Code cleaning after classification refactor 1/n

CHANGELOG.md

@@ -29,8 +29,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Removed
 
--
-
+- Removed deprecated `BinnedAveragePrecision`, `BinnedPrecisionRecallCurve`, `RecallAtFixedPrecision` ([#1251](https://github.com/Lightning-AI/metrics/pull/1251))
+- Removed deprecated `LabelRankingAveragePrecision`, `LabelRankingLoss` and `CoverageError` ([#1251](https://github.com/Lightning-AI/metrics/pull/1251))
+- Removed deprecated `KLDivergence` and `AUC` ([#1251](https://github.com/Lightning-AI/metrics/pull/1251))
 
 ### Fixed
 
@@ -74,6 +75,19 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Improved performance of retrieval metrics ([#1242](https://github.com/Lightning-AI/metrics/pull/1242))
 - Changed `SSIM` and `MSSSIM` update to be online to reduce memory usage ([#1231](https://github.com/Lightning-AI/metrics/pull/1231))
 
+### Deprecated
+
+- Deprecated `BinnedAveragePrecision`, `BinnedPrecisionRecallCurve`, `BinnedRecallAtFixedPrecision` ([#1163](https://github.com/Lightning-AI/metrics/pull/1163))
+  * `BinnedAveragePrecision` -> use `AveragePrecision` with `thresholds` arg
+  * `BinnedPrecisionRecallCurve` -> use `AveragePrecisionRecallCurve` with `thresholds` arg
+  * `BinnedRecallAtFixedPrecision` -> use `RecallAtFixedPrecision` with `thresholds` arg
+- Renamed and refactored `LabelRankingAveragePrecision`, `LabelRankingLoss` and `CoverageError` ([#1167](https://github.com/Lightning-AI/metrics/pull/1167))
+  * `LabelRankingAveragePrecision` -> `MultilabelRankingAveragePrecision`
+  * `LabelRankingLoss` -> `MultilabelRankingLoss`
+  * `CoverageError` -> `MultilabelCoverageError`
+- Deprecated `KLDivergence` and `AUC` from classification package ([#1189](https://github.com/Lightning-AI/metrics/pull/1189))
+  * `KLDivergence` moved to `regression` package
+  * Instead of `AUC` use `torchmetrics.utils.compute.auc`
 
 ### Fixed
 

docs/source/classification/coverage_error.rst

@@ -10,17 +10,11 @@ Coverage Error
 Module Interface
 ________________
 
-.. autoclass:: torchmetrics.CoverageError
-    :noindex:
-
 .. autoclass:: torchmetrics.classification.MultilabelCoverageError
     :noindex:
 
 Functional Interface
 ____________________
 
-.. autofunction:: torchmetrics.functional.coverage_error
-    :noindex:
-
 .. autofunction:: torchmetrics.functional.classification.multilabel_coverage_error
     :noindex:

docs/source/classification/label_ranking_average_precision.rst

@@ -10,18 +10,12 @@ Label Ranking Average Precision
 Module Interface
 ________________
 
-.. autoclass:: torchmetrics.LabelRankingAveragePrecision
-    :noindex:
-
 .. autoclass:: torchmetrics.classification.MultilabelRankingAveragePrecision
     :noindex:
 
 
 Functional Interface
 ____________________
 
-.. autofunction:: torchmetrics.functional.label_ranking_average_precision
-    :noindex:
-
 .. autofunction:: torchmetrics.functional.classification.multilabel_ranking_average_precision
     :noindex:

docs/source/classification/label_ranking_loss.rst

@@ -10,18 +10,11 @@ Label Ranking Loss
 Module Interface
 ________________
 
-.. autoclass:: torchmetrics.LabelRankingLoss
-    :noindex:
-
-
 .. autoclass:: torchmetrics.classification.MultilabelRankingLoss
     :noindex:
 
 Functional Interface
 ____________________
 
-.. autofunction:: torchmetrics.functional.label_ranking_loss
-    :noindex:
-
 .. autofunction:: torchmetrics.functional.classification.multilabel_ranking_loss
     :noindex:

docs/source/index.rst

@@ -132,7 +132,6 @@ Or directly from conda
    pages/overview
    pages/implement
    pages/lightning
-   pages/classification
    pages/retrieval
 
 .. toctree::

src/torchmetrics/__init__.py

@@ -21,26 +21,19 @@
     SignalNoiseRatio,
 )
 from torchmetrics.classification import (  # noqa: E402
-    AUC,
     AUROC,
     ROC,
     Accuracy,
     AveragePrecision,
-    BinnedAveragePrecision,
-    BinnedPrecisionRecallCurve,
-    BinnedRecallAtFixedPrecision,
     CalibrationError,
     CohenKappa,
     ConfusionMatrix,
-    CoverageError,
     Dice,
     F1Score,
     FBetaScore,
     HammingDistance,
     HingeLoss,
     JaccardIndex,
-    LabelRankingAveragePrecision,
-    LabelRankingLoss,
     MatthewsCorrCoef,
     Precision,
     PrecisionRecallCurve,
@@ -113,12 +106,8 @@
 __all__ = [
     "functional",
     "Accuracy",
-    "AUC",
     "AUROC",
     "AveragePrecision",
-    "BinnedAveragePrecision",
-    "BinnedPrecisionRecallCurve",
-    "BinnedRecallAtFixedPrecision",
     "BLEUScore",
     "BootStrapper",
     "CalibrationError",
@@ -130,7 +119,6 @@
     "CohenKappa",
     "ConfusionMatrix",
     "CosineSimilarity",
-    "CoverageError",
     "Dice",
     "TweedieDevianceScore",
     "ErrorRelativeGlobalDimensionlessSynthesis",
@@ -142,8 +130,6 @@
     "HingeLoss",
     "JaccardIndex",
     "KLDivergence",
-    "LabelRankingAveragePrecision",
-    "LabelRankingLoss",
     "MatchErrorRate",
     "MatthewsCorrCoef",
     "MaxMetric",

src/torchmetrics/classification/__init__.py

@@ -29,21 +29,14 @@
     MultilabelStatScores,
     StatScores,
 )
-
 from torchmetrics.classification.accuracy import Accuracy, BinaryAccuracy, MulticlassAccuracy, MultilabelAccuracy
-from torchmetrics.classification.auc import AUC
 from torchmetrics.classification.auroc import AUROC, BinaryAUROC, MulticlassAUROC, MultilabelAUROC
 from torchmetrics.classification.average_precision import (
     AveragePrecision,
     BinaryAveragePrecision,
     MulticlassAveragePrecision,
     MultilabelAveragePrecision,
 )
-from torchmetrics.classification.binned_precision_recall import (
-    BinnedAveragePrecision,
-    BinnedPrecisionRecallCurve,
-    BinnedRecallAtFixedPrecision,
-)
 from torchmetrics.classification.calibration_error import (
     BinaryCalibrationError,
     CalibrationError,
@@ -92,9 +85,6 @@
     Recall,
 )
 from torchmetrics.classification.ranking import (
-    CoverageError,
-    LabelRankingAveragePrecision,
-    LabelRankingLoss,
     MultilabelCoverageError,
     MultilabelRankingAveragePrecision,
     MultilabelRankingLoss,

src/torchmetrics/classification/accuracy.py

@@ -350,8 +350,6 @@ class Accuracy(StatScores):
     changed to subset accuracy (which requires all labels or sub-samples in the sample to
     be correctly predicted) by setting ``subset_accuracy=True``.
 
-    Accepts all input types listed in :ref:`pages/classification:input types`.
-
     Args:
         num_classes:
             Number of classes. Necessary for ``'macro'``, ``'weighted'`` and ``None`` average methods.
@@ -387,11 +385,10 @@ class Accuracy(StatScores):
             - ``'samplewise'``: In this case, the statistics are computed separately for each
               sample on the ``N`` axis, and then averaged over samples.
               The computation for each sample is done by treating the flattened extra axes ``...``
-              (see :ref:`pages/classification:input types`) as the ``N`` dimension within the sample,
+              as the ``N`` dimension within the sample,
               and computing the metric for the sample based on that.
 
             - ``'global'``: In this case the ``N`` and ``...`` dimensions of the inputs
-              (see :ref:`pages/classification:input types`)
               are flattened into a new ``N_X`` sample axis, i.e. the inputs are treated as if they
               were ``(N_X, C)``. From here on the ``average`` parameter applies as usual.
 
@@ -409,9 +406,7 @@ class Accuracy(StatScores):
 
         multiclass:
             Used only in certain special cases, where you want to treat inputs as a different type
-            than what they appear to be. See the parameter's
-            :ref:`documentation section <pages/classification:using the multiclass parameter>`
-            for a more detailed explanation and examples.
+            than what they appear to be.
 
         subset_accuracy:
             Whether to compute subset accuracy for multi-label and multi-dimensional
@@ -557,9 +552,7 @@ def __init__(
             self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
-        """Update state with predictions and targets. See
-        :ref:`pages/classification:input types` for more information on input
-        types.
+        """Update state with predictions and targets.
 
         Args:
             preds: Predictions from model (logits, probabilities, or labels)