Remove deprecated metrics (#545)

Co-authored-by: Jirka Borovec <[email protected]>

torchmetrics/functional/__init__.py

@@ -27,7 +27,7 @@
 from torchmetrics.functional.classification.hamming_distance import hamming_distance
 from torchmetrics.functional.classification.hinge import hinge
 from torchmetrics.functional.classification.iou import iou
-from torchmetrics.functional.classification.kl_divergence import kl_divergence, kldivergence
+from torchmetrics.functional.classification.kl_divergence import kl_divergence
 from torchmetrics.functional.classification.matthews_corrcoef import matthews_corrcoef
 from torchmetrics.functional.classification.precision_recall import precision, precision_recall, recall
 from torchmetrics.functional.classification.precision_recall_curve import precision_recall_curve
@@ -45,7 +45,6 @@
 from torchmetrics.functional.regression.mean_squared_log_error import mean_squared_log_error
 from torchmetrics.functional.regression.pearson import pearson_corrcoef
 from torchmetrics.functional.regression.r2 import r2_score
-from torchmetrics.functional.regression.r2score import r2score
 from torchmetrics.functional.regression.spearman import spearman_corrcoef
 from torchmetrics.functional.regression.symmetric_mean_absolute_percentage_error import (
     symmetric_mean_absolute_percentage_error,

torchmetrics/functional/classification/kl_divergence.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 from typing import Optional, Tuple
-from warnings import warn
 
 import torch
 from torch import Tensor
@@ -109,21 +108,3 @@ def kl_divergence(p: Tensor, q: Tensor, log_prob: bool = False, reduction: Optio
     """
     measures, total = _kld_update(p, q, log_prob)
     return _kld_compute(measures, total, reduction)
-
-
-def kldivergence(p: Tensor, q: Tensor, log_prob: bool = False, reduction: Optional[str] = "mean") -> Tensor:
-    r"""Computes `KL divergence`_
-
-    .. deprecated:: v0.5
-        `kldivergence` was renamed as `kl_divergence` in v0.5 and it will be removed in v0.6
-
-    Example:
-        >>> import torch
-        >>> from torchmetrics.functional import kldivergence
-        >>> p = torch.tensor([[0.36, 0.48, 0.16]])
-        >>> q = torch.tensor([[1/3, 1/3, 1/3]])
-        >>> kldivergence(p, q)
-        tensor(0.0853)
-    """
-    warn("`kldivergence` was renamed as `kl_divergence` in v0.5 and it will be removed in v0.6", DeprecationWarning)
-    return kl_divergence(p, q, log_prob, reduction)

torchmetrics/functional/regression/__init__.py

@@ -23,6 +23,5 @@
 from torchmetrics.functional.regression.mean_squared_log_error import mean_squared_log_error  # noqa: F401
 from torchmetrics.functional.regression.pearson import pearson_corrcoef  # noqa: F401
 from torchmetrics.functional.regression.r2 import r2_score  # noqa: F401
-from torchmetrics.functional.regression.r2score import r2score  # noqa: F401
 from torchmetrics.functional.regression.spearman import spearman_corrcoef  # noqa: F401
 from torchmetrics.functional.regression.tweedie_deviance import tweedie_deviance_score  # noqa: F401