again torchmetrics

tests/test_metrics/test_torchmetrics.py

@@ -30,18 +30,18 @@ y_pred_bin_pt = torch.from_numpy(y_pred_bin_np)
 @pytest.mark.parametrize(
     "metric_name, sklearn_metric, torch_metric",
     [
-        ("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(task="binary", beta=2.0)),
+        ("BinaryAccuracy", accuracy_score, Accuracy(task="binary")),
+        ("BinaryPrecision", precision_score, Precision(task="binary")),
+        ("BinaryRecall", recall_score, Recall(task="binary")),
+        ("BinaryF1Score", f1_score, F1Score(task="binary")),
+        ("BinaryFBetaScore", 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[f"Binary{metric_name}"]
+    wd_res = wd_logs[metric_name]
     if wd_res.size != 1:
         wd_res = wd_res[1]
     assert np.isclose(sk_res, wd_res)
@@ -77,46 +77,6 @@ def f2_score_multi(y_true, y_pred, average):
 @pytest.mark.parametrize(
     "metric_name, sklearn_metric, torch_metric",
     [
-        (
-            "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"),
-        ),
-        (
-            "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"),
-        ),
         (
             "MulticlassAccuracy",
             accuracy_score,
@@ -140,7 +100,7 @@ def f2_score_multi(y_true, y_pred, average):
         (
             "MulticlassFBetaScore",
             f2_score_multi,
-            FBetaScore(task="multiclass", beta=3.0, num_classes=3, average="macro"),
+            FBetaScore(beta=3.0, task="multiclass", num_classes=3, average="macro"),
         ),
     ],
 )
@@ -154,6 +114,6 @@ 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[f"Multiclass{metric_name}"]
+    wd_res = wd_logs[metric_name]
 
     assert np.isclose(sk_res, wd_res, atol=0.01)