fixed torchmetrics

tests/test_metrics/test_torchmetrics.py

@@ -34,14 +34,14 @@ y_pred_bin_pt = torch.from_numpy(y_pred_bin_np)
         ("Precision", precision_score, Precision(task="binary")),
         ("Recall", recall_score, Recall(task="binary")),
         ("F1Score", f1_score, F1Score(task="binary")),
-        ("FBetaScore", f2_score_bin, FBetaScore(task="binary", beta=2)),
+        ("FBetaScore", f2_score_bin, FBetaScore(task="binary", beta=2.0)),
     ],
 )
 def test_binary_metrics(metric_name, sklearn_metric, torch_metric):
     sk_res = sklearn_metric(y_true_bin_np, y_pred_bin_np.round())
     wd_metric = MultipleMetrics(metrics=[torch_metric])
     wd_logs = wd_metric(y_pred_bin_pt, y_true_bin_pt)
-    wd_res = wd_logs[metric_name]
+    wd_res = wd_logs[f"Binary{metric_name}"]
     if wd_res.size != 1:
         wd_res = wd_res[1]
     assert np.isclose(sk_res, wd_res)
@@ -120,7 +120,7 @@ def f2_score_multi(y_true, y_pred, average):
         (
             "MulticlassFBetaScore",
             f2_score_multi,
-            FBetaScore(task="multiclass", beta=3, num_classes=3, average="macro"),
+            FBetaScore(task="multiclass", beta=3.0, num_classes=3, average="macro"),
         ),
     ],
 )
@@ -134,6 +134,6 @@ def test_muticlass_metrics(metric_name, sklearn_metric, torch_metric):
 
     wd_metric = MultipleMetrics(metrics=[torch_metric])
     wd_logs = wd_metric(y_pred_multi_pt, y_true_multi_pt)
-    wd_res = wd_logs[metric_name]
+    wd_res = wd_logs[f"Multiclass{metric_name}"]
 
     assert np.isclose(sk_res, wd_res, atol=0.01)