assert

src/torchmetrics/functional/classification/accuracy.py

@@ -625,7 +625,7 @@ def _subset_accuracy_compute(correct: Tensor, total: Tensor) -> Tensor:
 def accuracy(
     preds: Tensor,
     target: Tensor,
-    average: Optional[str] = "micro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
     mdmc_average: Optional[str] = "global",
     threshold: float = 0.5,
     top_k: Optional[int] = None,

src/torchmetrics/functional/classification/auroc.py

@@ -53,7 +53,6 @@ def _reduce_auroc(
     weights: Optional[Tensor] = None,
 ) -> Tensor:
     """Utility function for reducing multiple average precision score into one number."""
-    res = []
     if isinstance(fpr, Tensor):
         res = _auc_compute_without_check(fpr, tpr, 1.0, axis=1)
     else:
@@ -96,7 +95,7 @@ def _binary_auroc_compute(
     thresholds: Optional[Tensor],
     max_fpr: Optional[float] = None,
     pos_label: int = 1,
-) -> Tuple[Tensor, Tensor, Tensor]:
+) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor]]:
     fpr, tpr, _ = _binary_roc_compute(state, thresholds, pos_label)
     if max_fpr is None or max_fpr == 1:
         return _auc_compute_without_check(fpr, tpr, 1.0)
@@ -221,7 +220,7 @@ def multiclass_auroc(
     thresholds: Optional[Union[int, List[float], Tensor]] = None,
     ignore_index: Optional[int] = None,
     validate_args: bool = True,
-) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
+) -> Tensor:
     r"""
     Compute Area Under the Receiver Operating Characteristic Curve (`ROC AUC`_) for multiclass tasks. The AUROC score
     summarizes the ROC curve into an single number that describes the performance of a model for multiple
@@ -317,7 +316,7 @@ def _multilabel_auroc_compute(
     average: Optional[Literal["micro", "macro", "weighted", "none"]],
     thresholds: Optional[Tensor],
     ignore_index: Optional[int] = None,
-) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
+) -> Union[Tuple[Tensor, Tensor, Tensor], Tensor]:
     if average == "micro":
         if isinstance(state, Tensor) and thresholds is not None:
             return _binary_auroc_compute(state.sum(1), thresholds, max_fpr=None)
@@ -328,7 +327,7 @@ def _multilabel_auroc_compute(
                 idx = target == ignore_index
                 preds = preds[~idx]
                 target = target[~idx]
-            return _binary_auroc_compute([preds, target], thresholds, max_fpr=None)
+            return _binary_auroc_compute((preds, target), thresholds, max_fpr=None)
 
     else:
         fpr, tpr, _ = _multilabel_roc_compute(state, num_labels, thresholds, ignore_index)
@@ -605,15 +604,15 @@ def auroc(
     target: Tensor,
     num_classes: Optional[int] = None,
     pos_label: Optional[int] = None,
-    average: Optional[str] = "macro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
     max_fpr: Optional[float] = None,
     sample_weights: Optional[Sequence] = None,
     task: Optional[Literal["binary", "multiclass", "multilabel"]] = None,
     thresholds: Optional[Union[int, List[float], Tensor]] = None,
     num_labels: Optional[int] = None,
     ignore_index: Optional[int] = None,
     validate_args: bool = True,
-) -> Tensor:
+) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
     r"""
     .. note::
         From v0.10 an `'binary_*'`, `'multiclass_*', `'multilabel_*'` version now exist of each classification
@@ -686,13 +685,14 @@ def auroc(
         tensor(0.7778)
     """
     if task is not None:
-        kwargs = dict(thresholds=thresholds, ignore_index=ignore_index, validate_args=validate_args)
         if task == "binary":
-            return binary_auroc(preds, target, max_fpr, **kwargs)
+            return binary_auroc(preds, target, max_fpr, thresholds, ignore_index, validate_args)
         if task == "multiclass":
-            return multiclass_auroc(preds, target, num_classes, average, **kwargs)
+            assert isinstance(num_classes, int)
+            return multiclass_auroc(preds, target, num_classes, average, thresholds, ignore_index, validate_args)
         if task == "multilabel":
-            return multilabel_auroc(preds, target, num_labels, average, **kwargs)
+            assert isinstance(num_labels, int)
+            return multilabel_auroc(preds, target, num_labels, average, thresholds, ignore_index, validate_args)
         raise ValueError(
             f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
         )

src/torchmetrics/functional/classification/average_precision.py

@@ -562,7 +562,7 @@ def average_precision(
     target: Tensor,
     num_classes: Optional[int] = None,
     pos_label: Optional[int] = None,
-    average: Optional[str] = "macro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
     task: Optional[Literal["binary", "multiclass", "multilabel"]] = None,
     thresholds: Optional[Union[int, List[float], Tensor]] = None,
     num_labels: Optional[int] = None,

src/torchmetrics/functional/classification/cohen_kappa.py

@@ -287,7 +287,7 @@ def cohen_kappa(
     preds: Tensor,
     target: Tensor,
     num_classes: int,
-    weights: Optional[str] = None,
+    weights: Optional[Literal["linear", "quadratic", "none"]] = None,
     threshold: float = 0.5,
     task: Optional[Literal["binary", "multiclass", "multilabel"]] = None,
     ignore_index: Optional[int] = None,
@@ -335,11 +335,10 @@ class labels.
         tensor(0.5000)
     """
     if task is not None:
-        kwargs = dict(weights=weights, ignore_index=ignore_index, validate_args=validate_args)
         if task == "binary":
-            return binary_cohen_kappa(preds, target, threshold, **kwargs)
+            return binary_cohen_kappa(preds, target, threshold, weights, ignore_index, validate_args)
         if task == "multiclass":
-            return multiclass_cohen_kappa(preds, target, num_classes, **kwargs)
+            return multiclass_cohen_kappa(preds, target, num_classes, weights, ignore_index, validate_args)
         raise ValueError(
             f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
         )

src/torchmetrics/functional/classification/f_beta.py

@@ -794,7 +794,7 @@ def fbeta_score(
     preds: Tensor,
     target: Tensor,
     beta: float = 1.0,
-    average: Optional[str] = "micro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
     mdmc_average: Optional[str] = None,
     ignore_index: Optional[int] = None,
     num_classes: Optional[int] = None,
@@ -962,7 +962,7 @@ def f1_score(
     preds: Tensor,
     target: Tensor,
     beta: float = 1.0,
-    average: Optional[str] = "micro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
     mdmc_average: Optional[str] = None,
     ignore_index: Optional[int] = None,
     num_classes: Optional[int] = None,
@@ -1076,13 +1076,20 @@ def f1_score(
         tensor(0.3333)
     """
     if task is not None:
-        kwargs = dict(multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args)
+        assert multidim_average is not None
         if task == "binary":
-            return binary_f1_score(preds, target, threshold, **kwargs)
+            return binary_f1_score(preds, target, threshold, multidim_average, ignore_index, validate_args)
         if task == "multiclass":
-            return multiclass_f1_score(preds, target, num_classes, average, top_k, **kwargs)
+            assert isinstance(num_classes, int)
+            assert isinstance(top_k, int)
+            return multiclass_f1_score(
+                preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
+            )
         if task == "multilabel":
-            return multilabel_f1_score(preds, target, num_labels, threshold, average, **kwargs)
+            assert isinstance(num_labels, int)
+            return multilabel_f1_score(
+                preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
+            )
         raise ValueError(
             f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
         )

src/torchmetrics/functional/classification/hamming.py

@@ -437,7 +437,7 @@ def hamming_distance(
     task: Optional[Literal["binary", "multiclass", "multilabel"]] = None,
     num_classes: Optional[int] = None,
     num_labels: Optional[int] = None,
-    average: Optional[str] = "macro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
     top_k: int = 1,
     multidim_average: Optional[Literal["global", "samplewise"]] = "global",
     ignore_index: Optional[int] = None,
@@ -482,13 +482,20 @@ def hamming_distance(
         tensor(0.2500)
     """
     if task is not None:
-        kwargs = dict(multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args)
+        assert multidim_average is not None
         if task == "binary":
-            return binary_hamming_distance(preds, target, threshold, **kwargs)
+            return binary_hamming_distance(preds, target, threshold, multidim_average, ignore_index, validate_args)
         if task == "multiclass":
-            return multiclass_hamming_distance(preds, target, num_classes, average, top_k, **kwargs)
+            assert isinstance(num_classes, int)
+            assert isinstance(top_k, int)
+            return multiclass_hamming_distance(
+                preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
+            )
         if task == "multilabel":
-            return multilabel_hamming_distance(preds, target, num_labels, threshold, average, **kwargs)
+            assert isinstance(num_labels, int)
+            return multilabel_hamming_distance(
+                preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
+            )
         raise ValueError(
             f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
         )

src/torchmetrics/functional/classification/jaccard.py

@@ -378,7 +378,7 @@ def jaccard_index(
     preds: Tensor,
     target: Tensor,
     num_classes: int,
-    average: Optional[str] = "macro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
     ignore_index: Optional[int] = None,
     absent_score: float = 0.0,
     threshold: float = 0.5,

src/torchmetrics/functional/classification/precision_recall.py

@@ -712,7 +712,7 @@ def _precision_compute(
 def precision(
     preds: Tensor,
     target: Tensor,
-    average: Optional[str] = "micro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
     mdmc_average: Optional[str] = None,
     ignore_index: Optional[int] = None,
     num_classes: Optional[int] = None,
@@ -833,13 +833,20 @@ def precision(
 
     """
     if task is not None:
-        kwargs = dict(multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args)
+        assert multidim_average is not None
         if task == "binary":
-            return binary_precision(preds, target, threshold, **kwargs)
+            return binary_precision(preds, target, threshold, multidim_average, ignore_index, validate_args)
         if task == "multiclass":
-            return multiclass_precision(preds, target, num_classes, average, top_k, **kwargs)
+            assert isinstance(num_classes, int)
+            assert isinstance(top_k, int)
+            return multiclass_precision(
+                preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
+            )
         if task == "multilabel":
-            return multilabel_precision(preds, target, num_labels, threshold, average, **kwargs)
+            assert isinstance(num_labels, int)
+            return multilabel_precision(
+                preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
+            )
         raise ValueError(
             f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
         )
@@ -936,7 +943,7 @@ def _recall_compute(
 def recall(
     preds: Tensor,
     target: Tensor,
-    average: Optional[str] = "micro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
     mdmc_average: Optional[str] = None,
     ignore_index: Optional[int] = None,
     num_classes: Optional[int] = None,
@@ -1058,13 +1065,20 @@ def recall(
 
     """
     if task is not None:
-        kwargs = dict(multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args)
+        assert multidim_average is not None
         if task == "binary":
-            return binary_recall(preds, target, threshold, **kwargs)
+            return binary_recall(preds, target, threshold, multidim_average, ignore_index, validate_args)
         if task == "multiclass":
-            return multiclass_recall(preds, target, num_classes, average, top_k, **kwargs)
+            assert isinstance(num_classes, int)
+            assert isinstance(top_k, int)
+            return multiclass_recall(
+                preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
+            )
         if task == "multilabel":
-            return multilabel_recall(preds, target, num_labels, threshold, average, **kwargs)
+            assert isinstance(num_labels, int)
+            return multilabel_recall(
+                preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
+            )
         raise ValueError(
             f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
         )
@@ -1110,7 +1124,7 @@ def recall(
 def precision_recall(
     preds: Tensor,
     target: Tensor,
-    average: Optional[str] = "micro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
     mdmc_average: Optional[str] = None,
     ignore_index: Optional[int] = None,
     num_classes: Optional[int] = None,

src/torchmetrics/functional/classification/precision_recall_curve.py

@@ -1053,13 +1053,15 @@ def precision_recall_curve(
         [tensor([0.7500]), tensor([0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500, 0.7500]), tensor([0.0500])]
     """
     if task is not None:
-        kwargs = dict(thresholds=thresholds, ignore_index=ignore_index, validate_args=validate_args)
         if task == "binary":
-            return binary_precision_recall_curve(preds, target, **kwargs)
+            return binary_precision_recall_curve(preds, target, thresholds, ignore_index, validate_args)
         if task == "multiclass":
-            return multiclass_precision_recall_curve(preds, target, num_classes, **kwargs)
+            assert isinstance(num_classes, int)
+            return multiclass_precision_recall_curve(
+                preds, target, num_classes, thresholds, ignore_index, validate_args
+            )
         if task == "multilabel":
-            return multilabel_precision_recall_curve(preds, target, num_labels, **kwargs)
+            return multilabel_precision_recall_curve(preds, target, num_labels, thresholds, ignore_index, validate_args)
         raise ValueError(
             f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
         )

src/torchmetrics/functional/classification/roc.py

@@ -696,13 +696,14 @@ def roc(
          tensor([1.1837, 0.1837, 0.1338, 0.1183, 0.1138])]
     """
     if task is not None:
-        kwargs = dict(thresholds=thresholds, ignore_index=ignore_index, validate_args=validate_args)
         if task == "binary":
-            return binary_roc(preds, target, **kwargs)
+            return binary_roc(preds, target, thresholds, ignore_index, validate_args)
         if task == "multiclass":
-            return multiclass_roc(preds, target, num_classes, **kwargs)
+            assert isinstance(num_classes, int)
+            return multiclass_roc(preds, target, num_classes, thresholds, ignore_index, validate_args)
         if task == "multilabel":
-            return multilabel_roc(preds, target, num_labels, **kwargs)
+            assert isinstance(num_labels, int)
+            return multilabel_roc(preds, target, num_labels, thresholds, ignore_index, validate_args)
         raise ValueError(
             f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
         )

src/torchmetrics/functional/classification/specificity.py

@@ -409,7 +409,7 @@ def _specificity_compute(
 def specificity(
     preds: Tensor,
     target: Tensor,
-    average: Optional[str] = "micro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
     mdmc_average: Optional[str] = None,
     ignore_index: Optional[int] = None,
     num_classes: Optional[int] = None,
@@ -529,13 +529,20 @@ def specificity(
         tensor(0.6250)
     """
     if task is not None:
-        kwargs = dict(multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args)
+        assert multidim_average is not None
         if task == "binary":
-            return binary_specificity(preds, target, threshold, **kwargs)
+            return binary_specificity(preds, target, threshold, multidim_average, ignore_index, validate_args)
         if task == "multiclass":
-            return multiclass_specificity(preds, target, num_classes, average, top_k, **kwargs)
+            assert isinstance(num_classes, int)
+            assert isinstance(top_k, int)
+            return multiclass_specificity(
+                preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
+            )
         if task == "multilabel":
-            return multilabel_specificity(preds, target, num_labels, threshold, average, **kwargs)
+            assert isinstance(num_labels, int)
+            return multilabel_specificity(
+                preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
+            )
         raise ValueError(
             f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
         )

src/torchmetrics/functional/classification/stat_scores.py

@@ -1095,7 +1095,7 @@ def stat_scores(
     ignore_index: Optional[int] = None,
     task: Optional[Literal["binary", "multiclass", "multilabel"]] = None,
     num_labels: Optional[int] = None,
-    average: Optional[str] = "micro",
+    average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
     multidim_average: Optional[Literal["global", "samplewise"]] = "global",
     validate_args: bool = True,
 ) -> Tensor:
@@ -1225,13 +1225,20 @@ def stat_scores(
 
     """
     if task is not None:
-        kwargs = dict(multidim_average=multidim_average, ignore_index=ignore_index, validate_args=validate_args)
+        assert multidim_average is not None
         if task == "binary":
-            return binary_stat_scores(preds, target, threshold, **kwargs)
+            return binary_stat_scores(preds, target, threshold, multidim_average, ignore_index, validate_args)
         if task == "multiclass":
-            return multiclass_stat_scores(preds, target, num_classes, average, top_k, **kwargs)
+            assert isinstance(num_classes, int)
+            assert isinstance(top_k, int)
+            return multiclass_stat_scores(
+                preds, target, num_classes, average, top_k, multidim_average, ignore_index, validate_args
+            )
         if task == "multilabel":
-            return multilabel_stat_scores(preds, target, num_labels, threshold, average, **kwargs)
+            assert isinstance(num_labels, int)
+            return multilabel_stat_scores(
+                preds, target, num_labels, threshold, average, multidim_average, ignore_index, validate_args
+            )
         raise ValueError(
             f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
         )