Update analysis for multiview and commented binned

Code/MonoMutliViewClassifiers/Multiview/Fusion/analyzeResults.py

@@ -20,16 +20,19 @@ def error(testLabels, computedLabels):
     return float(error) * 100 / len(computedLabels)
 
 
-def getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred):
+def printMetricScore(metricScores, metrics):
+    metricScoreString = "\n\n"
+    for metric in metrics:
         metricModule = getattr(Metrics, metric[0])
         if metric[1]!=None:
             metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
         else:
             metricKWARGS = {}
-    metricScoreString = "\tFor "+metricModule.getConfig(**metricKWARGS)+" : "
-    metricScoreString += "\n\t\t- Score on train : "+str(metricModule.score(y_train, y_train_pred))
-    metricScoreString += "\n\t\t- Score on test : "+str(metricModule.score(y_test, y_test_pred))
-    metricScoreString += "\n"
+        metricScoreString += "\tFor "+metricModule.getConfig(**metricKWARGS)+" : "
+        metricScoreString += "\n\t\t- Score on train : "+str(metricScores[metric[0]][0])
+        metricScoreString += "\n\t\t- Score on test : "+str(metricScores[metric[0]][1])
+        metricScoreString += "\n\t\t- Score on validation : "+str(metricScores[metric[0]][2])
+        metricScoreString += "\n\n"
     return metricScoreString
 
 
@@ -104,7 +107,9 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels,
 
     if fusionType=="LateFusion":
         stringAnalysis+=Methods.LateFusion.getAccuracies(kFoldClassifier)
-
+    metricsScores = getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+                                     kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
+    stringAnalysis+=printMetricScore(metricsScores, metrics)
     stringAnalysis += "\n\nComputation time on " + str(nbCores) + " cores : \n\tDatabase extraction time : " + str(
         hms(seconds=int(extractionTime))) + "\n\t"
     row_format = "{:>15}" * 3
@@ -118,6 +123,4 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels,
                                                    str(hms(seconds=int(sum(kFoldPredictionTime))))])
     stringAnalysis += "\n\tSo a total classification time of " + str(hms(seconds=int(classificationTime))) + ".\n\n"
     imagesAnalysis = {}
-    metricsScores = getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
-                                     kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
     return stringAnalysis, imagesAnalysis, metricsScores

Code/MonoMutliViewClassifiers/Multiview/GetMultiviewDb.py

@@ -430,16 +430,6 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
     mrnaseqDset.attrs["sparse"] = False
     logging.debug("Done:\t Getting Sorted RNASeq Data")
 
-    logging.debug("Start:\t Getting Binned RNASeq Data")
-    sparseBinnedRNASeq = makeSparseTotalMatrix(modifiedRNASeq)
-    sparseBinnedRNASeqGrp = datasetFile.create_group("View6")
-    dataDset = sparseBinnedRNASeqGrp.create_dataset("data", sparseBinnedRNASeq.data.shape, data=sparseBinnedRNASeq.data)
-    indicesDset = sparseBinnedRNASeqGrp.create_dataset("indices", sparseBinnedRNASeq.indices.shape, data=sparseBinnedRNASeq.indices)
-    indptrDset = sparseBinnedRNASeqGrp.create_dataset("indptr", sparseBinnedRNASeq.indptr.shape, data=sparseBinnedRNASeq.indptr)
-    sparseBinnedRNASeqGrp.attrs["name"]="BRNASeq"
-    sparseBinnedRNASeqGrp.attrs["sparse"]=True
-    sparseBinnedRNASeqGrp.attrs["shape"]=sparseBinnedRNASeq.shape
-    logging.debug("Done:\t Getting Binned RNASeq Data")
 
     logging.debug("Start:\t Getting Binarized RNASeq Data")
     k=127
@@ -455,6 +445,16 @@ def getModifiedMultiOmicDBcsv(features, path, name, NB_CLASS, LABELS_NAMES):
     brnaseqDset.attrs["sparse"] = False
     logging.debug("Done:\t Getting Binarized RNASeq Data")
 
+    # logging.debug("Start:\t Getting Binned RNASeq Data")
+    # sparseBinnedRNASeq = makeSparseTotalMatrix(modifiedRNASeq)
+    # sparseBinnedRNASeqGrp = datasetFile.create_group("View6")
+    # dataDset = sparseBinnedRNASeqGrp.create_dataset("data", sparseBinnedRNASeq.data.shape, data=sparseBinnedRNASeq.data)
+    # indicesDset = sparseBinnedRNASeqGrp.create_dataset("indices", sparseBinnedRNASeq.indices.shape, data=sparseBinnedRNASeq.indices)
+    # indptrDset = sparseBinnedRNASeqGrp.create_dataset("indptr", sparseBinnedRNASeq.indptr.shape, data=sparseBinnedRNASeq.indptr)
+    # sparseBinnedRNASeqGrp.attrs["name"]="BRNASeq"
+    # sparseBinnedRNASeqGrp.attrs["sparse"]=True
+    # sparseBinnedRNASeqGrp.attrs["shape"]=sparseBinnedRNASeq.shape
+    # logging.debug("Done:\t Getting Binned RNASeq Data")
 
     labelFile = open(path+'brca_labels_triple-negatif.csv')
     labels = np.array([int(line.strip().split(',')[1]) for line in labelFile])

Code/MonoMutliViewClassifiers/Multiview/Mumbo/analyzeResults.py

@@ -221,16 +221,19 @@ def modifiedMean(surplusAccuracies):
     return meanAccuracies
 
 
-def getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred):
+def printMetricScore(metricScores, metrics):
+    metricScoreString = "\n\n"
+    for metric in metrics:
         metricModule = getattr(Metrics, metric[0])
         if metric[1]!=None:
             metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
         else:
             metricKWARGS = {}
-    metricScoreString = "\tFor "+metricModule.getConfig(**metricKWARGS)+" : "
-    metricScoreString += "\n\t\t- Score on train : "+str(metricModule.score(y_train, y_train_pred))
-    metricScoreString += "\n\t\t- Score on test : "+str(metricModule.score(y_test, y_test_pred))
-    metricScoreString += "\n"
+        metricScoreString += "\tFor "+metricModule.getConfig(**metricKWARGS)+" : "
+        metricScoreString += "\n\t\t- Score on train : "+str(metricScores[metric[0]][0])
+        metricScoreString += "\n\t\t- Score on test : "+str(metricScores[metric[0]][1])
+        metricScoreString += "\n\t\t- Score on validation : "+str(metricScores[metric[0]][2])
+        metricScoreString += "\n\n"
     return metricScoreString
 
 
@@ -298,6 +301,9 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
                      str(totalAccuracyOnTest) + "\n\t-On Validation : " + str(totalAccuracyOnValidation)
     stringAnalysis += dbConfigurationString
     stringAnalysis += algoConfigurationString
+    metricsScores = getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
+                                     kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
+    stringAnalysis += printMetricScore(metricsScores, metrics)
     stringAnalysis += "Mean average accuracies and stats for each fold :"
     for foldIdx in range(nbFolds):
         stringAnalysis += "\n\t- Fold "+str(foldIdx)+", used "+str(kFoldClassifier[foldIdx].iterIndex + 1)
@@ -322,6 +328,4 @@ def execute(kFoldClassifier, kFoldPredictedTrainLabels, kFoldPredictedTestLabels
     name, image = plotAccuracyByIter(trainAccuracyByIter, testAccuracyByIter, validationAccuracyByIter, nbMaxIter,
                                      bestViews, views, classifierAnalysis)
     imagesAnalysis = {name: image}
-    metricsScores = getMetricsScores(metrics, kFoldPredictedTrainLabels, kFoldPredictedTestLabels,
-                                     kFoldPredictedValidationLabels, DATASET, validationIndices, kFolds)
     return stringAnalysis, imagesAnalysis, metricsScores