rename CorrCoef

CHANGELOG.md

@@ -56,6 +56,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Renamed IoU -> Jaccard Index ([#662](https://github.com/PyTorchLightning/metrics/pull/662))
 
 
+- Renamed correlation coefficient classes: ([#710](https://github.com/PyTorchLightning/metrics/pull/710))
+  * `MatthewsCorrcoef` -> `MatthewsCorrCoef`
+  * `PearsonCorrcoef` -> `PearsonCorrCoef`
+  * `SpearmanCorrcoef` -> `SpearmanCorrCoef`
+
+
 ### Removed
 
 - Removed `embedding_similarity` metric ([#638](https://github.com/PyTorchLightning/metrics/pull/638))

docs/source/references/modules.rst

@@ -314,10 +314,10 @@ KLDivergence
 .. autoclass:: torchmetrics.KLDivergence
     :noindex:
 
-MatthewsCorrcoef
+MatthewsCorrCoef
 ~~~~~~~~~~~~~~~~
 
-.. autoclass:: torchmetrics.MatthewsCorrcoef
+.. autoclass:: torchmetrics.MatthewsCorrCoef
     :noindex:
 
 Precision
@@ -460,10 +460,10 @@ MeanSquaredLogError
     :noindex:
 
 
-PearsonCorrcoef
+PearsonCorrCoef
 ~~~~~~~~~~~~~~~
 
-.. autoclass:: torchmetrics.PearsonCorrcoef
+.. autoclass:: torchmetrics.PearsonCorrCoef
     :noindex:
 
 
@@ -474,10 +474,10 @@ R2Score
     :noindex:
 
 
-SpearmanCorrcoef
+SpearmanCorrCoef
 ~~~~~~~~~~~~~~~~
 
-.. autoclass:: torchmetrics.SpearmanCorrcoef
+.. autoclass:: torchmetrics.SpearmanCorrCoef
     :noindex:
 
 SymmetricMeanAbsolutePercentageError

tests/classification/test_matthews_corrcoef.py

@@ -25,7 +25,7 @@
 from tests.classification.inputs import _input_multilabel_prob as _input_mlb_prob
 from tests.helpers import seed_all
 from tests.helpers.testers import NUM_CLASSES, THRESHOLD, MetricTester
-from torchmetrics.classification.matthews_corrcoef import MatthewsCorrcoef
+from torchmetrics.classification.matthews_corrcoef import MatthewsCorrCoef
 from torchmetrics.functional.classification.matthews_corrcoef import matthews_corrcoef
 
 seed_all(42)
@@ -108,7 +108,7 @@ def test_matthews_corrcoef(self, preds, target, sk_metric, num_classes, ddp, dis
             ddp=ddp,
             preds=preds,
             target=target,
-            metric_class=MatthewsCorrcoef,
+            metric_class=MatthewsCorrCoef,
             sk_metric=sk_metric,
             dist_sync_on_step=dist_sync_on_step,
             metric_args={
@@ -133,7 +133,7 @@ def test_matthews_corrcoef_differentiability(self, preds, target, sk_metric, num
         self.run_differentiability_test(
             preds=preds,
             target=target,
-            metric_module=MatthewsCorrcoef,
+            metric_module=MatthewsCorrCoef,
             metric_functional=matthews_corrcoef,
             metric_args={
                 "num_classes": num_classes,

tests/regression/test_pearson.py

@@ -20,7 +20,7 @@
 from tests.helpers import seed_all
 from tests.helpers.testers import BATCH_SIZE, NUM_BATCHES, MetricTester
 from torchmetrics.functional.regression.pearson import pearson_corrcoef
-from torchmetrics.regression.pearson import PearsonCorrcoef
+from torchmetrics.regression.pearson import PearsonCorrCoef
 
 seed_all(42)
 
@@ -59,7 +59,7 @@ def test_pearson_corrcoef(self, preds, target, ddp):
             ddp=ddp,
             preds=preds,
             target=target,
-            metric_class=PearsonCorrcoef,
+            metric_class=PearsonCorrCoef,
             sk_metric=_sk_pearsonr,
             dist_sync_on_step=False,
         )
@@ -71,21 +71,21 @@ def test_pearson_corrcoef_functional(self, preds, target):
 
     def test_pearson_corrcoef_differentiability(self, preds, target):
         self.run_differentiability_test(
-            preds=preds, target=target, metric_module=PearsonCorrcoef, metric_functional=pearson_corrcoef
+            preds=preds, target=target, metric_module=PearsonCorrCoef, metric_functional=pearson_corrcoef
         )
 
     # Pearson half + cpu does not work due to missing support in torch.sqrt
     @pytest.mark.xfail(reason="PearsonCorrcoef metric does not support cpu + half precision")
     def test_pearson_corrcoef_half_cpu(self, preds, target):
-        self.run_precision_test_cpu(preds, target, PearsonCorrcoef, pearson_corrcoef)
+        self.run_precision_test_cpu(preds, target, PearsonCorrCoef, pearson_corrcoef)
 
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires cuda")
     def test_pearson_corrcoef_half_gpu(self, preds, target):
-        self.run_precision_test_gpu(preds, target, PearsonCorrcoef, pearson_corrcoef)
+        self.run_precision_test_gpu(preds, target, PearsonCorrCoef, pearson_corrcoef)
 
 
 def test_error_on_different_shape():
-    metric = PearsonCorrcoef()
+    metric = PearsonCorrCoef()
     with pytest.raises(RuntimeError, match="Predictions and targets are expected to have the same shape"):
         metric(torch.randn(100), torch.randn(50))
 

tests/regression/test_spearman.py

@@ -20,7 +20,7 @@
 from tests.helpers import seed_all
 from tests.helpers.testers import BATCH_SIZE, NUM_BATCHES, MetricTester
 from torchmetrics.functional.regression.spearman import _rank_data, spearman_corrcoef
-from torchmetrics.regression.spearman import SpearmanCorrcoef
+from torchmetrics.regression.spearman import SpearmanCorrCoef
 
 seed_all(42)
 
@@ -83,7 +83,7 @@ def test_spearman_corrcoef(self, preds, target, ddp, dist_sync_on_step):
             ddp,
             preds,
             target,
-            SpearmanCorrcoef,
+            SpearmanCorrCoef,
             _sk_metric,
             dist_sync_on_step,
         )
@@ -93,21 +93,21 @@ def test_spearman_corrcoef_functional(self, preds, target):
 
     def test_spearman_corrcoef_differentiability(self, preds, target):
         self.run_differentiability_test(
-            preds=preds, target=target, metric_module=SpearmanCorrcoef, metric_functional=spearman_corrcoef
+            preds=preds, target=target, metric_module=SpearmanCorrCoef, metric_functional=spearman_corrcoef
         )
 
     # Spearman half + cpu does not work due to missing support in torch.arange
     @pytest.mark.xfail(reason="Spearman metric does not support cpu + half precision")
     def test_spearman_corrcoef_half_cpu(self, preds, target):
-        self.run_precision_test_cpu(preds, target, SpearmanCorrcoef, spearman_corrcoef)
+        self.run_precision_test_cpu(preds, target, SpearmanCorrCoef, spearman_corrcoef)
 
     @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires cuda")
     def test_spearman_corrcoef_half_gpu(self, preds, target):
-        self.run_precision_test_gpu(preds, target, SpearmanCorrcoef, spearman_corrcoef)
+        self.run_precision_test_gpu(preds, target, SpearmanCorrCoef, spearman_corrcoef)
 
 
 def test_error_on_different_shape():
-    metric = SpearmanCorrcoef()
+    metric = SpearmanCorrCoef()
     with pytest.raises(RuntimeError, match="Predictions and targets are expected to have the same shape"):
         metric(torch.randn(100), torch.randn(50))
 

torchmetrics/__init__.py

@@ -34,6 +34,7 @@
     JaccardIndex,
     KLDivergence,
     MatthewsCorrcoef,
+    MatthewsCorrCoef,
     Precision,
     PrecisionRecallCurve,
     Recall,
@@ -51,8 +52,10 @@
     MeanSquaredError,
     MeanSquaredLogError,
     PearsonCorrcoef,
+    PearsonCorrCoef,
     R2Score,
     SpearmanCorrcoef,
+    SpearmanCorrCoef,
     SymmetricMeanAbsolutePercentageError,
     TweedieDevianceScore,
 )
@@ -106,6 +109,7 @@
     "JaccardIndex",
     "KLDivergence",
     "MatthewsCorrcoef",
+    "MatthewsCorrCoef",
     "MaxMetric",
     "MeanAbsoluteError",
     "MeanAbsolutePercentageError",
@@ -119,6 +123,7 @@
     "MinMetric",
     "MultioutputWrapper",
     "PearsonCorrcoef",
+    "PearsonCorrCoef",
     "PIT",
     "Precision",
     "PrecisionRecallCurve",
@@ -140,6 +145,7 @@
     "SI_SNR",
     "SNR",
     "SpearmanCorrcoef",
+    "SpearmanCorrCoef",
     "Specificity",
     "SQuAD",
     "SSIM",

torchmetrics/classification/__init__.py

@@ -27,7 +27,7 @@
 from torchmetrics.classification.iou import IoU  # 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  # noqa: F401
+from torchmetrics.classification.matthews_corrcoef import MatthewsCorrCoef, 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/matthews_corrcoef.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from typing import Any, Callable, Optional
+from warnings import warn
 
 import torch
 from torch import Tensor
@@ -23,7 +24,7 @@
 from torchmetrics.metric import Metric
 
 
-class MatthewsCorrcoef(Metric):
+class MatthewsCorrCoef(Metric):
     r"""
     Calculates `Matthews correlation coefficient`_ that measures
     the general correlation or quality of a classification. In the binary case it
@@ -65,10 +66,10 @@ class MatthewsCorrcoef(Metric):
             will be used to perform the allgather
 
     Example:
-        >>> from torchmetrics import MatthewsCorrcoef
+        >>> from torchmetrics import MatthewsCorrCoef
         >>> target = torch.tensor([1, 1, 0, 0])
         >>> preds = torch.tensor([0, 1, 0, 0])
-        >>> matthews_corrcoef = MatthewsCorrcoef(num_classes=2)
+        >>> matthews_corrcoef = MatthewsCorrCoef(num_classes=2)
         >>> matthews_corrcoef(preds, target)
         tensor(0.5774)
 
@@ -110,3 +111,32 @@ 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.
+    """
+
+    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:
+        warn(
+            "`MatthewsCorrcoef` was renamed to `MatthewsCorrCoef` in v0.7 and it will be removed in v0.8",
+            DeprecationWarning,
+        )
+        super().__init__(num_classes, threshold, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)

torchmetrics/regression/__init__.py

@@ -17,9 +17,9 @@
 from torchmetrics.regression.mean_absolute_percentage_error import MeanAbsolutePercentageError  # noqa: F401
 from torchmetrics.regression.mean_squared_error import MeanSquaredError  # noqa: F401
 from torchmetrics.regression.mean_squared_log_error import MeanSquaredLogError  # noqa: F401
-from torchmetrics.regression.pearson import PearsonCorrcoef  # noqa: F401
+from torchmetrics.regression.pearson import PearsonCorrCoef, PearsonCorrcoef  # noqa: F401
 from torchmetrics.regression.r2 import R2Score  # noqa: F401
-from torchmetrics.regression.spearman import SpearmanCorrcoef  # noqa: F401
+from torchmetrics.regression.spearman import SpearmanCorrCoef, SpearmanCorrcoef  # noqa: F401
 from torchmetrics.regression.symmetric_mean_absolute_percentage_error import (  # noqa: F401
     SymmetricMeanAbsolutePercentageError,
 )

torchmetrics/regression/pearson.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from typing import Any, List, Optional, Tuple
+from warnings import warn
 
 import torch
 from torch import Tensor
@@ -52,7 +53,7 @@ def _final_aggregation(
     return var_x, var_y, corr_xy, nb
 
 
-class PearsonCorrcoef(Metric):
+class PearsonCorrCoef(Metric):
     r"""
     Computes `Pearson Correlation Coefficient`_:
 
@@ -77,10 +78,10 @@ class PearsonCorrcoef(Metric):
             Specify the process group on which synchronization is called.
 
     Example:
-        >>> from torchmetrics import PearsonCorrcoef
+        >>> from torchmetrics import PearsonCorrCoef
         >>> target = torch.tensor([3, -0.5, 2, 7])
         >>> preds = torch.tensor([2.5, 0.0, 2, 8])
-        >>> pearson = PearsonCorrcoef()
+        >>> pearson = PearsonCorrCoef()
         >>> pearson(preds, target)
         tensor(0.9849)
 
@@ -139,3 +140,31 @@ def compute(self) -> Tensor:
             n_total = self.n_total
 
         return _pearson_corrcoef_compute(var_x, var_y, corr_xy, n_total)
+
+
+class PearsonCorrcoef(PearsonCorrCoef):
+    r"""
+    Computes `Pearson Correlation Coefficient`_:
+
+    Example:
+        >>> pearson = PearsonCorrcoef()
+        >>> pearson(torch.tensor([2.5, 0.0, 2, 8]), torch.tensor([3, -0.5, 2, 7]))
+        tensor(0.9849)
+
+    .. deprecated:: v0.7
+        Renamed in favor of :class:`torchmetrics.PearsonCorrCoef`. Will be removed in v0.8.
+
+    """
+
+    def __init__(
+        self,
+        compute_on_step: bool = True,
+        dist_sync_on_step: bool = False,
+        process_group: Optional[Any] = None,
+    ) -> None:
+        warn(
+            "`PearsonCorrcoef` was renamed to `PearsonCorrCoef` in v0.7 and it will be removed in v0.8",
+            DeprecationWarning,
+        )
+
+        super().__init__(compute_on_step, dist_sync_on_step, process_group)