Code cleaning after classification refactor 1/n (#1251)

* cleanup * remove some more * fix broken doctest * remove tests Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Lightning-AI · Oct 12, 2022 · 84ef54a · 84ef54a
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diff --git a/CHANGELOG.md b/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
 

diff --git a/docs/source/classification/auc.rst b/docs/source/classification/auc.rst
diff --git a/docs/source/classification/binned_average_precision.rst b/docs/source/classification/binned_average_precision.rst
diff --git a/docs/source/classification/binned_precision_recall_curve.rst b/docs/source/classification/binned_precision_recall_curve.rst
diff --git a/docs/source/classification/binned_recall_fixed_precision.rst b/docs/source/classification/binned_recall_fixed_precision.rst
diff --git a/docs/source/classification/coverage_error.rst b/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:
diff --git a/docs/source/classification/label_ranking_average_precision.rst b/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:
diff --git a/docs/source/classification/label_ranking_loss.rst b/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:
diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -132,7 +132,6 @@ Or directly from conda
  pages/overview
  pages/implement
  pages/lightning
- pages/classification
  pages/retrieval
 
 .. toctree::

diff --git a/docs/source/pages/classification.rst b/docs/source/pages/classification.rst
diff --git a/src/torchmetrics/__init__.py b/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",

diff --git a/src/torchmetrics/classification/__init__.py b/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,

diff --git a/src/torchmetrics/classification/accuracy.py b/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)