Debugging gridSearch for SCM

Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py

@@ -25,9 +25,13 @@ def fit(DATASET, CLASS_LABELS, NB_CORES=1,**kwargs):
         attributeClassification = kwargs["attributeClassification"]
         binaryAttributes = kwargs["binaryAttributes"]
     except:
-        attributeClassification, binaryAttributes = transformData(DATASET)
+        attributeClassification, binaryAttributes, dsetFile = transformData(DATASET)
     classifier = pyscm.scm.SetCoveringMachine(p=p, max_attributes=max_attrtibutes, model_type=model_type, verbose=False)
     classifier.fit(binaryAttributes, CLASS_LABELS, X=None, attribute_classifications=attributeClassification, iteration_callback=None)
+    try:
+        dsetFile.close()
+    except:
+        pass
     return classifier
 
 
@@ -60,12 +64,18 @@ def gridSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIt
                 fold.sort()
                 trainIndices = [index for index in range(len(y_train)) if (index not in fold)]
                 attributeClassification, binaryAttributes, dsetFile = transformData(X_train[trainIndices])
+                try:
                     classifier.fit(binaryAttributes, y_train[trainIndices], X=None, attribute_classifications=attributeClassification, iteration_callback=None)
 
                     predictedLabels = classifier.predict(X_train[fold])
                     score = metricModule.score(y_train[fold], predictedLabels)
                     scores.append(score)
+                except:
+                    pass
                 dsetFile.close()
+        if scores==[]:
+            score = baseScore
+        else:
             score = np.mean(np.array(scores))
 
         if isBetter=="higher" and score>baseScore: