Added perdict probas hdf5 for mumbo and fusion (only early fusion)

Code/MonoMutliViewClassifiers/Multiview/Fusion/Fusion.py

@@ -41,6 +41,15 @@ class Fusion:
             predictedLabels = []
         return predictedLabels
 
+    def predict_probas_hdf5(self, DATASET, usedIndices=None):
+        if usedIndices == None:
+            usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
+        if usedIndices:
+            predictedLabels = self.classifier.predict_probas_hdf5(DATASET, usedIndices=usedIndices)
+        else:
+            predictedLabels = []
+        return predictedLabels
+
     def predict(self, DATASET, classifier, NB_CLASS):
         fusionType, fusionMethod, fusionClassifier = classifier
         fusionType = globals()[fusionType]  # Early/late fusion

Code/MonoMutliViewClassifiers/Multiview/Fusion/Methods/EarlyFusion.py

@@ -53,10 +53,20 @@ class WeightedLinear(EarlyFusionClassifier):
             predictedLabels=[]
         return predictedLabels
 
+    def predict_proba_hdf5(self, DATASET, usedIndices=None):
+        if usedIndices == None:
+            usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
+        if usedIndices:
+            self.makeMonoviewData_hdf5(DATASET, weights=self.weights, usedIndices=usedIndices)
+            predictedLabels = self.monoviewClassifier.predict_proba(self.monoviewData)
+        else:
+            predictedLabels=[]
+        return predictedLabels
+
     def getConfig(self, fusionMethodConfig ,monoviewClassifiersNames, monoviewClassifiersConfigs):
         configString = "with weighted concatenation, using weights : "+", ".join(map(str, self.weights))+\
                        " with monoview classifier : "
-        monoviewClassifierModule = getattr(poulet, monoviewClassifiersNames[0])
+        monoviewClassifierModule = getattr(MonoviewClassifiers, monoviewClassifiersNames[0])
         configString += monoviewClassifierModule.getConfig(monoviewClassifiersConfigs[0])
         return configString
 

Code/MonoMutliViewClassifiers/Multiview/Mumbo/Mumbo.py

@@ -206,6 +206,25 @@ class Mumbo:
             predictedLabels = []
         return predictedLabels
 
+    def predict_proba_hdf5(self, DATASET, usedIndices=None):
+        NB_CLASS = DATASET.get("Metadata").attrs["nbClass"]
+        if usedIndices == None:
+            usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
+        if usedIndices:
+            DATASET_LENGTH = len(usedIndices)
+            predictedProbas = np.zeros((DATASET_LENGTH, NB_CLASS))
+
+            for labelIndex, exampleIndex in enumerate(usedIndices):
+                votes = np.zeros(NB_CLASS)
+                for classifier, alpha, view in zip(self.bestClassifiers, self.alphas, self.bestViews):
+                    data = DATASET["/View"+str(int(view))][exampleIndex, :]
+                    predictedProbas[labelIndex, int(classifier.predict(np.array([data])))] += alpha[view]
+                predictedProbas[labelIndex,:] = predictedProbas[labelIndex,:]/np.sum(predictedProbas[labelIndex,:])
+        else:
+            predictedProbas = []
+        return predictedProbas
+
+
     def trainWeakClassifiers(self, DATASET, CLASS_LABELS, NB_CLASS, DATASET_LENGTH, NB_VIEW):
         trainedClassifiers = []
         labelsMatrix = []