[Refactor] Classification 6/n

docs/source/classification/auroc.rst

@@ -15,8 +15,44 @@ ________________
 .. autoclass:: torchmetrics.AUROC
  :noindex:
 
+BinaryAUROC
+^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.BinaryAUROC
+ :noindex:
+
+MulticlassAUROC
+^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.MulticlassAUROC
+ :noindex:
+
+MultilabelAUROC
+^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.MultilabelAUROC
+ :noindex:
+
 Functional Interface
 ____________________
 
 .. autofunction:: torchmetrics.functional.auroc
  :noindex:
+
+binary_auroc
+^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.binary_auroc
+ :noindex:
+
+multiclass_auroc
+^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.multiclass_auroc
+ :noindex:
+
+multilabel_auroc
+^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.multilabel_auroc
+ :noindex:

docs/source/classification/average_precision.rst

@@ -13,8 +13,44 @@ ________________
 .. autoclass:: torchmetrics.AveragePrecision
  :noindex:
 
+BinaryAveragePrecision
+^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.BinaryAveragePrecision
+ :noindex:
+
+MulticlassAveragePrecision
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.MulticlassAveragePrecision
+ :noindex:
+
+MultilabelAveragePrecision
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.MultilabelAveragePrecision
+ :noindex:
+
 Functional Interface
 ____________________
 
 .. autofunction:: torchmetrics.functional.average_precision
  :noindex:
+
+binary_average_precision
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.binary_average_precision
+ :noindex:
+
+multiclass_average_precision
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.multiclass_average_precision
+ :noindex:
+
+multilabel_average_precision
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.multilabel_average_precision
+ :noindex:

docs/source/classification/precision_recall_curve.rst

@@ -13,8 +13,44 @@ ________________
 .. autoclass:: torchmetrics.PrecisionRecallCurve
  :noindex:
 
+BinaryPrecisionRecallCurve
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.BinaryPrecisionRecallCurve
+ :noindex:
+
+MulticlassPrecisionRecallCurve
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.MulticlassPrecisionRecallCurve
+ :noindex:
+
+MultilabelPrecisionRecallCurve
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.MultilabelPrecisionRecallCurve
+ :noindex:
+
 Functional Interface
 ____________________
 
 .. autofunction:: torchmetrics.functional.precision_recall_curve
  :noindex:
+
+binary_precision_recall_curve
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.binary_precision_recall_curve
+ :noindex:
+
+multiclass_precision_recall_curve
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.multiclass_precision_recall_curve
+ :noindex:
+
+multilabel_precision_recall_curve
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.multilabel_precision_recall_curve
+ :noindex:

docs/source/classification/recall_at_fixed_precision.rst

@@ -0,0 +1,50 @@
+.. customcarditem::
+ :header: Recall At Fixed Precision
+ :image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/tabular_classification.svg
+ :tags: Classification
+
+#########################
+Recall At Fixed Precision
+#########################
+
+Module Interface
+________________
+
+BinaryRecallAtFixedPrecision
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.BinaryRecallAtFixedPrecision
+ :noindex:
+
+MulticlassRecallAtFixedPrecision
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.MulticlassRecallAtFixedPrecision
+ :noindex:
+
+MultilabelRecallAtFixedPrecision
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.MultilabelRecallAtFixedPrecision
+ :noindex:
+
+Functional Interface
+____________________
+
+binary_recall_at_fixed_precision
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.binary_recall_at_fixed_precision
+ :noindex:
+
+multiclass_recall_at_fixed_precision
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.multiclass_recall_at_fixed_precision
+ :noindex:
+
+multilabel_recall_at_fixed_precision
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.multilabel_recall_at_fixed_precision
+ :noindex:

docs/source/classification/roc.rst

@@ -13,8 +13,44 @@ ________________
 .. autoclass:: torchmetrics.ROC
  :noindex:
 
+BinaryROC
+^^^^^^^^^
+
+.. autoclass:: torchmetrics.BinaryROC
+ :noindex:
+
+MulticlassROC
+^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.MulticlassROC
+ :noindex:
+
+MultilabelROC
+^^^^^^^^^^^^^
+
+.. autoclass:: torchmetrics.MultilabelROC
+ :noindex:
+
 Functional Interface
 ____________________
 
 .. autofunction:: torchmetrics.functional.roc
  :noindex:
+
+binary_roc
+^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.binary_roc
+ :noindex:
+
+multiclass_roc
+^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.multiclass_roc
+ :noindex:
+
+multilabel_roc
+^^^^^^^^^^^^^^
+
+.. autofunction:: torchmetrics.functional.multilabel_roc
+ :noindex:

src/torchmetrics/__init__.py

@@ -27,6 +27,8 @@
  Accuracy,
  AveragePrecision,
  BinaryAccuracy,
+ BinaryAUROC,
+ BinaryAveragePrecision,
  BinaryCohenKappa,
  BinaryConfusionMatrix,
  BinaryF1Score,
@@ -35,7 +37,10 @@
  BinaryJaccardIndex,
  BinaryMatthewsCorrCoef,
  BinaryPrecision,
+ BinaryPrecisionRecallCurve,
  BinaryRecall,
+ BinaryRecallAtFixedPrecision,
+ BinaryROC,
  BinarySpecificity,
  BinaryStatScores,
  BinnedAveragePrecision,
@@ -56,6 +61,8 @@
  LabelRankingLoss,
  MatthewsCorrCoef,
  MulticlassAccuracy,
+ MulticlassAUROC,
+ MulticlassAveragePrecision,
  MulticlassCohenKappa,
  MulticlassConfusionMatrix,
  MulticlassF1Score,
@@ -64,10 +71,15 @@
  MulticlassJaccardIndex,
  MulticlassMatthewsCorrCoef,
  MulticlassPrecision,
+ MulticlassPrecisionRecallCurve,
  MulticlassRecall,
+ MulticlassRecallAtFixedPrecision,
+ MulticlassROC,
  MulticlassSpecificity,
  MulticlassStatScores,
  MultilabelAccuracy,
+ MultilabelAUROC,
+ MultilabelAveragePrecision,
  MultilabelConfusionMatrix,
  MultilabelCoverageError,
  MultilabelExactMatch,
@@ -77,9 +89,12 @@
  MultilabelJaccardIndex,
  MultilabelMatthewsCorrCoef,
  MultilabelPrecision,
+ MultilabelPrecisionRecallCurve,
  MultilabelRankingAveragePrecision,
  MultilabelRankingLoss,
  MultilabelRecall,
+ MultilabelRecallAtFixedPrecision,
+ MultilabelROC,
  MultilabelSpecificity,
  MultilabelStatScores,
  Precision,
@@ -155,6 +170,21 @@
  "MultilabelAccuracy",
  "AUC",
  "AUROC",
+ "BinaryAUROC",
+ "BinaryAveragePrecision",
+ "BinaryPrecisionRecallCurve",
+ "BinaryRecallAtFixedPrecision",
+ "BinaryROC",
+ "MultilabelROC",
+ "MulticlassAUROC",
+ "MulticlassAveragePrecision",
+ "MulticlassPrecisionRecallCurve",
+ "MulticlassRecallAtFixedPrecision",
+ "MulticlassROC",
+ "MultilabelAUROC",
+ "MultilabelAveragePrecision",
+ "MultilabelPrecisionRecallCurve",
+ "MultilabelRecallAtFixedPrecision",
  "AveragePrecision",
  "BinnedAveragePrecision",
  "BinnedPrecisionRecallCurve",

src/torchmetrics/classification/__init__.py

@@ -17,6 +17,12 @@
  MulticlassConfusionMatrix,
  MultilabelConfusionMatrix,
 )
+from torchmetrics.classification.precision_recall_curve import ( # noqa: F401 isort:skip
+ PrecisionRecallCurve,
+ BinaryPrecisionRecallCurve,
+ MulticlassPrecisionRecallCurve,
+ MultilabelPrecisionRecallCurve,
+)
 from torchmetrics.classification.stat_scores import ( # noqa: F401 isort:skip
  BinaryStatScores,
  MulticlassStatScores,
@@ -31,8 +37,13 @@
  MultilabelAccuracy,
 )
 from torchmetrics.classification.auc import AUC # noqa: F401
-from torchmetrics.classification.auroc import AUROC # noqa: F401
-from torchmetrics.classification.avg_precision import AveragePrecision # noqa: F401
+from torchmetrics.classification.auroc import AUROC, BinaryAUROC, MulticlassAUROC, MultilabelAUROC # noqa: F401
+from torchmetrics.classification.average_precision import ( # noqa: F401
+ AveragePrecision,
+ BinaryAveragePrecision,
+ MulticlassAveragePrecision,
+ MultilabelAveragePrecision,
+)
 from torchmetrics.classification.binned_precision_recall import BinnedAveragePrecision # noqa: F401
 from torchmetrics.classification.binned_precision_recall import BinnedPrecisionRecallCurve # noqa: F401
 from torchmetrics.classification.binned_precision_recall import BinnedRecallAtFixedPrecision # noqa: F401
@@ -80,7 +91,6 @@
  Precision,
  Recall,
 )
-from torchmetrics.classification.precision_recall_curve import PrecisionRecallCurve # noqa: F401
 from torchmetrics.classification.ranking import ( # noqa: F401
  CoverageError,
  LabelRankingAveragePrecision,
@@ -89,7 +99,12 @@
  MultilabelRankingAveragePrecision,
  MultilabelRankingLoss,
 )
-from torchmetrics.classification.roc import ROC # noqa: F401
+from torchmetrics.classification.recall_at_fixed_precision import ( # noqa: F401
+ BinaryRecallAtFixedPrecision,
+ MulticlassRecallAtFixedPrecision,
+ MultilabelRecallAtFixedPrecision,
+)
+from torchmetrics.classification.roc import ROC, BinaryROC, MulticlassROC, MultilabelROC # noqa: F401
 from torchmetrics.classification.specificity import ( # noqa: F401
  BinarySpecificity,
  MulticlassSpecificity,