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performance.py

@@ -8,15 +8,15 @@ def evaluate(classifier, features, Y):
 
     print("Evaluating performance...")
 
-    predictions = classifier.predict_proba(features)[:, 1]
-    roc = roc_auc_score(Y, predictions)
-    print("ROC of predictions: %s"%roc)
+    #predictions = classifier.predict_proba(features)[:, 1]
+    #roc = roc_auc_score(Y, predictions)
+    #print("ROC of predictions: %s"%roc)
 
-    #predictions = classifier.predict(features)
-    #cm = confusion_matrix(Y, predictions)
+    predictions = classifier.predict(features)
+    cm = confusion_matrix(Y, predictions)
 
-    #Se = cm[1, 1] / float(cm[1, 0] + cm[1, 0])
-    #P = cm[1, 1] / float(cm[1, 0] + cm[0, 1])
-    #score = min(Se, P)
+    Se = cm[1, 1] / float(cm[1, 1] + cm[1, 0])
+    P = cm[1, 1] / float(cm[1, 1] + cm[0, 1])
+    score = min(Se, P)
 
-    #print("Score of predictions: %s"%score)
+    print("Score of predictions: %s"%score)