bug in metric calculation

metrics/binary_class_classifictaion.py

@@ -5,6 +5,7 @@ from sklearn.ensemble import IsolationForest
 from sklearn.metrics import recall_score, roc_auc_score, average_precision_score
 
 from ml_lib.metrics._base_score import _BaseScores
+from ml_lib.utils.tools import to_one_hot
 
 
 class BinaryScores(_BaseScores):
@@ -17,16 +18,27 @@ class BinaryScores(_BaseScores):
 
         # Additional Score like the unweighted Average Recall:
         #########################
+        # INIT
+        if isinstance(outputs['batch_y'], torch.Tensor):
+            y_true = outputs['batch_y'].cpu().numpy()
+        else:
+            y_true = torch.cat([output['batch_y'] for output in outputs]).cpu().numpy()
+
+        if isinstance(outputs['y'], torch.Tensor):
+            y_pred = outputs['y'].cpu().numpy()
+        else:
+            y_pred = torch.cat([output['y'] for output in outputs]).squeeze().cpu().float().numpy()
+
         # UnweightedAverageRecall
-        y_true = torch.cat([output['batch_y'] for output in outputs]) .cpu().numpy()
-        y_pred = torch.cat([output['element_wise_recon_error'] for output in outputs]).squeeze().cpu().numpy()
+        # y_true = torch.cat([output['batch_y'] for output in outputs]).cpu().numpy()
+        # y_pred = torch.cat([output['element_wise_recon_error'] for output in outputs]).squeeze().cpu().numpy()
 
         # How to apply a threshold manualy
         # y_pred = (y_pred >= 0.5).astype(np.float32)
 
         # How to apply a threshold by IF (Isolation Forest)
-        clf = IsolationForest(random_state=self.model.seed)
-        y_score = clf.fit_predict(y_pred.reshape(-1,1))
+        clf = IsolationForest()
+        y_score = clf.fit_predict(y_pred.reshape(-1, 1))
         y_score = (np.asarray(y_score) == -1).astype(np.float32)
 
         uar_score = recall_score(y_true, y_score, labels=[0, 1], average='macro',