diff --git a/Code/MonoMutliViewClassifiers/ExecClassif.py b/Code/MonoMutliViewClassifiers/ExecClassif.py
index 5ed69368220819a2c614d735b49e3b45da9ec7f5..9d61039aeac623d1171a7a67a12c750f71084785 100644
--- a/Code/MonoMutliViewClassifiers/ExecClassif.py
+++ b/Code/MonoMutliViewClassifiers/ExecClassif.py
@@ -24,7 +24,7 @@ from Multiview.ExecMultiview import ExecMultiview, ExecMultiview_multicore
from Monoview.ExecClassifMonoView import ExecMonoview, ExecMonoview_multicore
import Multiview.GetMultiviewDb as DB
from Versions import testVersions
-from ResultAnalysis import resultAnalysis, analyzeLabels
+from ResultAnalysis import resultAnalysis, analyzeLabels, analyzeIterResults
# Author-Info
__author__ = "Baptiste Bauvin"
@@ -278,8 +278,9 @@ def genDirecortiesNames(directory, statsIter):
def classifyOneIter_multicore(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory, args, classificationIndices, kFolds,
randomState, hyperParamSearch, metrics, coreIndex, viewsIndices, dataBaseTime, start, benchmark,
views):
+ resultsMonoview = []
labelsNames = LABELS_DICTIONARY.values()
- resultsMonoview = [ExecMonoview_multicore(directory, args.name, labelsNames, classificationIndices, kFolds,
+ resultsMonoview += [ExecMonoview_multicore(directory, args.name, labelsNames, classificationIndices, kFolds,
coreIndex, args.type, args.pathF, randomState,
hyperParamSearch=hyperParamSearch,
metrics=metrics, nIter=args.CL_GS_iter,
@@ -289,7 +290,8 @@ def classifyOneIter_multicore(LABELS_DICTIONARY, argumentDictionaries, nbCores,
argumentDictionaries = initMultiviewArguments(args, benchmark, views, viewsIndices, argumentDictionaries, randomState, directory, resultsMonoview)
- resultsMultiview = [
+ resultsMultiview = []
+ resultsMultiview += [
ExecMultiview_multicore(directory, coreIndex, args.name, classificationIndices, kFolds, args.type,
args.pathF, LABELS_DICTIONARY, randomState, hyperParamSearch=hyperParamSearch,
metrics=metrics, nIter=args.CL_GS_iter,**arguments)
@@ -302,17 +304,17 @@ def classifyOneIter_multicore(LABELS_DICTIONARY, argumentDictionaries, nbCores,
trueLabels = DATASET.get("Labels").value
times = [dataBaseTime, monoviewTime, multiviewTime]
results = (resultsMonoview, resultsMultiview)
- analyzeLabels(labels, trueLabels, results, directory)
- logging.debug("Start:\t Analyze Global Results")
- resultAnalysis(benchmark, results, args.name, times, metrics, directory)
- logging.debug("Done:\t Analyze Global Results")
- globalAnalysisTime = time.time() - monoviewTime - dataBaseTime - start - multiviewTime
- totalTime = time.time() - start
- logging.info("Extraction time : "+str(dataBaseTime)+
- "s, Monoview time : "+str(monoviewTime)+
- "s, Multiview Time : "+str(multiviewTime)+
- "s, Global Analysis Time : "+str(globalAnalysisTime)+
- "s, Total Duration : "+str(totalTime)+"s")
+ # analyzeLabels(labels, trueLabels, results, directory)
+ # logging.debug("Start:\t Analyze Global Results for iteration")
+ # resultAnalysis(benchmark, results, args.name, times, metrics, directory)
+ # logging.debug("Done:\t Analyze Global Results for iteration")
+ # globalAnalysisTime = time.time() - monoviewTime - dataBaseTime - start - multiviewTime
+ # totalTime = time.time() - start
+ # logging.info("Extraction time : "+str(dataBaseTime)+
+ # "s, Monoview time : "+str(monoviewTime)+
+ # "s, Multiview Time : "+str(multiviewTime)+
+ # "s, Global Analysis Time : "+str(globalAnalysisTime)+
+ # "s, Total Duration : "+str(totalTime)+"s")
return results
@@ -382,7 +384,6 @@ def classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory,
"s, Multiview Time : "+str(multiviewTime)+
"s, Global Analysis Time : "+str(globalAnalysisTime)+
"s, Total Duration : "+str(totalTime)+"s")
- return results
def initRandomState(randomStateArg, directory):
@@ -642,8 +643,12 @@ if statsIter>1:
logging.debug("Start:\t Deleting " + str(nbCores) + " temporary datasets for multiprocessing")
datasetFiles = DB.deleteHDF5(args.pathF, args.name, nbCores)
logging.debug("Start:\t Deleting datasets for multiprocessing")
+ analyzeIterResults(iterResults, args.name, metrics, directory)
else:
- res = classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directories, args, classificationIndices, kFolds,
- statsIterRandomStates, hyperParamSearch, metrics, DATASET, viewsIndices, dataBaseTime, start,
- benchmark, views)
\ No newline at end of file
+ classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directories, args, classificationIndices, kFolds,
+ statsIterRandomStates, hyperParamSearch, metrics, DATASET, viewsIndices, dataBaseTime, start,
+ benchmark, views)
+
+if statsIter > 1:
+ pass
\ No newline at end of file
diff --git a/Code/MonoMutliViewClassifiers/ResultAnalysis.py b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
index 500423344828ddeefdc3010ecb34a778db65b47e..818860cb1e3729c379d8d291a5badf5c67d24ce2 100644
--- a/Code/MonoMutliViewClassifiers/ResultAnalysis.py
+++ b/Code/MonoMutliViewClassifiers/ResultAnalysis.py
@@ -103,4 +103,61 @@ def analyzeLabels(labelsArrays, realLabels, results, directory):
plt.xticks(ticks, labels, rotation="vertical")
cbar = fig.colorbar(cax, ticks=[0, 1])
cbar.ax.set_yticklabels(['Wrong', ' Right'])
- fig.savefig(directory+time.strftime("%Y%m%d-%H%M%S")+"-error_analysis.png")
\ No newline at end of file
+ fig.savefig(directory+time.strftime("%Y%m%d-%H%M%S")+"-error_analysis.png")
+
+
+def genScoresNames(iterResults, metric, nbResults, names):
+ validationScores = []
+ trainScores = []
+ for iterindex, iterResult in enumerate(iterResults):
+ mono, multi = iterResult
+ import pdb;pdb.set_trace()
+ validationScores[iterindex] = [float(res[1][2][metric[0]][1]) for res in mono]
+ validationScores[iterindex] += [float(scores[metric[0]][1]) for a, b, scores, c in multi]
+ trainScores[iterindex] = [float(res[1][1][2][metric[0]][0]) for res in mono]
+ trainScores[iterindex] += [float(scores[metric[0]][0]) for a, b, scores, c in multi]
+
+ validationScores[iterindex] = np.array(validationScores)
+ trainScores[iterindex] = np.array(trainScores)
+
+ validationScores = np.array(validationScores)
+ trainScores = np.array(trainScores)
+ validationSTDs = np.std(validationScores, axis=0)
+ trainSTDs = np.std(trainScores, axis=0)
+ validationMeans = np.mean(validationScores, axis=0)
+ trainMeans = np.mean(trainScores, axis=0)
+
+ f = pylab.figure(figsize=(40, 30))
+ width = 0.35 # the width of the bars
+ fig = plt.gcf()
+ fig.subplots_adjust(bottom=105.0, top=105.01)
+ ax = f.add_axes([0.1, 0.1, 0.8, 0.8])
+ if metric[1]!=None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ sorted_indices = np.argsort(validationMeans)
+ validationMeans = validationMeans[sorted_indices]
+ trainMeans = trainMeans[sorted_indices]
+ names = names[sorted_indices]
+
+ ax.set_title(getattr(Metrics, metric[0]).getConfig(**metricKWARGS)+" for each classifier")
+ rects = ax.bar(range(nbResults), validationMeans, width, color="r", yerr=validationSTDs)
+ rect2 = ax.bar(np.arange(nbResults)+width, trainMeans, width, color="0.7", yerr=trainSTDs)
+ autolabel(rects, ax)
+ autolabel(rect2, ax)
+ ax.legend((rects[0], rect2[0]), ('Test', 'Train'))
+ ax.set_xticks(np.arange(nbResults)+width)
+ ax.set_xticklabels(names, rotation="vertical")
+
+ return f
+
+
+def analyzeIterResults(iterResults, name, metrics, directory):
+ nbResults = len(iterResults[0][0])+len(iterResults[0][1])
+ nbIter = len(iterResults)
+ names = genNamesFromRes(iterResults[0][0], iterResults[0][1])
+ for metric in metrics:
+ figure = genScoresNames(iterResults, metric, nbResults, names)
+ figure.savefig(directory+time.strftime("%Y%m%d-%H%M%S")+"-"+name+"-Mean_on_"
+ +str(nbIter)+"_iter-"+metric[0]+".png")