WE work

Code/MonoMultiViewClassifiers/Metrics/accuracy_score.py

@@ -11,7 +11,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     """Arguments:
     y_true: real labels
     y_pred predicted labels

Code/MonoMultiViewClassifiers/Metrics/f1_score.py

@@ -11,7 +11,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     try:
         sample_weight = kwargs["0"]
     except:
@@ -27,7 +27,7 @@ def score(y_true, y_pred, **kwargs):
     try:
         average = kwargs["3"]
     except:
-        if set(y_true) != {0,1} or (set(y_pred) != {0,1} and set(y_pred) != {0} and set(y_pred) != {1}):
+        if multiclass:
             average = "micro"
         else:
             average = "binary"

Code/MonoMultiViewClassifiers/Metrics/fbeta_score.py

@@ -6,7 +6,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     try:
         sample_weight = kwargs["0"]
     except:
@@ -26,7 +26,7 @@ def score(y_true, y_pred, **kwargs):
     try:
         average = kwargs["4"]
     except:
-        if set(y_true) != {0,1} or (set(y_pred) != {0,1} and set(y_pred) != {0} and set(y_pred) != {1}):
+        if multiclass:
             average = "micro"
         else:
             average = "binary"

Code/MonoMultiViewClassifiers/Metrics/hamming_loss.py

@@ -6,7 +6,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     try:
         classes = kwargs["0"]
     except:

Code/MonoMultiViewClassifiers/Metrics/jaccard_similarity_score.py

@@ -6,7 +6,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     try:
         sample_weight = kwargs["0"]
     except:

Code/MonoMultiViewClassifiers/Metrics/log_loss.py

@@ -6,7 +6,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     try:
         sample_weight = kwargs["0"]
     except:

Code/MonoMultiViewClassifiers/Metrics/matthews_corrcoef.py

@@ -6,7 +6,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     score = metric(y_true, y_pred)
     return score
 

Code/MonoMultiViewClassifiers/Metrics/precision_score.py

@@ -6,7 +6,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     try:
         sample_weight = kwargs["0"]
     except:
@@ -22,7 +22,7 @@ def score(y_true, y_pred, **kwargs):
     try:
         average = kwargs["3"]
     except:
-        if set(y_true) != {0,1} or (set(y_pred) != {0,1} and set(y_pred) != {0} and set(y_pred) != {1}):
+        if multiclass:
             average = "micro"
         else:
             average = "binary"

Code/MonoMultiViewClassifiers/Metrics/recall_score.py

@@ -6,7 +6,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     try:
         sample_weight = kwargs["0"]
     except:
@@ -22,7 +22,7 @@ def score(y_true, y_pred, **kwargs):
     try:
         average = kwargs["3"]
     except:
-        if set(y_true) != {0,1} or (set(y_pred) != {0,1} and set(y_pred) != {0} and set(y_pred) != {1}):
+        if multiclass:
             average = "micro"
         else:
             average = "binary"

Code/MonoMultiViewClassifiers/Metrics/roc_auc_score.py

@@ -7,7 +7,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     try:
         sample_weight = kwargs["0"]
     except:
@@ -15,11 +15,11 @@ def score(y_true, y_pred, **kwargs):
     try:
         average = kwargs["1"]
     except:
-        if set(y_true) != {0,1} or (set(y_pred) != {0,1} and set(y_pred) != {0} and set(y_pred) != {1}):
+        if multiclass:
             average = "micro"
         else:
             average = None
-    if set(y_true) != {0,1} or (set(y_pred) != {0,1} and set(y_pred) != {0} and set(y_pred) != {1}):
+    if multiclass:
         mlb = MultiLabelBinarizer()
         y_true = mlb.fit_transform([(label) for label in y_true])
         y_pred = mlb.fit_transform([(label) for label in y_pred])

Code/MonoMultiViewClassifiers/Metrics/zero_one_loss.py

@@ -6,7 +6,7 @@ __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
 
-def score(y_true, y_pred, **kwargs):
+def score(y_true, y_pred, multiclass=False, **kwargs):
     try:
         sample_weight = kwargs["0"]
     except:

Code/MonoMultiViewClassifiers/ResultAnalysis.py

@@ -218,9 +218,10 @@ def genMetricsScoresMulticlass(results, trueLabels, metrics, argumentsDictionari
             for classifierName, resultDictionary in iterResults.items():
                 if not "metricsScores" in resultDictionary:
                     results[iterIndex][classifierName]["metricsScores"]={}
-                trainScore = metricModule.score(trueLabels[trainIndices],resultDictionary["labels"][trainIndices])
+                trainScore = metricModule.score(trueLabels[trainIndices],resultDictionary["labels"][trainIndices], multiclass=True)
                 testScore = metricModule.score(trueLabels[multiclassTestIndices],
-                                               resultDictionary["labels"][multiclassTestIndices])
+                                               resultDictionary["labels"][multiclassTestIndices],
+                                               multiclass=True)
                 results[iterIndex][classifierName]["metricsScores"][metric[0]] = [trainScore, testScore]
 
 
@@ -720,8 +721,6 @@ def resultAnalysis(benchmark, results, name, times, metrics, directory, minSize=
         logging.debug("Done:\t Score graph generation for " + metric[0])
 
 
-
-
 def analyzeLabels(labelsArrays, realLabels, results, directory, minSize = 10):
     """Used to generate a graph showing errors on each example depending on classifier"""
     logging.debug("Start:\t Label analysis figure generation")
@@ -807,7 +806,6 @@ def analyzeIterLabels(labelsAnalysisList, directory, classifiersNames, minSize=1
     logging.debug("Done:\t Global error by example figure generation")
 
 
-
 def genFig(iterResults, metric, nbResults, names, nbMono, minSize=10):
     """Used to generate the bar graph representing the mean scores of each classifiers if multiple iteration
      with different random states"""