fix typo in specificity_at_sensitivity

src/torchmetrics/classification/__init__.py

@@ -117,18 +117,6 @@
 )
 
 __all__ = [
-    "BinaryConfusionMatrix",
-    "ConfusionMatrix",
-    "MulticlassConfusionMatrix",
-    "MultilabelConfusionMatrix",
-    "PrecisionRecallCurve",
-    "BinaryPrecisionRecallCurve",
-    "MulticlassPrecisionRecallCurve",
-    "MultilabelPrecisionRecallCurve",
-    "BinaryStatScores",
-    "MulticlassStatScores",
-    "MultilabelStatScores",
-    "StatScores",
     "Accuracy",
     "BinaryAccuracy",
     "MulticlassAccuracy",
@@ -147,6 +135,10 @@
     "BinaryCohenKappa",
     "CohenKappa",
     "MulticlassCohenKappa",
+    "BinaryConfusionMatrix",
+    "ConfusionMatrix",
+    "MulticlassConfusionMatrix",
+    "MultilabelConfusionMatrix",
     "Dice",
     "ExactMatch",
     "MulticlassExactMatch",
@@ -184,29 +176,34 @@
     "MultilabelRecall",
     "Precision",
     "Recall",
+    "BinaryPrecisionRecallCurve",
+    "MulticlassPrecisionRecallCurve",
+    "MultilabelPrecisionRecallCurve",
+    "PrecisionRecallCurve",
     "MultilabelCoverageError",
     "MultilabelRankingAveragePrecision",
     "MultilabelRankingLoss",
+    "RecallAtFixedPrecision",
     "BinaryRecallAtFixedPrecision",
     "MulticlassRecallAtFixedPrecision",
     "MultilabelRecallAtFixedPrecision",
-    "ROC",
     "BinaryROC",
     "MulticlassROC",
     "MultilabelROC",
+    "ROC",
     "BinarySpecificity",
     "MulticlassSpecificity",
     "MultilabelSpecificity",
     "Specificity",
     "BinarySpecificityAtSensitivity",
     "MulticlassSpecificityAtSensitivity",
     "MultilabelSpecificityAtSensitivity",
-    "BinaryPrecisionAtFixedRecall",
     "SpecificityAtSensitivity",
-    "MulticlassPrecisionAtFixedRecall",
-    "MultilabelPrecisionAtFixedRecall",
+    "BinaryStatScores",
+    "MulticlassStatScores",
+    "MultilabelStatScores",
+    "StatScores",
     "PrecisionAtFixedRecall",
-    "RecallAtFixedPrecision",
     "BinaryPrecisionAtFixedRecall",
     "MulticlassPrecisionAtFixedRecall",
     "MultilabelPrecisionAtFixedRecall",

src/torchmetrics/functional/classification/__init__.py

@@ -120,6 +120,7 @@
     multiclass_specificity_at_sensitivity,
     multilabel_specificity_at_sensitivity,
     specicity_at_sensitivity,
+    specificity_at_sensitivity,
 )
 from torchmetrics.functional.classification.stat_scores import (
     binary_stat_scores,
@@ -165,8 +166,6 @@
     "multilabel_fbeta_score",
     "binary_fairness",
     "binary_groups_stat_rates",
-    "demographic_parity",
-    "equal_opportunity",
     "binary_hamming_distance",
     "hamming_distance",
     "multiclass_hamming_distance",
@@ -212,11 +211,14 @@
     "multiclass_specificity_at_sensitivity",
     "multilabel_specificity_at_sensitivity",
     "specicity_at_sensitivity",
+    "specificity_at_sensitivity",
     "binary_stat_scores",
     "multiclass_stat_scores",
     "multilabel_stat_scores",
     "stat_scores",
     "binary_precision_at_fixed_recall",
     "multilabel_precision_at_fixed_recall",
     "multiclass_precision_at_fixed_recall",
+    "demographic_parity",
+    "equal_opportunity",
 ]

src/torchmetrics/functional/classification/specificity_sensitivity.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import warnings
 from typing import List, Optional, Tuple, Union
 
 import torch
@@ -403,6 +404,7 @@ def multilabel_specificity_at_sensitivity(
     return _multilabel_specificity_at_sensitivity_compute(state, num_labels, thresholds, ignore_index, min_sensitivity)
 
 
+# create specicity_at_sensitivity that calls specificity_at_sensitivity
 def specicity_at_sensitivity(
     preds: Tensor,
     target: Tensor,
@@ -414,7 +416,49 @@ def specicity_at_sensitivity(
     ignore_index: Optional[int] = None,
     validate_args: bool = True,
 ) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
-    r"""Compute the highest possible specicity value given the minimum sensitivity thresholds provided.
+    r"""Compute the highest possible specificity value given the minimum sensitivity thresholds provided.
+
+    This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and
+    the find the specificity for a given sensitivity level.
+
+    This function is a simple wrapper to get the task specific versions of this metric, which is done by setting the
+    ``task`` argument to either ``'binary'``, ``'multiclass'`` or ``multilabel``. See the documentation of
+    :func:`~torchmetrics.functional.classification.binary_specificity_at_sensitivity`,
+    :func:`~torchmetrics.functional.classification.multiclass_specificity_at_sensitivity` and
+    :func:`~torchmetrics.functional.classification.multilabel_specificity_at_sensitivity` for the specific details of
+    each argument influence and examples.
+
+    """
+    warnings.warn(
+        "This method has will be removed in 2.0.0. Use `specificity_at_sensitivity` instead.",
+        DeprecationWarning,
+        stacklevel=1,
+    )
+    return specificity_at_sensitivity(
+    preds=preds,
+    target=target,
+    task=task,
+    min_sensitivity=min_sensitivity,
+    thresholds=thresholds,
+    num_classes=num_classes,
+    num_labels=num_labels,
+    ignore_index=ignore_index,
+    validate_args=validate_args
+    )
+
+
+def specificity_at_sensitivity(
+    preds: Tensor,
+    target: Tensor,
+    task: Literal["binary", "multiclass", "multilabel"],
+    min_sensitivity: float,
+    thresholds: Optional[Union[int, List[float], Tensor]] = None,
+    num_classes: Optional[int] = None,
+    num_labels: Optional[int] = None,
+    ignore_index: Optional[int] = None,
+    validate_args: bool = True,
+) -> Union[Tensor, Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
+    r"""Compute the highest possible specificity value given the minimum sensitivity thresholds provided.
 
     This is done by first calculating the Receiver Operating Characteristic (ROC) curve for different thresholds and
     the find the specificity for a given sensitivity level.