[Refactor] Classification 9/n (#1189)

* micro to macro

* new kl

* deprecate old

* move auc

* move tests around

* change import paths

* update test path

* change ref

* Apply suggestions from code review

Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>

* update doctests

* doctests fix

Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>

docs/source/pages/overview.rst

@@ -183,7 +183,7 @@ the following limitations:
 
  - :ref:`image/peak_signal_noise_ratio:Peak Signal-to-Noise Ratio (PSNR)`
  - :ref:`image/structural_similarity:Structural Similarity Index Measure (SSIM)`
- - :ref:`classification/kl_divergence:KL Divergence`
+ - :ref:`regression/kl_divergence:KL Divergence`
 
 You can always check the precision/dtype of the metric by checking the `.dtype` property.
 

src/torchmetrics/__init__.py

@@ -58,7 +58,6 @@
  HammingDistance,
  HingeLoss,
  JaccardIndex,
- KLDivergence,
  LabelRankingAveragePrecision,
  LabelRankingLoss,
  MatthewsCorrCoef,
@@ -121,6 +120,7 @@
 from torchmetrics.regression import ( # noqa: E402
  CosineSimilarity,
  ExplainedVariance,
+ KLDivergence,
  MeanAbsoluteError,
  MeanAbsolutePercentageError,
  MeanSquaredError,

src/torchmetrics/classification/__init__.py

@@ -78,7 +78,6 @@
  MulticlassJaccardIndex,
  MultilabelJaccardIndex,
 )
-from torchmetrics.classification.kl_divergence import KLDivergence # noqa: F401
 from torchmetrics.classification.matthews_corrcoef import ( # noqa: F401
  BinaryMatthewsCorrCoef,
  MatthewsCorrCoef,

src/torchmetrics/classification/accuracy.py

@@ -173,7 +173,7 @@ class MulticlassAccuracy(MulticlassStatScores):
  >>> preds = torch.tensor([2, 1, 0, 1])
  >>> metric = MulticlassAccuracy(num_classes=3)
  >>> metric(preds, target)
- tensor(0.7500)
+ tensor(0.8333)
  >>> metric = MulticlassAccuracy(num_classes=3, average=None)
  >>> metric(preds, target)
  tensor([0.5000, 1.0000, 1.0000])
@@ -189,7 +189,7 @@ class MulticlassAccuracy(MulticlassStatScores):
  ... ])
  >>> metric = MulticlassAccuracy(num_classes=3)
  >>> metric(preds, target)
- tensor(0.7500)
+ tensor(0.8333)
  >>> metric = MulticlassAccuracy(num_classes=3, average=None)
  >>> metric(preds, target)
  tensor([0.5000, 1.0000, 1.0000])
@@ -200,7 +200,7 @@ class MulticlassAccuracy(MulticlassStatScores):
  >>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
  >>> metric = MulticlassAccuracy(num_classes=3, multidim_average='samplewise')
  >>> metric(preds, target)
- tensor([0.5000, 0.3333])
+ tensor([0.5000, 0.2778])
  >>> metric = MulticlassAccuracy(num_classes=3, multidim_average='samplewise', average=None)
  >>> metric(preds, target)
  tensor([[1.0000, 0.0000, 0.5000],

src/torchmetrics/classification/auc.py

@@ -15,9 +15,9 @@
 
 from torch import Tensor
 
-from torchmetrics.functional.classification.auc import _auc_compute, _auc_update
 from torchmetrics.metric import Metric
 from torchmetrics.utilities import rank_zero_warn
+from torchmetrics.utilities.compute import _auc_compute, _auc_format_inputs
 from torchmetrics.utilities.data import dim_zero_cat
 
 
@@ -28,6 +28,9 @@ class AUC(Metric):
  Forward accepts two input tensors that should be 1D and have the same number
  of elements
 
+ .. note::
+ This metric has been deprecated in v0.10 and will be removed in v0.11.
+
  Args:
  reorder: AUC expects its first input to be sorted. If this is not the case,
  setting this argument to ``True`` will use a stable sorting algorithm to
@@ -47,6 +50,11 @@ def __init__(
  **kwargs: Any,
  ) -> None:
  super().__init__(**kwargs)
+ rank_zero_warn(
+ "`torchmetrics.classification.AUC` has been deprecated in v0.10 and will be removed in v0.11."
+ "A functional version is still available in `torchmetrics.utilities.compute`",
+ DeprecationWarning,
+ )
 
  self.reorder = reorder
 
@@ -65,7 +73,7 @@ def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
  preds: Predictions from model (probabilities, or labels)
  target: Ground truth labels
  """
- x, y = _auc_update(preds, target)
+ x, y = _auc_format_inputs(preds, target)
 
  self.x.append(x)
  self.y.append(y)

src/torchmetrics/classification/f_beta.py

@@ -192,7 +192,7 @@ class MulticlassFBetaScore(MulticlassStatScores):
  >>> preds = torch.tensor([2, 1, 0, 1])
  >>> metric = MulticlassFBetaScore(beta=2.0, num_classes=3)
  >>> metric(preds, target)
- tensor(0.7500)
+ tensor(0.7963)
  >>> metric = MulticlassFBetaScore(beta=2.0, num_classes=3, average=None)
  >>> metric(preds, target)
  tensor([0.5556, 0.8333, 1.0000])
@@ -208,7 +208,7 @@ class MulticlassFBetaScore(MulticlassStatScores):
  ... ])
  >>> metric = MulticlassFBetaScore(beta=2.0, num_classes=3)
  >>> metric(preds, target)
- tensor(0.7500)
+ tensor(0.7963)
  >>> metric = MulticlassFBetaScore(beta=2.0, num_classes=3, average=None)
  >>> metric(preds, target)
  tensor([0.5556, 0.8333, 1.0000])
@@ -219,7 +219,7 @@ class MulticlassFBetaScore(MulticlassStatScores):
  >>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
  >>> metric = MulticlassFBetaScore(beta=2.0, num_classes=3, multidim_average='samplewise')
  >>> metric(preds, target)
- tensor([0.5000, 0.3333])
+ tensor([0.4697, 0.2706])
  >>> metric = MulticlassFBetaScore(beta=2.0, num_classes=3, multidim_average='samplewise', average=None)
  >>> metric(preds, target)
  tensor([[0.9091, 0.0000, 0.5000],
@@ -234,7 +234,7 @@ def __init__(
  beta: float,
  num_classes: int,
  top_k: int = 1,
- average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
+ average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
  multidim_average: Literal["global", "samplewise"] = "global",
  ignore_index: Optional[int] = None,
  validate_args: bool = True,
@@ -320,7 +320,7 @@ class MultilabelFBetaScore(MultilabelStatScores):
  >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
  >>> metric = MultilabelFBetaScore(beta=2.0, num_labels=3)
  >>> metric(preds, target)
- tensor(0.6667)
+ tensor(0.6111)
  >>> metric = MultilabelFBetaScore(beta=2.0, num_labels=3, average=None)
  >>> metric(preds, target)
  tensor([1.0000, 0.0000, 0.8333])
@@ -331,7 +331,7 @@ class MultilabelFBetaScore(MultilabelStatScores):
  >>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
  >>> metric = MultilabelFBetaScore(beta=2.0, num_labels=3)
  >>> metric(preds, target)
- tensor(0.6667)
+ tensor(0.6111)
  >>> metric = MultilabelFBetaScore(beta=2.0, num_labels=3, average=None)
  >>> metric(preds, target)
  tensor([1.0000, 0.0000, 0.8333])
@@ -347,7 +347,7 @@ class MultilabelFBetaScore(MultilabelStatScores):
  ... )
  >>> metric = MultilabelFBetaScore(num_labels=3, beta=2.0, multidim_average='samplewise')
  >>> metric(preds, target)
- tensor([0.5882, 0.0000])
+ tensor([0.5556, 0.0000])
  >>> metric = MultilabelFBetaScore(num_labels=3, beta=2.0, multidim_average='samplewise', average=None)
  >>> metric(preds, target)
  tensor([[0.8333, 0.8333, 0.0000],
@@ -363,7 +363,7 @@ def __init__(
  beta: float,
  num_labels: int,
  threshold: float = 0.5,
- average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
+ average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
  multidim_average: Literal["global", "samplewise"] = "global",
  ignore_index: Optional[int] = None,
  validate_args: bool = True,
@@ -536,7 +536,7 @@ class MulticlassF1Score(MulticlassFBetaScore):
  >>> preds = torch.tensor([2, 1, 0, 1])
  >>> metric = MulticlassF1Score(num_classes=3)
  >>> metric(preds, target)
- tensor(0.7500)
+ tensor(0.7778)
  >>> metric = MulticlassF1Score(num_classes=3, average=None)
  >>> metric(preds, target)
  tensor([0.6667, 0.6667, 1.0000])
@@ -552,7 +552,7 @@ class MulticlassF1Score(MulticlassFBetaScore):
  ... ])
  >>> metric = MulticlassF1Score(num_classes=3)
  >>> metric(preds, target)
- tensor(0.7500)
+ tensor(0.7778)
  >>> metric = MulticlassF1Score(num_classes=3, average=None)
  >>> metric(preds, target)
  tensor([0.6667, 0.6667, 1.0000])
@@ -563,7 +563,7 @@ class MulticlassF1Score(MulticlassFBetaScore):
  >>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
  >>> metric = MulticlassF1Score(num_classes=3, multidim_average='samplewise')
  >>> metric(preds, target)
- tensor([0.5000, 0.3333])
+ tensor([0.4333, 0.2667])
  >>> metric = MulticlassF1Score(num_classes=3, multidim_average='samplewise', average=None)
  >>> metric(preds, target)
  tensor([[0.8000, 0.0000, 0.5000],
@@ -577,7 +577,7 @@ def __init__(
  self,
  num_classes: int,
  top_k: int = 1,
- average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
+ average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
  multidim_average: Literal["global", "samplewise"] = "global",
  ignore_index: Optional[int] = None,
  validate_args: bool = True,
@@ -654,7 +654,7 @@ class MultilabelF1Score(MultilabelFBetaScore):
  >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
  >>> metric = MultilabelF1Score(num_labels=3)
  >>> metric(preds, target)
- tensor(0.6667)
+ tensor(0.5556)
  >>> metric = MultilabelF1Score(num_labels=3, average=None)
  >>> metric(preds, target)
  tensor([1.0000, 0.0000, 0.6667])
@@ -665,7 +665,7 @@ class MultilabelF1Score(MultilabelFBetaScore):
  >>> preds = torch.tensor([[0.11, 0.22, 0.84], [0.73, 0.33, 0.92]])
  >>> metric = MultilabelF1Score(num_labels=3)
  >>> metric(preds, target)
- tensor(0.6667)
+ tensor(0.5556)
  >>> metric = MultilabelF1Score(num_labels=3, average=None)
  >>> metric(preds, target)
  tensor([1.0000, 0.0000, 0.6667])
@@ -681,7 +681,7 @@ class MultilabelF1Score(MultilabelFBetaScore):
  ... )
  >>> metric = MultilabelF1Score(num_labels=3, multidim_average='samplewise')
  >>> metric(preds, target)
- tensor([0.5000, 0.0000])
+ tensor([0.4444, 0.0000])
  >>> metric = MultilabelF1Score(num_labels=3, multidim_average='samplewise', average=None)
  >>> metric(preds, target)
  tensor([[0.6667, 0.6667, 0.0000],
@@ -696,7 +696,7 @@ def __init__(
  self,
  num_labels: int,
  threshold: float = 0.5,
- average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
+ average: Optional[Literal["micro", "macro", "weighted", "none"]] = "macro",
  multidim_average: Literal["global", "samplewise"] = "global",
  ignore_index: Optional[int] = None,
  validate_args: bool = True,

src/torchmetrics/classification/hamming.py

@@ -165,7 +165,7 @@ class MulticlassHammingDistance(MulticlassStatScores):
  >>> preds = torch.tensor([2, 1, 0, 1])
  >>> metric = MulticlassHammingDistance(num_classes=3)
  >>> metric(preds, target)
- tensor(0.2500)
+ tensor(0.1667)
  >>> metric = MulticlassHammingDistance(num_classes=3, average=None)
  >>> metric(preds, target)
  tensor([0.5000, 0.0000, 0.0000])
@@ -181,7 +181,7 @@ class MulticlassHammingDistance(MulticlassStatScores):
  ... ])
  >>> metric = MulticlassHammingDistance(num_classes=3)
  >>> metric(preds, target)
- tensor(0.2500)
+ tensor(0.1667)
  >>> metric = MulticlassHammingDistance(num_classes=3, average=None)
  >>> metric(preds, target)
  tensor([0.5000, 0.0000, 0.0000])
@@ -192,7 +192,7 @@ class MulticlassHammingDistance(MulticlassStatScores):
  >>> preds = torch.tensor([[[0, 2], [2, 0], [0, 1]], [[2, 2], [2, 1], [1, 0]]])
  >>> metric = MulticlassHammingDistance(num_classes=3, multidim_average='samplewise')
  >>> metric(preds, target)
- tensor([0.5000, 0.6667])
+ tensor([0.5000, 0.7222])
  >>> metric = MulticlassHammingDistance(num_classes=3, multidim_average='samplewise', average=None)
  >>> metric(preds, target)
  tensor([[0.0000, 1.0000, 0.5000],

src/torchmetrics/classification/kl_divergence.py

@@ -13,16 +13,13 @@
 # limitations under the License.
 from typing import Any
 
-import torch
-from torch import Tensor
 from typing_extensions import Literal
 
-from torchmetrics.functional.classification.kl_divergence import _kld_compute, _kld_update
-from torchmetrics.metric import Metric
-from torchmetrics.utilities.data import dim_zero_cat
+from torchmetrics.regression.kl_divergence import KLDivergence as _KLDivergence
+from torchmetrics.utilities.prints import rank_zero_warn
 
 
-class KLDivergence(Metric):
+class KLDivergence(_KLDivergence):
  r"""Computes the `KL divergence`_:
 
  .. math::
@@ -46,6 +43,8 @@ class KLDivergence(Metric):
 
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
 
+ .. note::
+ This metric have been moved to the regression package in v0.10 and this version will be removed in v0.11.
 
  Raises:
  TypeError:
@@ -65,41 +64,16 @@ class KLDivergence(Metric):
  tensor(0.0853)
 
  """
- is_differentiable: bool = True
- higher_is_better: bool = False
- full_state_update: bool = False
- total: Tensor
 
  def __init__(
  self,
  log_prob: bool = False,
  reduction: Literal["mean", "sum", "none", None] = "mean",
  **kwargs: Any,
  ) -> None:
- super().__init__(**kwargs)
- if not isinstance(log_prob, bool):
- raise TypeError(f"Expected argument `log_prob` to be bool but got {log_prob}")
- self.log_prob = log_prob
-
- allowed_reduction = ["mean", "sum", "none", None]
- if reduction not in allowed_reduction:
- raise ValueError(f"Expected argument `reduction` to be one of {allowed_reduction} but got {reduction}")
- self.reduction = reduction
-
- if self.reduction in ["mean", "sum"]:
- self.add_state("measures", torch.tensor(0.0), dist_reduce_fx="sum")
- else:
- self.add_state("measures", [], dist_reduce_fx="cat")
- self.add_state("total", torch.tensor(0), dist_reduce_fx="sum")
-
- def update(self, p: Tensor, q: Tensor) -> None: # type: ignore
- measures, total = _kld_update(p, q, self.log_prob)
- if self.reduction is None or self.reduction == "none":
- self.measures.append(measures)
- else:
- self.measures += measures.sum()
- self.total += total
-
- def compute(self) -> Tensor:
- measures = dim_zero_cat(self.measures) if self.reduction is None or self.reduction == "none" else self.measures
- return _kld_compute(measures, self.total, self.reduction)
+ super().__init__(log_prob, reduction, **kwargs)
+ rank_zero_warn(
+ "`torchmetrics.classification.KLDivergence` have been moved to `torchmetrics.regression.KLDivergence`"
+ " from v0.10 and this version will be removed in v0.11. Please update import paths.",
+ DeprecationWarning,
+ )