cross validation',
type=int, default=2)
type=int, default=5)
groupClass.add_argument('--CL_nb_class', metavar='INT', action='store', help='Number of classes, -1 for all', type=int,
default=4)
groupClass.add_argument('--CL_classes', metavar='STRING', action='store',
......@@ -156,7 +156,7 @@ views = [str(DATASET.get("View"+str(viewIndex)).attrs["name"]) for viewIndex in
NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
logging.info("Begginging")
logging.info("Start:\t Finding all available mono- & multiview algorithms")
benchmark = {}
if args.CL_type.split(":")==["Benchmark"]:
if args.CL_algorithm=='':
......@@ -197,7 +197,6 @@ if "Monoview" in args.CL_type.strip(":"):
benchmark["Monoview"] = args.CL_algos_monoview.split(":")
classifierTable = "a"
fusionClassifierConfig = "a"
fusionMethodConfig = "a"
mumboNB_ITER = 2
......@@ -217,7 +216,7 @@ AdaboostKWARGS = {"0": args.CL_Ada_n_est.split(":"), "1": args.CL_Ada_b_est.spli
argumentDictionaries = {"Monoview": {}, "Multiview": []}
if benchmark["Monoview"]:
for view in args.views.split(":"):
for view in views:
argumentDictionaries["Monoview"][str(view)] = []
for classifier in benchmark["Monoview"]:
......@@ -227,66 +226,68 @@ if benchmark["Monoview"]:
argumentDictionaries["Monoview"][str(view)].append(arguments)
bestClassifiers = []
bestClassifiersConfigs = []
resultsMonoview =[]
for viewIndex, viewArguments in enumerate(argumentDictionaries["Monoview"].values()):
resultsMonoview = Parallel(n_jobs=nbCores)(
delayed(ExecMonoview)(DATASET.get("View"+str(viewIndex)).value, DATASET.get("labels").value, args.name,
resultsMonoview += (Parallel(n_jobs=nbCores)(
delayed(ExecMonoview)(DATASET.get("View"+str(viewIndex)), DATASET.get("labels").value, args.name,
args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF, gridSearch=True,
**arguments)
for arguments in viewArguments)
for arguments in viewArguments))
accuracies = [result[1] for result in resultsMonoview]
classifiersNames = [result[0] for result in resultsMonoview]
classifiersConfigs = [result[2] for result in resultsMonoview]
accuracies = [result[1] for result in resultsMonoview[viewIndex]]
classifiersNames = [result[0] for result in resultsMonoview[viewIndex]]
classifiersConfigs = [result[2] for result in resultsMonoview[viewIndex]]
bestClassifiers.append(classifiersNames[np.argmax(np.array(accuracies))])
bestClassifiersConfigs.append(classifiersConfigs[np.argmax(np.array(accuracies))])
# bestClassifiers = ["DecisionTree", "DecisionTree", "DecisionTree", "DecisionTree"]
# bestClassifiersConfigs = [["1"],["1"],["1"],["1"]]
if benchmark["Multiview"]:
if benchmark["Multiview"]["Mumbo"]:
for classifier in benchmark["Multiview"]["Mumbo"]:
arguments = {"CL_type": "Mumbo",
"views": args.views.split(":"),
"NB_VIEW": len(args.views.split(":")),
"NB_CLASS": len(args.CL_classes.split(":")),
"LABELS_NAMES": args.CL_classes.split(":"),
"MumboKWARGS": {"classifiersNames": ["DecisionTree", "DecisionTree", "DecisionTree",
"DecisionTree"],
"maxIter":int(args.MU_iter[0]), "minIter":int(args.MU_iter[1]),
"threshold":args.MU_iter[2]}}
argumentDictionaries["Multiview"].append(arguments)
if benchmark["Multiview"]["Fusion"]:
if benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"] and benchmark["Multiview"]["Fusion"]["Classifiers"]:
for method in benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"]:
arguments = {"CL_type": "Fusion",
"views": args.views.split(":"),
"NB_VIEW": len(args.views.split(":")),
"NB_CLASS": len(args.CL_classes.split(":")),
"LABELS_NAMES": args.CL_classes.split(":"),
"FusionKWARGS": {"fusionType":"LateFusion", "fusionMethod":method,
"classifiersNames": bestClassifiers,
"classifiersConfigs": bestClassifiersConfigs,
'fusionMethodConfig': fusionMethodConfig}}
argumentDictionaries["Multiview"].append(arguments)
if benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"] and benchmark["Multiview"]["Fusion"]["Classifiers"]:
for method in benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"]:
for classifier in benchmark["Multiview"]["Fusion"]["Classifiers"]:
arguments = {"CL_type": "Fusion",
"views": args.views.split(":"),
"NB_VIEW": len(args.views.split(":")),
"NB_CLASS": len(args.CL_classes.split(":")),
"LABELS_NAMES": args.CL_classes.split(":"),
"FusionKWARGS": {"fusionType":"EarlyFusion", "fusionMethod":method,
"classifiersNames": classifier,
"classifiersConfigs": fusionClassifierConfig,
'fusionMethodConfig': fusionMethodConfig}}
argumentDictionaries["Multiview"].append(arguments)
resultsMultiview = Parallel(n_jobs=nbCores)(
delayed(ExecMultiview)(DATASET, args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
LABELS_DICTIONARY, gridSearch=True, **arguments)
for arguments in argumentDictionaries["Multiview"])
#
# if benchmark["Multiview"]:
# if benchmark["Multiview"]["Mumbo"]:
# for classifier in benchmark["Multiview"]["Mumbo"]:
# arguments = {"CL_type": "Mumbo",
# "views": args.views.split(":"),
# "NB_VIEW": len(args.views.split(":")),
# "NB_CLASS": len(args.CL_classes.split(":")),
# "LABELS_NAMES": args.CL_classes.split(":"),
# "MumboKWARGS": {"classifiersNames": ["DecisionTree", "DecisionTree", "DecisionTree",
# "DecisionTree"],
# "maxIter":int(args.MU_iter[0]), "minIter":int(args.MU_iter[1]),
# "threshold":args.MU_iter[2]}}
# argumentDictionaries["Multiview"].append(arguments)
# if benchmark["Multiview"]["Fusion"]:
# if benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"] and benchmark["Multiview"]["Fusion"]["Classifiers"]:
# for method in benchmark["Multiview"]["Fusion"]["Methods"]["LateFusion"]:
# arguments = {"CL_type": "Fusion",
# "views": args.views.split(":"),
# "NB_VIEW": len(args.views.split(":")),
# "NB_CLASS": len(args.CL_classes.split(":")),
# "LABELS_NAMES": args.CL_classes.split(":"),
# "FusionKWARGS": {"fusionType":"LateFusion", "fusionMethod":method,
# "classifiersNames": bestClassifiers,
# "classifiersConfigs": bestClassifiersConfigs,
# 'fusionMethodConfig': fusionMethodConfig}}
# argumentDictionaries["Multiview"].append(arguments)
# if benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"] and benchmark["Multiview"]["Fusion"]["Classifiers"]:
# for method in benchmark["Multiview"]["Fusion"]["Methods"]["EarlyFusion"]:
# for classifier in benchmark["Multiview"]["Fusion"]["Classifiers"]:
# arguments = {"CL_type": "Fusion",
# "views": args.views.split(":"),
# "NB_VIEW": len(args.views.split(":")),
# "NB_CLASS": len(args.CL_classes.split(":")),
# "LABELS_NAMES": args.CL_classes.split(":"),
# "FusionKWARGS": {"fusionType":"EarlyFusion", "fusionMethod":method,
# "classifiersNames": classifier,
# "classifiersConfigs": fusionClassifierConfig,
# 'fusionMethodConfig': fusionMethodConfig}}
# argumentDictionaries["Multiview"].append(arguments)
# resultsMultiview = Parallel(n_jobs=nbCores)(
# delayed(ExecMultiview)(DATASET, args.name, args.CL_split, args.CL_nbFolds, 1, args.type, args.pathF,
# LABELS_DICTIONARY, gridSearch=True, **arguments)
# for arguments in argumentDictionaries["Multiview"])
resultsMultiview = []
results = (resultsMonoview, resultsMultiview)
resultAnalysis(benchmark, results)
print len(argumentDictionaries["Multiview"]), len(argumentDictionaries["Monoview"])
......
Code/MonoMutliViewClassifiers/Monoview/ExecClassifMonoView.py
+ 4
− 1
View file @ 30614dd6
......@@ -31,14 +31,16 @@ __date__ = 2016-03-25
def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path, gridSearch=True, **kwargs):
t_start = time.time()
directory = os.path.dirname(os.path.abspath(__file__)) + "/Results-ClassMonoView/"
feat = kwargs["feat"]
feat = X.attrs["name"]
fileFeat = kwargs["fileFeat"]
fileCL = kwargs["fileCL"]
fileCLD = kwargs["fileCLD"]
CL_type = kwargs["CL_type"]
classifierKWARGS = kwargs[CL_type+"KWARGS"]
X = X.value
# Determine the Database to extract features
logging.debug("### Main Programm for Classification MonoView")
......@@ -93,6 +95,7 @@ def ExecMonoview(X, Y, name, learningRate, nbFolds, nbCores, databaseType, path,
accuracy_score = ExportResults.accuracy_score(y_test, y_test_pred)
logging.info("Accuracy :" +str(accuracy_score))
return [CL_type, accuracy_score, cl_desc, feat]
# # Classification Report with Precision, Recall, F1 , Support
# logging.debug("Info:\t Classification report:")
# filename = datetime.datetime.now().strftime("%Y_%m_%d") + "-CMV-" + name + "-" + feat + "-Report"
......
Code/MonoMutliViewClassifiers/Multiview/ExecMultiview.py
+ 2
− 1
View file @ 30614dd6
......@@ -112,7 +112,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
times = (extractionTime, kFoldLearningTime, kFoldPredictionTime, classificationTime)
stringAnalysis, imagesAnalysis = analysisModule.execute(kFoldClassifier, kFoldPredictedTrainLabels,
stringAnalysis, imagesAnalysis, train, test, val = analysisModule.execute(kFoldClassifier, kFoldPredictedTrainLabels,
kFoldPredictedTestLabels, kFoldPredictedValidationLabels,
DATASET, initKWARGS, learningRate, LABELS_DICTIONARY,
views, nbCores, times, kFolds, name, nbFolds,
......@@ -141,6 +141,7 @@ def ExecMultiview(DATASET, name, learningRate, nbFolds, nbCores, databaseType, p
imagesAnalysis[imageName].savefig(outputFileName + imageName + '.png')
logging.info("Done:\t Result Analysis")
return CL_type, classificationKWARGS, train, test, val
if __name__=='__main__':
......
Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py
+ 1
− 1
View file @ 30614dd6
......@@ -121,4 +121,4 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
# name, image = plotAccuracyByIter(predictedTrainLabelsByIter, predictedTestLabelsByIter, trainLabels, testLabels, NB_ITER)
# imagesAnalysis[name] = image
return stringAnalysis, imagesAnalysis
return stringAnalysis, imagesAnalysis, totalAccuracyOnTrain, totalAccuracyOnTest, totalAccuracyOnValidation
Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py
+ 30
− 1
View file @ 30614dd6
......@@ -392,7 +392,7 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
labelsDset.attrs["name"] = "Labels"
metaDataGrp = datasetFile.create_group("Metadata")
metaDataGrp.attrs["nbView"] = 4
metaDataGrp.attrs["nbView"] = 5
metaDataGrp.attrs["nbClass"] = 2
metaDataGrp.attrs["datasetLength"] = len(labels)
labelDictionary = {0:"No", 1:"Yes"}
......@@ -408,12 +408,41 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
mrnaseqDset.attrs["name"] = "MRNASeq"
logging.debug("Done:\t Getting Modified RNASeq Data")
datasetFile = h5py.File(path+"ModifiedMultiOmic.hdf5", "r")
logging.debug("Start:\t Getting Binary RNASeq Data")
binarizedRNASeqDset = datasetFile.create_dataset("View5", shape=(len(labels), len(rnaseqData)*(len(rnaseqData)-1)/2), dtype=bool)
for exampleIndex in range(len(labels)):
offseti=0
rnaseqData = datasetFile["View2"][exampleIndex]
for i, idata in enumerate(rnaseqData):
for j, jdata in enumerate(rnaseqData):
if i < j:
binarizedRNASeqDset[offseti+j] = idata > jdata
offseti += len(rnaseqData)-i-1
binarizedRNASeqDset.attrs["name"] = "BRNASeq"
i=0
for featureIndex in range(len(rnaseqData)*(len(rnaseqData)-1)/2):
if allSame(binarizedRNASeqDset[:, featureIndex]):
i+=1
print i
logging.debug("Done:\t Getting Binary RNASeq Data")
datasetFile.close()
datasetFile = h5py.File(path+"ModifiedMultiOmic.hdf5", "r")
return datasetFile, labelDictionary
def allSame(array):
value = array[0]
areAllSame = True
for i in array:
if i != value:
areAllSame = False
return areAllSame
def getModifiedMultiOmicDBhdf5(features, path, name, NB_CLASS, LABELS_NAMES):
datasetFile = h5py.File(path+"ModifiedMultiOmic.hdf5", "r")
labelDictionary = {0:"No", 1:"Yes"}
......
Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py
+ 8
− 5
View file @ 30614dd6
......@@ -80,7 +80,10 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
DATASET, initKWARGS, LEARNING_RATE, LABELS_DICTIONARY, views, NB_CORES, times, kFolds, databaseName,
nbFolds, validationIndices):
CLASS_LABELS = DATASET.get("labels")[...]
NB_ITER, classifierNames, classifierConfigs = initKWARGS.values()
MumboKWARGS = {"classifiersConfigs":mumboClassifierConfig,
"classifiersNames":mumboclassifierNames, "maxIter":int(args.MU_iter[0]),
"minIter":int(args.MU_iter[1]), "threshold":args.MU_iter[2]}
classifierConfigs, classifierNames, maxIter, minIter, threshold = initKWARGS.values()
nbView = DATASET.get("Metadata").attrs["nbView"]
viewNames = [DATASET.get("View"+str(viewIndex)).attrs["name"] for viewIndex in range(nbView)]
......@@ -161,11 +164,11 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
nbFolds) + \
" folds\n\t- Validation set length : "+str(len(validationIndices))+" for learning rate : "+\
str(LEARNING_RATE)+\
"\n\nClassification configuration : \n\t-Algorithm used : Mumbo \n\t-Iterations : " + \
str(NB_ITER) + "\n\t-Weak Classifiers : " + "\n\t\t-".join(
"\n\nClassification configuration : \n\t-Algorithm used : Mumbo \n\t-Iterations : min " + \
str(minIter)+ ", max "+ str(maxIter)+", threshold "+ str(threshold)+ + "\n\t-Weak Classifiers : " + "\n\t\t-".join(
classifierAnalysis) + "\n\n Mean average accuracies and stats for each fold : "
for foldIdx in range(nbFolds):
stringAnalysis += "\n\t- Fold "+str(foldIdx)
stringAnalysis += "\n\t- Fold "+str(foldIdx)+", used "+str(kFoldClassifier.iterIndex + 1)
for viewIndex, (meanAverageAccuracy, bestViewStat) in enumerate(zip(kFoldMeanAverageAccuracies[foldIdx], kFoldBestViewsStats[foldIdx])):
stringAnalysis+="\n\t\t- On "+viewNames[viewIndex]+\
" : \n\t\t\t- Mean average Accuracy : "+str(meanAverageAccuracy)+\
......@@ -204,4 +207,4 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
bestViews, views, classifierAnalysis, viewNames)
imagesAnalysis = {name: image}
return stringAnalysis, imagesAnalysis
return stringAnalysis, imagesAnalysis, totalAccuracyOnTrain, totalAccuracyOnTest, totalAccuracyOnValidation
Code/MonoMutliViewClassifiers/Multiview/Results/20160825-120401-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log 0 → 100644
+ 19
− 0
View file @ 30614dd6
2016-08-25 12:04:01,154 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
2016-08-25 12:04:01,160 INFO: Info: Labels used: No, Yes
2016-08-25 12:04:01,160 INFO: Info: Length of dataset:347
2016-08-25 12:04:01,171 INFO: ### Main Programm for Multiview Classification
2016-08-25 12:04:01,172 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
2016-08-25 12:04:01,172 INFO: Info: Shape of Methyl_ :(347, 25978)
2016-08-25 12:04:01,172 INFO: Info: Shape of MiRNA__ :(347, 1046)
2016-08-25 12:04:01,173 INFO: Info: Shape of RANSeq_ :(347, 73599)
2016-08-25 12:04:01,173 INFO: Info: Shape of Clinic_ :(347, 127)
2016-08-25 12:04:01,173 INFO: Done: Read Database Files
2016-08-25 12:04:01,175 INFO: Start: Determine validation split for ratio 0.7
2016-08-25 12:04:01,180 INFO: Done: Determine validation split
2016-08-25 12:04:01,181 INFO: Start: Determine 5 folds
2016-08-25 12:04:01,190 INFO: Info: Length of Learning Sets: 196
2016-08-25 12:04:01,192 INFO: Info: Length of Testing Sets: 48
2016-08-25 12:04:01,194 INFO: Info: Length of Validation Set: 103
2016-08-25 12:04:01,195 INFO: Done: Determine folds
2016-08-25 12:04:01,197 INFO: Start: Learning with Fusion and 5 folds
2016-08-25 12:04:01,199 INFO: Start: Fold number 1
Code/MonoMutliViewClassifiers/Multiview/Results/20160825-120506-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log 0 → 100644
+ 19
− 0
View file @ 30614dd6
2016-08-25 12:05:06,726 INFO: Start: Read HDF5 Database Files for ModifiedMultiOmic
2016-08-25 12:05:06,732 INFO: Info: Labels used: No, Yes
2016-08-25 12:05:06,738 INFO: Info: Length of dataset:347
2016-08-25 12:05:06,745 INFO: ### Main Programm for Multiview Classification
2016-08-25 12:05:06,748 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
2016-08-25 12:05:06,750 INFO: Info: Shape of Methyl_ :(347, 25978)
2016-08-25 12:05:06,753 INFO: Info: Shape of MiRNA__ :(347, 1046)
2016-08-25 12:05:06,755 INFO: Info: Shape of RANSeq_ :(347, 73599)
2016-08-25 12:05:06,758 INFO: Info: Shape of Clinic_ :(347, 127)
2016-08-25 12:05:06,760 INFO: Done: Read Database Files
2016-08-25 12:05:06,762 INFO: Start: Determine validation split for ratio 0.7
2016-08-25 12:05:06,767 INFO: Done: Determine validation split
2016-08-25 12:05:06,769 INFO: Start: Determine 5 folds
2016-08-25 12:05:06,779 INFO: Info: Length of Learning Sets: 196
2016-08-25 12:05:06,781 INFO: Info: Length of Testing Sets: 48
2016-08-25 12:05:06,783 INFO: Info: Length of Validation Set: 103
2016-08-25 12:05:06,785 INFO: Done: Determine folds
2016-08-25 12:05:06,787 INFO: Start: Learning with Fusion and 5 folds
2016-08-25 12:05:06,789 INFO: Start: Fold number 1
Code/MonoMutliViewClassifiers/Multiview/Results/20160825-120526-CMultiV-Fusion-Methyl_MiRNA_RNASEQ_Clinical-ModifiedMultiOmic-LOG.log 0 → 100644
+ 30
− 0
View file @ 30614dd6
2016-08-25 12:05:26,273 INFO: Start: Read CSV Database Files for ModifiedMultiOmic
2016-08-25 12:05:26,331 DEBUG: Start: Getting Methylation Data
2016-08-25 12:05:39,490 DEBUG: Done: Getting Methylation Data
2016-08-25 12:05:39,495 DEBUG: Start: Getting MiRNA Data
2016-08-25 12:05:40,023 DEBUG: Done: Getting MiRNA Data
2016-08-25 12:05:40,025 DEBUG: Start: Getting RNASeq Data
2016-08-25 12:06:36,383 DEBUG: Done: Getting RNASeq Data
2016-08-25 12:06:36,527 DEBUG: Start: Getting Clinical Data
2016-08-25 12:06:37,052 DEBUG: Done: Getting Clinical Data
2016-08-25 12:06:37,247 DEBUG: Start: Getting Modified RNASeq Data
2016-08-25 12:07:27,431 DEBUG: Done: Getting Modified RNASeq Data
2016-08-25 12:07:27,433 DEBUG: Done: Getting Binary RNASeq Data
2016-08-25 12:07:29,769 INFO: Info: Labels used: No, Yes
2016-08-25 12:07:29,789 INFO: Info: Length of dataset:347
2016-08-25 12:07:29,819 INFO: ### Main Programm for Multiview Classification
2016-08-25 12:07:29,821 INFO: ### Classification - Database : ModifiedMultiOmic ; Views : Methyl, MiRNA, RNASEQ, Clinical ; Algorithm : Fusion ; Cores : 4
2016-08-25 12:07:29,823 INFO: Info: Shape of Methyl_ :(347, 25978)
2016-08-25 12:07:29,825 INFO: Info: Shape of MiRNA__ :(347, 1046)
2016-08-25 12:07:29,827 INFO: Info: Shape of RNASeq_ :(347, 73599)
2016-08-25 12:07:29,830 INFO: Info: Shape of Clinic_ :(347, 127)
2016-08-25 12:07:29,832 INFO: Done: Read Database Files
2016-08-25 12:07:29,833 INFO: Start: Determine validation split for ratio 0.7
2016-08-25 12:07:29,890 INFO: Done: Determine validation split
2016-08-25 12:07:29,892 INFO: Start: Determine 5 folds
2016-08-25 12:07:29,900 INFO: Info: Length of Learning Sets: 196
2016-08-25 12:07:29,902 INFO: Info: Length of Testing Sets: 48
2016-08-25 12:07:29,903 INFO: Info: Length of Validation Set: 103
2016-08-25 12:07:29,905 INFO: Done: Determine folds
2016-08-25 12:07:29,907 INFO: Start: Learning with Fusion and 5 folds
2016-08-25 12:07:29,908 INFO: Start: Fold number 1
Code/MonoMutliViewClassifiers/Multiview/run.py
+ 1
− 1
View file @ 30614dd6
# coding=utf-8
import os
os.system('python ExecMultiview.py -log --name ModifiedMultiOmic --type .hdf5 --views Methyl:MiRNA:RNASEQ:Clinical --pathF /home/bbauvin/Documents/Data/Data_multi_omics/ --CL_split 0.7 --CL_nbFolds 5 --CL_nb_class 2 --CL_classes Positive:Negative --CL_type Fusion --CL_cores 4 --FU_type EarlyFusion --FU_method WeightedLinear')
os.system('python ExecMultiview.py -log --name ModifiedMultiOmic --type .csv --views Methyl:MiRNA:RNASEQ:Clinical --pathF /home/bbauvin/Documents/Data/Data_multi_omics/ --CL_split 0.7 --CL_nbFolds 5 --CL_nb_class 2 --CL_classes Positive:Negative --CL_type Fusion --CL_cores 4 --FU_type EarlyFusion --FU_method WeightedLinear')
# /donnees/pj_bdd_bbauvin/Data_multi_omics/
#
# /home/bbauvin/Documents/Data/Data_multi_omics/
......
Code/MonoMutliViewClassifiers/ResultAnalysis.py
+ 32
− 1
View file @ 30614dd6
import matplotlib.pyplot as plt
import time
import pylab
def resultAnalysis(benchmark, results):
mono, multi = results
names = [type_+feat for [type_, b, c, feat] in mono]+[type_ if type_ != "Fusion" else type_+a["FusionType"]+a["FusionMethod"] for type_, a, b, c, d in multi]
nbResults = len(mono)+len(multi)
accuracies = [float(accuracy)*100 for [a, accuracy, c, d] in mono]+[float(accuracy)*100 for a, b, c, d, accuracy in multi]
f = pylab.figure()
ax = f.add_axes([0.1, 0.1, 0.8, 0.8])
ax.set_title("Accuracies on validation set for each classifier")
ax.bar(range(nbResults), accuracies, align='center')
ax.set_xticks(range(nbResults))
ax.set_xticklabels(names, rotation="vertical")
try:
fig = plt.gcf()
fig.subplots_adjust(bottom=0.8)
except:
pass
# plt.bar(range(nbResults), accuracies, 1)
# plt.xlabel('ClassLabels')
# plt.ylabel('Precision in %')
# plt.title('Results of benchmark-Classification')
# plt.axis([0, nbResults, 0, 100])
# plt.xticks(range(nbResults), rotation="vertical")
# Makes sure that the file does not yet exist
f.savefig("Results/poulet"+time.strftime("%Y%m%d-%H%M%S")+".png")
#plt.close()
Code/MonoMutliViewClassifiers/Results/20160825-095006-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log 0 → 100644
+ 8
− 0
View file @ 30614dd6
2016-08-25 09:50:06,447 INFO: Begginging
2016-08-25 09:50:06,902 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:50:06,903 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
2016-08-25 09:50:06,903 DEBUG: Start: Determine Train/Test split
2016-08-25 09:50:06,959 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:50:06,960 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:50:06,960 DEBUG: Done: Determine Train/Test split
2016-08-25 09:50:06,960 DEBUG: Start: Classification
Code/MonoMutliViewClassifiers/Results/20160825-095124-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log 0 → 100644
+ 89
− 0
View file @ 30614dd6
2016-08-25 09:51:24,124 INFO: Begginging
2016-08-25 09:51:24,186 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:51:24,186 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
2016-08-25 09:51:24,186 DEBUG: Start: Determine Train/Test split
2016-08-25 09:51:24,210 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:51:24,210 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:51:24,210 DEBUG: Done: Determine Train/Test split
2016-08-25 09:51:24,210 DEBUG: Start: Classification
2016-08-25 09:51:33,000 DEBUG: Info: Time for Classification: 8.71911692619[s]
2016-08-25 09:51:33,000 DEBUG: Done: Classification
2016-08-25 09:51:33,022 DEBUG: Start: Statistic Results
2016-08-25 09:51:33,023 INFO: Accuracy :0.742857142857
2016-08-25 09:51:33,036 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:51:33,036 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
2016-08-25 09:51:33,036 DEBUG: Start: Determine Train/Test split
2016-08-25 09:51:33,048 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:51:33,048 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:51:33,048 DEBUG: Done: Determine Train/Test split
2016-08-25 09:51:33,048 DEBUG: Start: Classification
2016-08-25 09:51:40,005 DEBUG: Info: Time for Classification: 6.96573996544[s]
2016-08-25 09:51:40,005 DEBUG: Done: Classification
2016-08-25 09:51:40,007 DEBUG: Start: Statistic Results
2016-08-25 09:51:40,007 INFO: Accuracy :1.0
2016-08-25 09:51:40,016 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:51:40,016 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : KNN
2016-08-25 09:51:40,017 DEBUG: Start: Determine Train/Test split
2016-08-25 09:51:40,029 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:51:40,030 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:51:40,030 DEBUG: Done: Determine Train/Test split
2016-08-25 09:51:40,030 DEBUG: Start: Classification
2016-08-25 09:51:50,394 DEBUG: Info: Time for Classification: 10.3715598583[s]
2016-08-25 09:51:50,394 DEBUG: Done: Classification
2016-08-25 09:51:50,958 DEBUG: Start: Statistic Results
2016-08-25 09:51:50,959 INFO: Accuracy :0.942857142857
2016-08-25 09:51:50,973 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:51:50,973 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
2016-08-25 09:51:50,973 DEBUG: Start: Determine Train/Test split
2016-08-25 09:51:50,987 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:51:50,987 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:51:50,987 DEBUG: Done: Determine Train/Test split
2016-08-25 09:51:50,987 DEBUG: Start: Classification
2016-08-25 09:51:58,555 DEBUG: Info: Time for Classification: 7.57835888863[s]
2016-08-25 09:51:58,555 DEBUG: Done: Classification
2016-08-25 09:51:58,568 DEBUG: Start: Statistic Results
2016-08-25 09:51:58,568 INFO: Accuracy :0.8
2016-08-25 09:51:58,577 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:51:58,577 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SGD
2016-08-25 09:51:58,577 DEBUG: Start: Determine Train/Test split
2016-08-25 09:51:58,590 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:51:58,590 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:51:58,590 DEBUG: Done: Determine Train/Test split
2016-08-25 09:51:58,590 DEBUG: Start: Classification
2016-08-25 09:52:00,152 DEBUG: Info: Time for Classification: 1.57103300095[s]
2016-08-25 09:52:00,152 DEBUG: Done: Classification
2016-08-25 09:52:00,156 DEBUG: Start: Statistic Results
2016-08-25 09:52:00,156 INFO: Accuracy :0.771428571429
2016-08-25 09:52:00,168 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:52:00,168 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
2016-08-25 09:52:00,168 DEBUG: Start: Determine Train/Test split
2016-08-25 09:52:00,183 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:52:00,184 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:52:00,184 DEBUG: Done: Determine Train/Test split
2016-08-25 09:52:00,184 DEBUG: Start: Classification
2016-08-25 09:52:08,021 DEBUG: Info: Time for Classification: 7.84802913666[s]
2016-08-25 09:52:08,021 DEBUG: Done: Classification
2016-08-25 09:52:08,170 DEBUG: Start: Statistic Results
2016-08-25 09:52:08,170 INFO: Accuracy :0.971428571429
2016-08-25 09:52:08,183 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:52:08,183 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
2016-08-25 09:52:08,183 DEBUG: Start: Determine Train/Test split
2016-08-25 09:52:08,196 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:52:08,196 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:52:08,196 DEBUG: Done: Determine Train/Test split
2016-08-25 09:52:08,196 DEBUG: Start: Classification
2016-08-25 09:52:35,761 DEBUG: Info: Time for Classification: 27.5747389793[s]
2016-08-25 09:52:35,761 DEBUG: Done: Classification
2016-08-25 09:52:35,917 DEBUG: Start: Statistic Results
2016-08-25 09:52:35,917 INFO: Accuracy :0.942857142857
2016-08-25 09:52:35,926 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:52:35,926 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
2016-08-25 09:52:35,926 DEBUG: Start: Determine Train/Test split
2016-08-25 09:52:35,937 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:52:35,937 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:52:35,937 DEBUG: Done: Determine Train/Test split
2016-08-25 09:52:35,937 DEBUG: Start: Classification
2016-08-25 09:52:43,959 DEBUG: Info: Time for Classification: 8.02944397926[s]
2016-08-25 09:52:43,959 DEBUG: Done: Classification
2016-08-25 09:52:44,145 DEBUG: Start: Statistic Results
2016-08-25 09:52:44,146 INFO: Accuracy :0.914285714286
Code/MonoMutliViewClassifiers/Results/20160825-095531-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-MultiOmic-LOG.log 0 → 100644
+ 8
− 0
View file @ 30614dd6
2016-08-25 09:55:31,328 INFO: Start: Finding all available mono- & multiview algorithms
2016-08-25 09:55:31,348 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:55:31,348 DEBUG: ### Classification - Database:MultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
2016-08-25 09:55:31,348 DEBUG: Start: Determine Train/Test split
2016-08-25 09:55:31,371 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:55:31,371 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:55:31,372 DEBUG: Done: Determine Train/Test split
2016-08-25 09:55:31,372 DEBUG: Start: Classification
Code/MonoMutliViewClassifiers/Results/20160825-095558-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log 0 → 100644
+ 41
− 0
View file @ 30614dd6
2016-08-25 09:55:58,318 INFO: Start: Finding all available mono- & multiview algorithms
2016-08-25 09:55:58,709 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:55:58,709 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
2016-08-25 09:55:58,709 DEBUG: Start: Determine Train/Test split
2016-08-25 09:55:58,733 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:55:58,733 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:55:58,733 DEBUG: Done: Determine Train/Test split
2016-08-25 09:55:58,733 DEBUG: Start: Classification
2016-08-25 09:56:08,648 DEBUG: Info: Time for Classification: 9.93510890007[s]
2016-08-25 09:56:08,648 DEBUG: Done: Classification
2016-08-25 09:56:08,650 DEBUG: Start: Statistic Results
2016-08-25 09:56:08,651 INFO: Accuracy :0.857142857143
2016-08-25 09:56:08,672 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:08,672 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
2016-08-25 09:56:08,672 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:08,688 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:56:08,688 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:56:08,688 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:08,688 DEBUG: Start: Classification
2016-08-25 09:56:15,790 DEBUG: Info: Time for Classification: 7.11475300789[s]
2016-08-25 09:56:15,790 DEBUG: Done: Classification
2016-08-25 09:56:15,792 DEBUG: Start: Statistic Results
2016-08-25 09:56:15,792 INFO: Accuracy :0.857142857143
2016-08-25 09:56:15,813 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:15,813 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : KNN
2016-08-25 09:56:15,813 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:15,835 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:56:15,835 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:56:15,835 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:15,836 DEBUG: Start: Classification
2016-08-25 09:56:25,691 DEBUG: Info: Time for Classification: 9.87491297722[s]
2016-08-25 09:56:25,691 DEBUG: Done: Classification
2016-08-25 09:56:26,261 DEBUG: Start: Statistic Results
2016-08-25 09:56:26,262 INFO: Accuracy :0.885714285714
2016-08-25 09:56:26,277 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:26,277 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.9, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
2016-08-25 09:56:26,277 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:26,288 DEBUG: Info: Shape X_train:(312, 25978), Length of y_train:312
2016-08-25 09:56:26,289 DEBUG: Info: Shape X_test:(35, 25978), Length of y_test:35
2016-08-25 09:56:26,289 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:26,289 DEBUG: Start: Classification
Code/MonoMutliViewClassifiers/Results/20160825-095629-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log 0 → 100644
+ 19
− 0
View file @ 30614dd6
2016-08-25 09:56:29,880 INFO: Start: Finding all available mono- & multiview algorithms
2016-08-25 09:56:29,893 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:29,893 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
2016-08-25 09:56:29,893 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:29,915 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:56:29,915 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:56:29,915 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:29,915 DEBUG: Start: Classification
2016-08-25 09:56:37,193 DEBUG: Info: Time for Classification: 7.29693698883[s]
2016-08-25 09:56:37,193 DEBUG: Done: Classification
2016-08-25 09:56:37,198 DEBUG: Start: Statistic Results
2016-08-25 09:56:37,199 INFO: Accuracy :0.838095238095
2016-08-25 09:56:37,212 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:37,212 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
2016-08-25 09:56:37,212 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:37,226 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:56:37,226 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:56:37,226 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:37,226 DEBUG: Start: Classification
Code/MonoMutliViewClassifiers/Results/20160825-095646-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log 0 → 100644
+ 43
− 0
View file @ 30614dd6
2016-08-25 09:56:46,701 INFO: Start: Finding all available mono- & multiview algorithms
2016-08-25 09:56:47,338 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:47,339 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
2016-08-25 09:56:47,339 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:47,392 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:56:47,393 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:56:47,393 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:47,393 DEBUG: Start: Classification
2016-08-25 09:56:47,398 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:47,398 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
2016-08-25 09:56:47,398 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:47,459 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:47,459 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
2016-08-25 09:56:47,459 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:47,514 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:47,515 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
2016-08-25 09:56:47,515 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:47,584 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:56:47,584 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:56:47,584 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:47,584 DEBUG: Start: Classification
2016-08-25 09:56:47,713 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:56:47,713 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:56:47,714 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:47,714 DEBUG: Start: Classification
2016-08-25 09:56:47,738 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:56:47,738 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:56:47,738 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:47,738 DEBUG: Start: Classification
2016-08-25 09:56:51,086 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:51,087 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
2016-08-25 09:56:51,087 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:51,139 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:56:51,139 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:56:51,139 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:51,140 DEBUG: Start: Classification
2016-08-25 09:56:51,192 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:56:51,192 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
2016-08-25 09:56:51,192 DEBUG: Start: Determine Train/Test split
2016-08-25 09:56:51,245 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:56:51,246 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:56:51,246 DEBUG: Done: Determine Train/Test split
2016-08-25 09:56:51,246 DEBUG: Start: Classification
Code/MonoMutliViewClassifiers/Results/20160825-095702-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log 0 → 100644
+ 261
− 0
View file @ 30614dd6
2016-08-25 09:57:02,550 INFO: Start: Finding all available mono- & multiview algorithms
2016-08-25 09:57:02,932 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:02,933 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
2016-08-25 09:57:02,933 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:02,947 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:57:02,947 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:57:02,947 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:02,947 DEBUG: Start: Classification
2016-08-25 09:57:09,651 DEBUG: Info: Time for Classification: 6.63170599937[s]
2016-08-25 09:57:09,651 DEBUG: Done: Classification
2016-08-25 09:57:09,682 DEBUG: Start: Statistic Results
2016-08-25 09:57:09,682 INFO: Accuracy :0.838095238095
2016-08-25 09:57:09,694 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:09,694 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
2016-08-25 09:57:09,694 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:09,705 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:57:09,705 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:57:09,705 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:09,705 DEBUG: Start: Classification
2016-08-25 09:57:14,510 DEBUG: Info: Time for Classification: 4.81275606155[s]
2016-08-25 09:57:14,510 DEBUG: Done: Classification
2016-08-25 09:57:14,513 DEBUG: Start: Statistic Results
2016-08-25 09:57:14,514 INFO: Accuracy :0.809523809524
2016-08-25 09:57:14,526 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:14,526 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
2016-08-25 09:57:14,526 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:14,538 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:57:14,538 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:57:14,538 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:14,538 DEBUG: Start: Classification
2016-08-25 09:57:20,888 DEBUG: Info: Time for Classification: 6.3594751358[s]
2016-08-25 09:57:20,889 DEBUG: Done: Classification
2016-08-25 09:57:22,151 DEBUG: Start: Statistic Results
2016-08-25 09:57:22,152 INFO: Accuracy :0.87619047619
2016-08-25 09:57:22,160 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:22,160 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
2016-08-25 09:57:22,160 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:22,172 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:57:22,172 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:57:22,172 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:22,172 DEBUG: Start: Classification
2016-08-25 09:57:27,748 DEBUG: Info: Time for Classification: 5.58509707451[s]
2016-08-25 09:57:27,748 DEBUG: Done: Classification
2016-08-25 09:57:27,763 DEBUG: Start: Statistic Results
2016-08-25 09:57:27,764 INFO: Accuracy :0.87619047619
2016-08-25 09:57:27,772 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:27,773 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
2016-08-25 09:57:27,773 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:27,785 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:57:27,785 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:57:27,786 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:27,786 DEBUG: Start: Classification
2016-08-25 09:57:28,974 DEBUG: Info: Time for Classification: 1.19820904732[s]
2016-08-25 09:57:28,975 DEBUG: Done: Classification
2016-08-25 09:57:28,984 DEBUG: Start: Statistic Results
2016-08-25 09:57:28,984 INFO: Accuracy :0.904761904762
2016-08-25 09:57:28,994 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:28,994 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
2016-08-25 09:57:28,994 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:29,009 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:57:29,010 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:57:29,010 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:29,010 DEBUG: Start: Classification
2016-08-25 09:57:33,704 DEBUG: Info: Time for Classification: 4.70660495758[s]
2016-08-25 09:57:33,704 DEBUG: Done: Classification
2016-08-25 09:57:34,033 DEBUG: Start: Statistic Results
2016-08-25 09:57:34,033 INFO: Accuracy :0.885714285714
2016-08-25 09:57:34,042 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:34,042 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
2016-08-25 09:57:34,042 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:34,055 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:57:34,055 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:57:34,055 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:34,055 DEBUG: Start: Classification
2016-08-25 09:57:49,625 DEBUG: Info: Time for Classification: 15.5797770023[s]
2016-08-25 09:57:49,625 DEBUG: Done: Classification
2016-08-25 09:57:49,953 DEBUG: Start: Statistic Results
2016-08-25 09:57:49,954 INFO: Accuracy :0.92380952381
2016-08-25 09:57:49,962 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:49,962 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:MiRNA_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
2016-08-25 09:57:49,962 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:49,975 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 09:57:49,975 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 09:57:49,975 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:49,975 DEBUG: Start: Classification
2016-08-25 09:57:54,196 DEBUG: Info: Time for Classification: 4.2311091423[s]
2016-08-25 09:57:54,197 DEBUG: Done: Classification
2016-08-25 09:57:54,518 DEBUG: Start: Statistic Results
2016-08-25 09:57:54,519 INFO: Accuracy :0.87619047619
2016-08-25 09:57:54,706 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:54,706 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
2016-08-25 09:57:54,706 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:54,707 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
2016-08-25 09:57:54,707 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
2016-08-25 09:57:54,707 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:54,707 DEBUG: Start: Classification
2016-08-25 09:57:54,944 DEBUG: Info: Time for Classification: 0.235062122345[s]
2016-08-25 09:57:54,944 DEBUG: Done: Classification
2016-08-25 09:57:54,946 DEBUG: Start: Statistic Results
2016-08-25 09:57:54,946 INFO: Accuracy :0.761904761905
2016-08-25 09:57:54,947 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:54,947 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
2016-08-25 09:57:54,948 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:54,948 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
2016-08-25 09:57:54,948 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
2016-08-25 09:57:54,948 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:54,948 DEBUG: Start: Classification
2016-08-25 09:57:55,124 DEBUG: Info: Time for Classification: 0.173202037811[s]
2016-08-25 09:57:55,124 DEBUG: Done: Classification
2016-08-25 09:57:55,125 DEBUG: Start: Statistic Results
2016-08-25 09:57:55,125 INFO: Accuracy :0.742857142857
2016-08-25 09:57:55,127 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:55,127 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
2016-08-25 09:57:55,127 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:55,127 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
2016-08-25 09:57:55,127 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
2016-08-25 09:57:55,128 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:55,128 DEBUG: Start: Classification
2016-08-25 09:57:55,347 DEBUG: Info: Time for Classification: 0.216780900955[s]
2016-08-25 09:57:55,347 DEBUG: Done: Classification
2016-08-25 09:57:55,386 DEBUG: Start: Statistic Results
2016-08-25 09:57:55,386 INFO: Accuracy :0.790476190476
2016-08-25 09:57:55,388 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:55,388 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
2016-08-25 09:57:55,388 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:55,388 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
2016-08-25 09:57:55,389 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
2016-08-25 09:57:55,389 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:55,389 DEBUG: Start: Classification
2016-08-25 09:57:58,105 DEBUG: Info: Time for Classification: 2.71416091919[s]
2016-08-25 09:57:58,105 DEBUG: Done: Classification
2016-08-25 09:57:58,123 DEBUG: Start: Statistic Results
2016-08-25 09:57:58,123 INFO: Accuracy :0.87619047619
2016-08-25 09:57:58,124 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:58,125 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
2016-08-25 09:57:58,125 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:58,125 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
2016-08-25 09:57:58,125 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
2016-08-25 09:57:58,125 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:58,126 DEBUG: Start: Classification
2016-08-25 09:57:58,207 DEBUG: Info: Time for Classification: 0.0786328315735[s]
2016-08-25 09:57:58,207 DEBUG: Done: Classification
2016-08-25 09:57:58,208 DEBUG: Start: Statistic Results
2016-08-25 09:57:58,209 INFO: Accuracy :0.819047619048
2016-08-25 09:57:58,210 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:57:58,210 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
2016-08-25 09:57:58,210 DEBUG: Start: Determine Train/Test split
2016-08-25 09:57:58,211 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
2016-08-25 09:57:58,211 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
2016-08-25 09:57:58,211 DEBUG: Done: Determine Train/Test split
2016-08-25 09:57:58,211 DEBUG: Start: Classification
2016-08-25 09:58:01,744 DEBUG: Info: Time for Classification: 3.5306661129[s]
2016-08-25 09:58:01,744 DEBUG: Done: Classification
2016-08-25 09:58:01,752 DEBUG: Start: Statistic Results
2016-08-25 09:58:01,752 INFO: Accuracy :0.819047619048
2016-08-25 09:58:01,754 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:58:01,754 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
2016-08-25 09:58:01,754 DEBUG: Start: Determine Train/Test split
2016-08-25 09:58:01,754 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
2016-08-25 09:58:01,755 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
2016-08-25 09:58:01,755 DEBUG: Done: Determine Train/Test split
2016-08-25 09:58:01,755 DEBUG: Start: Classification
2016-08-25 09:58:04,956 DEBUG: Info: Time for Classification: 3.19874405861[s]
2016-08-25 09:58:04,956 DEBUG: Done: Classification
2016-08-25 09:58:04,964 DEBUG: Start: Statistic Results
2016-08-25 09:58:04,965 INFO: Accuracy :0.847619047619
2016-08-25 09:58:04,966 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:58:04,966 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RNASeq train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
2016-08-25 09:58:04,966 DEBUG: Start: Determine Train/Test split
2016-08-25 09:58:04,967 DEBUG: Info: Shape X_train:(242, 1046), Length of y_train:242
2016-08-25 09:58:04,967 DEBUG: Info: Shape X_test:(105, 1046), Length of y_test:105
2016-08-25 09:58:04,967 DEBUG: Done: Determine Train/Test split
2016-08-25 09:58:04,967 DEBUG: Start: Classification
2016-08-25 09:58:05,317 DEBUG: Info: Time for Classification: 0.347983837128[s]
2016-08-25 09:58:05,317 DEBUG: Done: Classification
2016-08-25 09:58:05,346 DEBUG: Start: Statistic Results
2016-08-25 09:58:05,346 INFO: Accuracy :0.780952380952
2016-08-25 09:58:06,528 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:58:06,529 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
2016-08-25 09:58:06,529 DEBUG: Start: Determine Train/Test split
2016-08-25 09:58:06,586 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
2016-08-25 09:58:06,586 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
2016-08-25 09:58:06,586 DEBUG: Done: Determine Train/Test split
2016-08-25 09:58:06,586 DEBUG: Start: Classification
2016-08-25 09:58:30,020 DEBUG: Info: Time for Classification: 23.4878640175[s]
2016-08-25 09:58:30,020 DEBUG: Done: Classification
2016-08-25 09:58:30,031 DEBUG: Start: Statistic Results
2016-08-25 09:58:30,031 INFO: Accuracy :0.552380952381
2016-08-25 09:58:30,066 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:58:30,066 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
2016-08-25 09:58:30,066 DEBUG: Start: Determine Train/Test split
2016-08-25 09:58:30,103 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
2016-08-25 09:58:30,103 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
2016-08-25 09:58:30,103 DEBUG: Done: Determine Train/Test split
2016-08-25 09:58:30,103 DEBUG: Start: Classification
2016-08-25 09:58:44,565 DEBUG: Info: Time for Classification: 14.4965980053[s]
2016-08-25 09:58:44,566 DEBUG: Done: Classification
2016-08-25 09:58:44,572 DEBUG: Start: Statistic Results
2016-08-25 09:58:44,572 INFO: Accuracy :0.67619047619
2016-08-25 09:58:44,602 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:58:44,602 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : KNN
2016-08-25 09:58:44,602 DEBUG: Start: Determine Train/Test split
2016-08-25 09:58:44,635 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
2016-08-25 09:58:44,635 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
2016-08-25 09:58:44,635 DEBUG: Done: Determine Train/Test split
2016-08-25 09:58:44,635 DEBUG: Start: Classification
2016-08-25 09:59:01,389 DEBUG: Info: Time for Classification: 16.784001112[s]
2016-08-25 09:59:01,389 DEBUG: Done: Classification
2016-08-25 09:59:04,830 DEBUG: Start: Statistic Results
2016-08-25 09:59:04,831 INFO: Accuracy :0.733333333333
2016-08-25 09:59:04,871 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:59:04,871 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : RandomForest
2016-08-25 09:59:04,872 DEBUG: Start: Determine Train/Test split
2016-08-25 09:59:04,905 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
2016-08-25 09:59:04,905 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
2016-08-25 09:59:04,905 DEBUG: Done: Determine Train/Test split
2016-08-25 09:59:04,905 DEBUG: Start: Classification
2016-08-25 09:59:12,387 DEBUG: Info: Time for Classification: 7.51290988922[s]
2016-08-25 09:59:12,388 DEBUG: Done: Classification
2016-08-25 09:59:12,396 DEBUG: Start: Statistic Results
2016-08-25 09:59:12,396 INFO: Accuracy :0.619047619048
2016-08-25 09:59:12,427 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:59:12,427 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SGD
2016-08-25 09:59:12,427 DEBUG: Start: Determine Train/Test split
2016-08-25 09:59:12,461 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
2016-08-25 09:59:12,461 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
2016-08-25 09:59:12,461 DEBUG: Done: Determine Train/Test split
2016-08-25 09:59:12,461 DEBUG: Start: Classification
2016-08-25 09:59:14,501 DEBUG: Info: Time for Classification: 2.07080006599[s]
2016-08-25 09:59:14,501 DEBUG: Done: Classification
2016-08-25 09:59:14,527 DEBUG: Start: Statistic Results
2016-08-25 09:59:14,530 INFO: Accuracy :0.514285714286
2016-08-25 09:59:14,563 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:59:14,563 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMLinear
2016-08-25 09:59:14,563 DEBUG: Start: Determine Train/Test split
2016-08-25 09:59:14,603 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
2016-08-25 09:59:14,603 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
2016-08-25 09:59:14,604 DEBUG: Done: Determine Train/Test split
2016-08-25 09:59:14,604 DEBUG: Start: Classification
2016-08-25 09:59:34,815 DEBUG: Info: Time for Classification: 20.248374939[s]
2016-08-25 09:59:34,815 DEBUG: Done: Classification
2016-08-25 09:59:36,226 DEBUG: Start: Statistic Results
2016-08-25 09:59:36,226 INFO: Accuracy :0.666666666667
2016-08-25 09:59:36,264 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 09:59:36,264 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMPoly
2016-08-25 09:59:36,264 DEBUG: Start: Determine Train/Test split
2016-08-25 09:59:36,298 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
2016-08-25 09:59:36,298 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
2016-08-25 09:59:36,298 DEBUG: Done: Determine Train/Test split
2016-08-25 09:59:36,298 DEBUG: Start: Classification
2016-08-25 10:00:47,095 DEBUG: Info: Time for Classification: 70.827589035[s]
2016-08-25 10:00:47,095 DEBUG: Done: Classification
2016-08-25 10:00:48,573 DEBUG: Start: Statistic Results
2016-08-25 10:00:48,573 INFO: Accuracy :0.638095238095
2016-08-25 10:00:48,609 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 10:00:48,609 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:Clinic train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : SVMRBF
2016-08-25 10:00:48,609 DEBUG: Start: Determine Train/Test split
2016-08-25 10:00:48,644 DEBUG: Info: Shape X_train:(242, 73599), Length of y_train:242
2016-08-25 10:00:48,645 DEBUG: Info: Shape X_test:(105, 73599), Length of y_test:105
2016-08-25 10:00:48,645 DEBUG: Done: Determine Train/Test split
2016-08-25 10:00:48,645 DEBUG: Start: Classification
Code/MonoMutliViewClassifiers/Results/20160825-100239-CMultiV-Benchmark-Methyl_MiRNA__RNASeq_Clinic-ModifiedMultiOmic-LOG.log 0 → 100644
+ 19
− 0
View file @ 30614dd6
2016-08-25 10:02:39,609 INFO: Start: Finding all available mono- & multiview algorithms
2016-08-25 10:02:40,264 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 10:02:40,264 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RANSeq_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : Adaboost
2016-08-25 10:02:40,264 DEBUG: Start: Determine Train/Test split
2016-08-25 10:02:40,278 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 10:02:40,279 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 10:02:40,279 DEBUG: Done: Determine Train/Test split
2016-08-25 10:02:40,279 DEBUG: Start: Classification
2016-08-25 10:02:46,914 DEBUG: Info: Time for Classification: 6.64618396759[s]
2016-08-25 10:02:46,914 DEBUG: Done: Classification
2016-08-25 10:02:46,919 DEBUG: Start: Statistic Results
2016-08-25 10:02:46,920 INFO: Accuracy :0.828571428571
2016-08-25 10:02:46,931 DEBUG: ### Main Programm for Classification MonoView
2016-08-25 10:02:46,931 DEBUG: ### Classification - Database:ModifiedMultiOmic Feature:RANSeq_ train_size:0.7, CrossValidation k-folds:2, cores:1, algorithm : DecisionTree
2016-08-25 10:02:46,931 DEBUG: Start: Determine Train/Test split
2016-08-25 10:02:46,942 DEBUG: Info: Shape X_train:(242, 25978), Length of y_train:242
2016-08-25 10:02:46,943 DEBUG: Info: Shape X_test:(105, 25978), Length of y_test:105
2016-08-25 10:02:46,943 DEBUG: Done: Determine Train/Test split
2016-08-25 10:02:46,943 DEBUG: Start: Classification
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please to comment