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Commit fd1103b3 authored by Baptiste Bauvin's avatar Baptiste Bauvin
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Added logging commentary

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Code/MonoMultiViewClassifiers/ExecClassif.py
+ 18
− 8
View file @ fd1103b3
...@@ -171,16 +171,16 @@ def classifyOneIter_multicore(LABELS_DICTIONARY, argumentDictionaries, nbCores, ...@@ -171,16 +171,16 @@ def classifyOneIter_multicore(LABELS_DICTIONARY, argumentDictionaries, nbCores,
times = [dataBaseTime, monoviewTime, multiviewTime] times = [dataBaseTime, monoviewTime, multiviewTime]
results = (resultsMonoview, resultsMultiview) results = (resultsMonoview, resultsMultiview)
labelAnalysis = analyzeLabels(labels, trueLabels, results, directory) labelAnalysis = analyzeLabels(labels, trueLabels, results, directory)
logging.debug("Start:\t Analyze Global Results for iteration") logging.debug("Start:\t Analyze Iteration Results")
resultAnalysis(benchmark, results, args.name, times, metrics, directory) resultAnalysis(benchmark, results, args.name, times, metrics, directory)
logging.debug("Done:\t Analyze Global Results for iteration") logging.debug("Done:\t Analyze Iteration Results")
globalAnalysisTime = time.time() - monoviewTime - dataBaseTime - start - multiviewTime globalAnalysisTime = time.time() - monoviewTime - dataBaseTime - start - multiviewTime
totalTime = time.time() - start totalTime = time.time() - start
logging.info("Extraction time : " + str(dataBaseTime) + logging.info("Extraction time : " + str(dataBaseTime) +
"s, Monoview time : " + str(monoviewTime) + "s, Monoview time : " + str(monoviewTime) +
"s, Multiview Time : " + str(multiviewTime) + "s, Multiview Time : " + str(multiviewTime) +
"s, Global Analysis Time : " + str(globalAnalysisTime) + "s, Iteration Analysis Time : " + str(globalAnalysisTime) +
"s, Total Duration : " + str(totalTime) + "s") "s, Iteration Duration : " + str(totalTime) + "s")
return results, labelAnalysis return results, labelAnalysis
...@@ -245,16 +245,16 @@ def classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory, ...@@ -245,16 +245,16 @@ def classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory,
times = [dataBaseTime, monoviewTime, multiviewTime] times = [dataBaseTime, monoviewTime, multiviewTime]
results = (resultsMonoview, resultsMultiview) results = (resultsMonoview, resultsMultiview)
labelAnalysis = analyzeLabels(labels, trueLabels, results, directory) labelAnalysis = analyzeLabels(labels, trueLabels, results, directory)
logging.debug("Start:\t Analyze Global Results") logging.debug("Start:\t Analyze Iteration Results")
resultAnalysis(benchmark, results, args.name, times, metrics, directory) resultAnalysis(benchmark, results, args.name, times, metrics, directory)
logging.debug("Done:\t Analyze Global Results") logging.debug("Done:\t Analyze Iteration Results")
globalAnalysisTime = time.time() - monoviewTime - dataBaseTime - start - multiviewTime globalAnalysisTime = time.time() - monoviewTime - dataBaseTime - start - multiviewTime
totalTime = time.time() - start totalTime = time.time() - start
logging.info("Extraction time : " + str(dataBaseTime) + logging.info("Extraction time : " + str(dataBaseTime) +
"s, Monoview time : " + str(monoviewTime) + "s, Monoview time : " + str(monoviewTime) +
"s, Multiview Time : " + str(multiviewTime) + "s, Multiview Time : " + str(multiviewTime) +
"s, Global Analysis Time : " + str(globalAnalysisTime) + "s, Iteration Analysis Time : " + str(globalAnalysisTime) +
"s, Total Duration : " + str(totalTime) + "s") "s, Iteration Duration : " + str(totalTime) + "s")
return results, labelAnalysis return results, labelAnalysis
...@@ -324,6 +324,7 @@ def execClassif(arguments): ...@@ -324,6 +324,7 @@ def execClassif(arguments):
directories = execution.genDirecortiesNames(directory, statsIter) directories = execution.genDirecortiesNames(directory, statsIter)
if statsIter > 1: if statsIter > 1:
logging.debug("Start:\t Benchmark classification")
for statIterIndex in range(statsIter): for statIterIndex in range(statsIter):
if not os.path.exists(os.path.dirname(directories[statIterIndex] + "train_labels.csv")): if not os.path.exists(os.path.dirname(directories[statIterIndex] + "train_labels.csv")):
try: try:
...@@ -368,6 +369,8 @@ def execClassif(arguments): ...@@ -368,6 +369,8 @@ def execClassif(arguments):
classificationIndices[iterIndex], kFolds[iterIndex], statsIterRandomStates[iterIndex], classificationIndices[iterIndex], kFolds[iterIndex], statsIterRandomStates[iterIndex],
hyperParamSearch, metrics, DATASET, viewsIndices, dataBaseTime, start, benchmark, hyperParamSearch, metrics, DATASET, viewsIndices, dataBaseTime, start, benchmark,
views)) views))
logging.debug("Done:\t Benchmark classification")
logging.debug("Start:\t Global Results Analysis")
classifiersIterResults = [] classifiersIterResults = []
iterLabelAnalysis = [] iterLabelAnalysis = []
for result in iterResults: for result in iterResults:
...@@ -378,8 +381,12 @@ def execClassif(arguments): ...@@ -378,8 +381,12 @@ def execClassif(arguments):
classifiersNames = genNamesFromRes(mono, multi) classifiersNames = genNamesFromRes(mono, multi)
analyzeIterLabels(iterLabelAnalysis, directory, classifiersNames) analyzeIterLabels(iterLabelAnalysis, directory, classifiersNames)
analyzeIterResults(classifiersIterResults, args.name, metrics, directory) analyzeIterResults(classifiersIterResults, args.name, metrics, directory)
logging.debug("Done:\t Global Results Analysis")
totalDur = time.time()-start
logging.info("Info:\t Total duration : "+str(totalDur))
else: else:
logging.debug("Start:\t Benchmark classification")
if not os.path.exists(os.path.dirname(directories + "train_labels.csv")): if not os.path.exists(os.path.dirname(directories + "train_labels.csv")):
try: try:
os.makedirs(os.path.dirname(directories + "train_labels.csv")) os.makedirs(os.path.dirname(directories + "train_labels.csv"))
...@@ -393,6 +400,9 @@ def execClassif(arguments): ...@@ -393,6 +400,9 @@ def execClassif(arguments):
kFolds, kFolds,
statsIterRandomStates, hyperParamSearch, metrics, DATASET, viewsIndices, dataBaseTime, start, statsIterRandomStates, hyperParamSearch, metrics, DATASET, viewsIndices, dataBaseTime, start,
benchmark, views) benchmark, views)
logging.debug("Done:\t Benchmark classification")
totalDur = time.time()-start
logging.info("Info:\t Total duration : "+str(totalDur))
if statsIter > 1: if statsIter > 1:
pass pass
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