FP and FN were switched

model_selection/cv_metric_gen.py

@@ -113,26 +113,25 @@ def TN_scorer(clf, X, y):
     """Gives the number of samples predicted as true negatives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    TN = cm[0,0]
-    return TN
+    return cm[0, 0]
+
 def FN_scorer(clf, X, y):
     """Gives the number of samples predicted as false negatives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    FN = cm[0,1]
-    return FN
+    return cm[1, 0]
+
 def FP_scorer(clf, X, y):
-    """Gives the number of samples predicted as false positive"""
+    """Gives the number of samples predicted as false positives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    FP = cm[1,0]
-    return FP
+    return cm[0, 1]
+
 def TP_scorer(clf, X, y):
-    """Gives the number of samples predicted as true positive"""
+    """Gives the number of samples predicted as true positives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    TP = cm[1,1]
-    return TP
+    return cm[1, 1]
 
 def negative_recall_scorer(clf, X, y):
     """Gives the negative recall defined as the (number of true_negative_samples)/(total number of negative samples)"""

model_selection/test_models.py

@@ -124,26 +124,25 @@ def TN_scorer(clf, X, y):
     """Gives the number of samples predicted as true negatives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    TN = cm[0,0]
-    return TN
+    return cm[0, 0]
+
 def FN_scorer(clf, X, y):
     """Gives the number of samples predicted as false negatives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    FN = cm[0,1]
-    return FN
+    return cm[1, 0]
+
 def FP_scorer(clf, X, y):
-    """Gives the number of samples predicted as false positive"""
+    """Gives the number of samples predicted as false positives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    FP = cm[1,0]
-    return FP
+    return cm[0, 1]
+
 def TP_scorer(clf, X, y):
-    """Gives the number of samples predicted as true positive"""
+    """Gives the number of samples predicted as true positives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    TP = cm[1,1]
-    return TP
+    return cm[1, 1]
 
 def negative_recall_scorer(clf, X, y):
     """Gives the negative recall defined as the (number of true_negative_samples)/(total number of negative samples)"""