Prune deprecated: classification and regression

CHANGELOG.md

@@ -35,6 +35,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   * `PSNR`
 
 
+- Removed deprecated functions, and warnings in classification and regression ([#806](https://github.com/PyTorchLightning/metrics/pull/806))
+  * `FBeta`
+  * `F1`
+  * `Hinge`
+  * `IoU`
+  * `functional.iou`
+  * `MatthewsCorrcoef`
+  * `PearsonCorrcoef`
+  * `SpearmanCorrcoef`
+  * `functional.fbeta`
+  * `functional.f1`
+  * `functional.hinge`
+
+
+
 - Removed deprecated functions, and warnings in detection and pairwise ([#804](https://github.com/PyTorchLightning/metrics/pull/804))
   * `MAP`
   * `functional.pairwise.manhatten`

torchmetrics/__init__.py

@@ -20,10 +20,9 @@
     SignalDistortionRatio,
     SignalNoiseRatio,
 )
-from torchmetrics.classification import (  # noqa: E402, F401
+from torchmetrics.classification import (  # noqa: E402
     AUC,
     AUROC,
-    F1,
     ROC,
     Accuracy,
     AveragePrecision,
@@ -34,15 +33,11 @@
     CohenKappa,
     ConfusionMatrix,
     F1Score,
-    FBeta,
     FBetaScore,
     HammingDistance,
-    Hinge,
     HingeLoss,
-    IoU,
     JaccardIndex,
     KLDivergence,
-    MatthewsCorrcoef,
     MatthewsCorrCoef,
     Precision,
     PrecisionRecallCurve,
@@ -64,10 +59,8 @@
     MeanAbsolutePercentageError,
     MeanSquaredError,
     MeanSquaredLogError,
-    PearsonCorrcoef,
     PearsonCorrCoef,
     R2Score,
-    SpearmanCorrcoef,
     SpearmanCorrCoef,
     SymmetricMeanAbsolutePercentageError,
     TweedieDevianceScore,
@@ -117,16 +110,12 @@
     "TweedieDevianceScore",
     "ExplainedVariance",
     "ExtendedEditDistance",
-    "F1",
     "F1Score",
-    "FBeta",
     "FBetaScore",
     "HammingDistance",
-    "Hinge",
     "HingeLoss",
     "JaccardIndex",
     "KLDivergence",
-    "MatthewsCorrcoef",
     "MatthewsCorrCoef",
     "MaxMetric",
     "MeanAbsoluteError",
@@ -141,7 +130,6 @@
     "MinMetric",
     "MultioutputWrapper",
     "MultiScaleStructuralSimilarityIndexMeasure",
-    "PearsonCorrcoef",
     "PearsonCorrCoef",
     "PermutationInvariantTraining",
     "Precision",
@@ -163,7 +151,6 @@
     "ScaleInvariantSignalDistortionRatio",
     "ScaleInvariantSignalNoiseRatio",
     "SignalNoiseRatio",
-    "SpearmanCorrcoef",
     "SpearmanCorrCoef",
     "Specificity",
     "SQuAD",

torchmetrics/classification/__init__.py

@@ -21,13 +21,12 @@
 from torchmetrics.classification.calibration_error import CalibrationError  # noqa: F401
 from torchmetrics.classification.cohen_kappa import CohenKappa  # noqa: F401
 from torchmetrics.classification.confusion_matrix import ConfusionMatrix  # noqa: F401
-from torchmetrics.classification.f_beta import F1, F1Score, FBeta, FBetaScore  # noqa: F401
+from torchmetrics.classification.f_beta import F1Score, FBetaScore  # noqa: F401
 from torchmetrics.classification.hamming import HammingDistance  # noqa: F401
-from torchmetrics.classification.hinge import Hinge, HingeLoss  # noqa: F401
-from torchmetrics.classification.iou import IoU  # noqa: F401
+from torchmetrics.classification.hinge import HingeLoss  # noqa: F401
 from torchmetrics.classification.jaccard import JaccardIndex  # noqa: F401
 from torchmetrics.classification.kl_divergence import KLDivergence  # noqa: F401
-from torchmetrics.classification.matthews_corrcoef import MatthewsCorrCoef, MatthewsCorrcoef  # noqa: F401
+from torchmetrics.classification.matthews_corrcoef import MatthewsCorrCoef  # noqa: F401
 from torchmetrics.classification.precision_recall import Precision, Recall  # noqa: F401
 from torchmetrics.classification.precision_recall_curve import PrecisionRecallCurve  # noqa: F401
 from torchmetrics.classification.roc import ROC  # noqa: F401

torchmetrics/classification/f_beta.py

@@ -14,12 +14,10 @@
 from typing import Any, Callable, Optional
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor
 
 from torchmetrics.classification.stat_scores import StatScores
 from torchmetrics.functional.classification.f_beta import _fbeta_compute
-from torchmetrics.utilities import _future_warning
 from torchmetrics.utilities.enums import AverageMethod
 
 
@@ -176,51 +174,6 @@ def compute(self) -> Tensor:
         return _fbeta_compute(tp, fp, tn, fn, self.beta, self.ignore_index, self.average, self.mdmc_reduce)
 
 
-class FBeta(FBetaScore):
-    r"""
-    Computes `F-score`_, specifically:
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.FBetaScore`. Will be removed in v0.8.
-
-    Example::
-        >>> f_beta = FBetaScore(num_classes=3, beta=0.5)
-        >>> f_beta(torch.tensor([0, 2, 1, 0, 0, 1]), torch.tensor([0, 1, 2, 0, 1, 2]))
-        tensor(0.3333)
-    """
-
-    @deprecated(target=FBetaScore, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        num_classes: Optional[int] = None,
-        beta: float = 1.0,
-        threshold: float = 0.5,
-        average: str = "micro",
-        mdmc_average: Optional[str] = None,
-        ignore_index: Optional[int] = None,
-        top_k: Optional[int] = None,
-        multiclass: Optional[bool] = None,
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Callable = None,
-    ) -> None:
-        void(
-            num_classes,
-            beta,
-            threshold,
-            average,
-            mdmc_average,
-            ignore_index,
-            top_k,
-            multiclass,
-            compute_on_step,
-            dist_sync_on_step,
-            process_group,
-            dist_sync_fn,
-        )
-
-
 class F1Score(FBetaScore):
     """Computes F1 metric. F1 metrics correspond to a harmonic mean of the precision and recall scores.
 
@@ -347,48 +300,3 @@ def __init__(
             process_group=process_group,
             dist_sync_fn=dist_sync_fn,
         )
-
-
-class F1(F1Score):
-    """Computes F1 metric. F1 metrics correspond to a harmonic mean of the precision and recall scores.
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.F1Score`. Will be removed in v0.8.
-
-    Example:
-        >>> from torchmetrics import F1
-        >>> target = torch.tensor([0, 1, 2, 0, 1, 2])
-        >>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
-        >>> f1 = F1(num_classes=3)
-        >>> f1(preds, target)
-        tensor(0.3333)
-    """
-
-    @deprecated(target=F1Score, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        num_classes: Optional[int] = None,
-        threshold: float = 0.5,
-        average: str = "micro",
-        mdmc_average: Optional[str] = None,
-        ignore_index: Optional[int] = None,
-        top_k: Optional[int] = None,
-        multiclass: Optional[bool] = None,
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Callable = None,
-    ) -> None:
-        void(
-            num_classes,
-            threshold,
-            average,
-            mdmc_average,
-            ignore_index,
-            top_k,
-            multiclass,
-            compute_on_step,
-            dist_sync_on_step,
-            process_group,
-            dist_sync_fn,
-        )

torchmetrics/classification/hinge.py

@@ -13,12 +13,10 @@
 # limitations under the License.
 from typing import Any, Callable, Optional, Union
 
-from deprecate import deprecated, void
 from torch import Tensor, tensor
 
 from torchmetrics.functional.classification.hinge import MulticlassMode, _hinge_compute, _hinge_update
 from torchmetrics.metric import Metric
-from torchmetrics.utilities import _future_warning
 
 
 class HingeLoss(Metric):
@@ -128,41 +126,3 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
 
     def compute(self) -> Tensor:
         return _hinge_compute(self.measure, self.total)
-
-
-class Hinge(HingeLoss):
-    r"""
-    Computes the mean `Hinge loss`_, typically used for Support Vector Machines (SVMs).
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.HingeLoss`. Will be removed in v0.8.
-
-    Example (binary case):
-        >>> import torch
-        >>> hinge = Hinge()
-        >>> hinge(torch.tensor([-2.2, 2.4, 0.1]), torch.tensor([0, 1, 1]))
-        tensor(0.3000)
-
-    Example (default / multiclass case):
-        >>> hinge = Hinge()
-        >>> hinge(torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]]), torch.tensor([0, 1, 2]))
-        tensor(2.9000)
-
-    Example (multiclass example, one vs all mode):
-        >>> hinge = Hinge(multiclass_mode="one-vs-all")
-        >>> hinge(torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]]), torch.tensor([0, 1, 2]))
-        tensor([2.2333, 1.5000, 1.2333])
-
-    """
-
-    @deprecated(target=HingeLoss, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        squared: bool = False,
-        multiclass_mode: Optional[Union[str, MulticlassMode]] = None,
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Callable = None,
-    ) -> None:
-        void(squared, multiclass_mode, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)

torchmetrics/classification/matthews_corrcoef.py

@@ -14,15 +14,13 @@
 from typing import Any, Callable, Optional
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor
 
 from torchmetrics.functional.classification.matthews_corrcoef import (
     _matthews_corrcoef_compute,
     _matthews_corrcoef_update,
 )
 from torchmetrics.metric import Metric
-from torchmetrics.utilities import _future_warning
 
 
 class MatthewsCorrCoef(Metric):
@@ -112,29 +110,3 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
     def compute(self) -> Tensor:
         """Computes matthews correlation coefficient."""
         return _matthews_corrcoef_compute(self.confmat)
-
-
-class MatthewsCorrcoef(MatthewsCorrCoef):
-    """Calculates `Matthews correlation coefficient`_ that measures the general correlation or quality of a
-    classification.
-
-    Example:
-        >>> matthews_corrcoef = MatthewsCorrcoef(num_classes=2)
-        >>> matthews_corrcoef(torch.tensor([0, 1, 0, 0]), torch.tensor([1, 1, 0, 0]))
-        tensor(0.5774)
-
-    .. deprecated:: v0.7
-        Renamed in favor of :class:`torchmetrics.MatthewsCorrCoef`. Will be removed in v0.8.
-    """
-
-    @deprecated(target=MatthewsCorrCoef, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        num_classes: int,
-        threshold: float = 0.5,
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Callable = None,
-    ) -> None:
-        void(num_classes, threshold, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)