fixed torchmetrics test

tests/test_metrics/test_torchmetrics.py

@@ -30,10 +30,10 @@ y_pred_bin_pt = torch.from_numpy(y_pred_bin_np)
 @pytest.mark.parametrize(
     "metric_name, sklearn_metric, torch_metric",
     [
-        ("Accuracy", accuracy_score, Accuracy()),
-        ("Precision", precision_score, Precision()),
-        ("Recall", recall_score, Recall()),
-        ("F1Score", f1_score, F1Score()),
+        ("Accuracy", accuracy_score, Accuracy(task="binary")),
+        ("Precision", precision_score, Precision(task="binary")),
+        ("Recall", recall_score, Recall(task="binary")),
+        ("F1Score", f1_score, F1Score(task="binary")),
         ("FBetaScore", f2_score_bin, FBetaScore(beta=2)),
     ],
 )
@@ -77,10 +77,10 @@ def f2_score_multi(y_true, y_pred, average):
 @pytest.mark.parametrize(
     "metric_name, sklearn_metric, torch_metric",
     [
-        ("Accuracy", accuracy_score, Accuracy(num_classes=3, average="micro")),
-        ("Precision", precision_score, Precision(num_classes=3, average="macro")),
-        ("Recall", recall_score, Recall(num_classes=3, average="macro")),
-        ("F1Score", f1_score, F1Score(num_classes=3, average="macro")),
+        ("Accuracy", accuracy_score, Accuracy(task="multiclass", num_classes=3, average="micro")),
+        ("Precision", precision_score, Precision(task="multiclass", num_classes=3, average="macro")),
+        ("Recall", recall_score, Recall(task="multiclass", num_classes=3, average="macro")),
+        ("F1Score", f1_score, F1Score(task="multiclass", num_classes=3, average="macro")),
         (
             "FBetaScore",
             f2_score_multi,