Added a label analysis for statsiteration

Code/MonoMultiViewClassifiers/ExecClassif.py

@@ -22,7 +22,7 @@ from . import MonoviewClassifiers
 from .Multiview.ExecMultiview import ExecMultiview, ExecMultiview_multicore
 from .Monoview.ExecClassifMonoView import ExecMonoview, ExecMonoview_multicore
 from .Multiview import GetMultiviewDb as DB
-from ResultAnalysis import resultAnalysis, analyzeLabels, analyzeIterResults
+from ResultAnalysis import resultAnalysis, analyzeLabels, analyzeIterResults, analyzeIterLabels, genNamesFromRes
 from .utils import execution, Dataset
 
 # Author-Info
@@ -170,7 +170,7 @@ def classifyOneIter_multicore(LABELS_DICTIONARY, argumentDictionaries, nbCores,
     trueLabels = DATASET.get("Labels").value
     times = [dataBaseTime, monoviewTime, multiviewTime]
     results = (resultsMonoview, resultsMultiview)
-    analyzeLabels(labels, trueLabels, results, directory)
+    labelAnalysis = 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")
@@ -181,7 +181,7 @@ def classifyOneIter_multicore(LABELS_DICTIONARY, argumentDictionaries, nbCores,
                  "s, Multiview Time : " + str(multiviewTime) +
                  "s, Global Analysis Time : " + str(globalAnalysisTime) +
                  "s, Total Duration : " + str(totalTime) + "s")
-    return results
+    return results, labelAnalysis
 
 
 def classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory, args, classificationIndices, kFolds,
@@ -244,7 +244,7 @@ def classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directory,
     trueLabels = DATASET.get("Labels").value
     times = [dataBaseTime, monoviewTime, multiviewTime]
     results = (resultsMonoview, resultsMultiview)
-    analyzeLabels(labels, trueLabels, results, directory)
+    labelAnalysis = 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")
@@ -255,7 +255,7 @@ 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
+    return results, labelAnalysis
 
 
 # _______________ #
@@ -368,7 +368,16 @@ def execClassif(arguments):
                                     classificationIndices[iterIndex], kFolds[iterIndex], statsIterRandomStates[iterIndex],
                                     hyperParamSearch, metrics, DATASET, viewsIndices, dataBaseTime, start, benchmark,
                                     views))
-        analyzeIterResults(iterResults, args.name, metrics, directory)
+        classifiersIterResults = []
+        iterLabelAnalysis = []
+        for result in iterResults:
+            classifiersIterResults.append(result[0])
+            iterLabelAnalysis.append(result[1])
+
+        mono,multi = classifiersIterResults[0]
+        classifiersNames = genNamesFromRes(mono, multi)
+        analyzeIterLabels(iterLabelAnalysis, directory, classifiersNames)
+        analyzeIterResults(classifiersIterResults, args.name, metrics, directory)
 
     else:
         if not os.path.exists(os.path.dirname(directories + "train_labels.csv")):
@@ -380,7 +389,7 @@ def execClassif(arguments):
         trainIndices, testIndices = classificationIndices
         trainLabels = DATASET.get("Labels").value[trainIndices]
         np.savetxt(directories + "train_labels.csv", trainLabels, delimiter=",")
-        res = classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directories, args, classificationIndices,
+        res, labelAnalysis = classifyOneIter(LABELS_DICTIONARY, argumentDictionaries, nbCores, directories, args, classificationIndices,
                               kFolds,
                               statsIterRandomStates, hyperParamSearch, metrics, DATASET, viewsIndices, dataBaseTime, start,
                               benchmark, views)

Code/MonoMultiViewClassifiers/ResultAnalysis.py

@@ -80,6 +80,27 @@ def resultAnalysis(benchmark, results, name, times, metrics, directory, minSize=
         plt.close()
 
 
+def analyzeIterLabels(labelsAnalysisList, directory, classifiersNames, minSize=10):
+    nbExamples = labelsAnalysisList[0].shape[0]
+    nbClassifiers = len(classifiersNames)
+    nbIter = 2
+
+    figWidth = max(nbClassifiers / 2, minSize)
+    figHeight = max(nbExamples / 20, minSize)
+    figKW = {"figsize": (figWidth, figHeight)}
+    fig, ax = plt.subplots(nrows=1, ncols=1, **figKW)
+    data = sum(labelsAnalysisList)
+    cax = plt.imshow(data, interpolation='none', cmap="grey", aspect='auto')
+    plt.title('Errors depending on the classifier')
+    ticks = np.arange(nbIter/2-0.5, nbClassifiers * nbIter, nbIter)
+    plt.xticks(ticks, classifiersNames, rotation="vertical")
+    cbar = fig.colorbar(cax, ticks=[0, len(labelsAnalysisList)])
+    cbar.ax.set_yticklabels(['Always Wrong', 'Always Right'])
+    fig.tight_layout()
+    fig.savefig(directory + time.strftime("%Y%m%d-%H%M%S") + "-error_analysis.png")
+    plt.close()
+
+
 def analyzeLabels(labelsArrays, realLabels, results, directory, minSize = 10):
     mono, multi = results
     classifiersNames = genNamesFromRes(mono, multi)
@@ -101,7 +122,7 @@ def analyzeLabels(labelsArrays, realLabels, results, directory, minSize = 10):
 
     cax = plt.imshow(data, interpolation='none', cmap=cmap, norm=norm, aspect='auto')
     plt.title('Errors depending on the classifier')
-    ticks = np.arange(0, nbClassifiers * nbIter, nbIter)
+    ticks = np.arange(nbIter/2-0.5, nbClassifiers * nbIter, nbIter)
     labels = classifiersNames
     plt.xticks(ticks, labels, rotation="vertical")
     cbar = fig.colorbar(cax, ticks=[0, 1])
@@ -109,6 +130,7 @@ def analyzeLabels(labelsArrays, realLabels, results, directory, minSize = 10):
     fig.tight_layout()
     fig.savefig(directory + time.strftime("%Y%m%d-%H%M%S") + "-error_analysis.png")
     plt.close()
+    return data
 
 
 def genScoresNames(iterResults, metric, nbResults, names, nbMono, minSize=10):