Fixed macro averaging in recall

Author: hzavadil98

CollaborativeCoding/metrics/recall.py

@@ -79,15 +79,15 @@ def __compute_macro_averaging(self, y_true, y_pred):
         recall = 0
         for i in range(self.num_classes):
             tp = (y_true[:, i] * y_pred[:, i]).sum()
-            fn = torch.sum(~y_pred[y_true[:, i].bool()].bool())
+            fn = (y_true[:, i] * (1 - y_pred[:, i])).sum()
             recall += tp / (tp + fn)
         recall /= self.num_classes
 
         return recall
 
     def __compute_micro_averaging(self, y_true, y_pred):
         true_positives = (y_true * y_pred).sum()
-        false_negatives = torch.sum(~y_pred[y_true.bool()].bool())
+        false_negatives = (y_true * (1 - y_pred)).sum()
 
         recall = true_positives / (true_positives + false_negatives)
         return recall