ruff: fix couple of issues in src (#1586)

src/torchmetrics/classification/group_fairness.py

@@ -132,7 +132,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor, groups: torch.Tensor
     def compute(
         self,
     ) -> Dict[str, torch.Tensor]:
-        """Computes tp, fp, tn and fn rates based on inputs passed in to ``update`` previously."""
+        """Compute tp, fp, tn and fn rates based on inputs passed in to ``update`` previously."""
         results = torch.stack((self.tp, self.fp, self.tn, self.fn), dim=1)
 
         return {f"group_{i}": group / group.sum() for i, group in enumerate(results)}
@@ -247,7 +247,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor, groups: Optional[tor
     def compute(
         self,
     ) -> Dict[str, torch.Tensor]:
-        """Computes fairness criteria based on inputs passed in to ``update`` previously."""
+        """Compute fairness criteria based on inputs passed in to ``update`` previously."""
         if self.task == "demographic_parity":
             return _compute_binary_demographic_parity(self.tp, self.fp, self.tn, self.fn)
 

src/torchmetrics/functional/classification/group_fairness.py

@@ -59,7 +59,7 @@ def _binary_groups_stat_scores(
     ignore_index: Optional[int] = None,
     validate_args: bool = True,
 ) -> List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]]:
-    """Computes the true/false positives and true/false negatives rates for binary classification by group.
+    """Compute the true/false positives and true/false negatives rates for binary classification by group.
 
     Related to `Type I and Type II errors`_.
     """
@@ -113,7 +113,7 @@ def binary_groups_stat_rates(
     ignore_index: Optional[int] = None,
     validate_args: bool = True,
 ) -> Dict[str, torch.Tensor]:
-    r"""Computes the true/false positives and true/false negatives rates for binary classification by group.
+    r"""Compute the true/false positives and true/false negatives rates for binary classification by group.
 
     Related to `Type I and Type II errors`_.
 
@@ -331,7 +331,7 @@ def binary_fairness(
     ignore_index: Optional[int] = None,
     validate_args: bool = True,
 ) -> Dict[str, torch.Tensor]:
-    r"""This function is a simple wrapper to get the task specific versions of the metric.
+    r"""Compute either `Demographic parity`_ and `Equal opportunity`_ ratio for binary classification problems.
 
     This is done by setting the ``task`` argument to either ``'demographic_parity'``, ``'equal_opportunity'``
     or ``all``. See the documentation of :func:`_compute_binary_demographic_parity`

src/torchmetrics/functional/image/helper.py

@@ -108,7 +108,7 @@ def _reflection_pad_2d(inputs: Tensor, pad: int, outer_pad: int = 0) -> Tensor:
 
 
 def _uniform_filter(inputs: Tensor, window_size: int) -> Tensor:
-    """Applies uniform filtew with a window of a given size over the input image.
+    """Apply uniform filter with a window of a given size over the input image.
 
     Args:
         inputs: Input image

src/torchmetrics/functional/image/rase.py

@@ -24,7 +24,7 @@
 def _rase_update(
     preds: Tensor, target: Tensor, window_size: int, rmse_map: Tensor, target_sum: Tensor, total_images: Tensor
 ) -> Tuple[Tensor, Tensor, Tensor]:
-    """Calculates the sum of RMSE map values for the batch of examples and update intermediate states.
+    """Calculate the sum of RMSE map values for the batch of examples and update intermediate states.
 
     Args:
         preds: Deformed image
@@ -67,7 +67,7 @@ def _rase_compute(rmse_map: Tensor, target_sum: Tensor, total_images: Tensor, wi
 
 
 def relative_average_spectral_error(preds: Tensor, target: Tensor, window_size: int = 8) -> Tensor:
-    """Computes Relative Average Spectral Error (RASE) (RelativeAverageSpectralError_).
+    """Compute Relative Average Spectral Error (RASE) (RelativeAverageSpectralError_).
 
     Args:
         preds: Deformed image

src/torchmetrics/functional/image/rmse_sw.py

@@ -29,7 +29,7 @@ def _rmse_sw_update(
     rmse_map: Optional[Tensor],
     total_images: Optional[Tensor],
 ) -> Tuple[Tensor, Tensor, Tensor]:
-    """Calculates the sum of RMSE values and RMSE map for the batch of examples and update intermediate states.
+    """Calculate the sum of RMSE values and RMSE map for the batch of examples and update intermediate states.
 
     Args:
         preds: Deformed image
@@ -89,7 +89,7 @@ def _rmse_sw_update(
 def _rmse_sw_compute(
     rmse_val_sum: Optional[Tensor], rmse_map: Tensor, total_images: Tensor
 ) -> Tuple[Optional[Tensor], Tensor]:
-    """Computes RMSE from the aggregated RMSE value. Optionally also computes the mean value for RMSE map.
+    """Compute RMSE from the aggregated RMSE value. Optionally also computes the mean value for RMSE map.
 
     Args:
         rmse_val_sum: Sum of RMSE over all examples
@@ -109,7 +109,7 @@ def _rmse_sw_compute(
 def root_mean_squared_error_using_sliding_window(
     preds: Tensor, target: Tensor, window_size: int = 8, return_rmse_map: bool = False
 ) -> Union[Optional[Tensor], Tuple[Optional[Tensor], Tensor]]:
-    """Computes Root Mean Squared Error (RMSE) using sliding window.
+    """Compute Root Mean Squared Error (RMSE) using sliding window.
 
     Args:
         preds: Deformed image

src/torchmetrics/image/rase.py

@@ -81,7 +81,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:
         self.target.append(target)
 
     def compute(self) -> Tensor:
-        """Computes Relative Average Spectral Error (RASE)."""
+        """Compute Relative Average Spectral Error (RASE)."""
         preds = dim_zero_cat(self.preds)
         target = dim_zero_cat(self.target)
         return relative_average_spectral_error(preds, target, self.window_size)

src/torchmetrics/image/rmse_sw.py

@@ -81,7 +81,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:
         )
 
     def compute(self) -> Optional[Tensor]:
-        """Computes Root Mean Squared Error (using sliding window) and potentially return RMSE map."""
+        """Compute Root Mean Squared Error (using sliding window) and potentially return RMSE map."""
         assert self.rmse_map is not None
         rmse, _ = _rmse_sw_compute(self.rmse_val_sum, self.rmse_map, self.total_images)
         return rmse

src/torchmetrics/utilities/plot.py

@@ -37,7 +37,7 @@
 
     @contextmanager
     def style_change(*args: Any, **kwargs: Any) -> Generator:
-        """Default no-ops decorator if matplotlib is not installed."""
+        """No-ops decorator if matplotlib is not installed."""
         yield
 
 

tests/unittests/regression/test_mean_error.py

@@ -173,7 +173,6 @@ class TestMeanError(MetricTester):
     def test_mean_error_class(
         self, preds, target, ref_metric, metric_class, metric_functional, sk_fn, metric_args, ddp
     ):
-        # todo: `metric_functional` is unused
         self.run_class_metric_test(
             ddp=ddp,
             preds=preds,
@@ -186,7 +185,6 @@ def test_mean_error_class(
     def test_mean_error_functional(
         self, preds, target, ref_metric, metric_class, metric_functional, sk_fn, metric_args
     ):
-        # todo: `metric_class` is unused
         self.run_functional_metric_test(
             preds=preds,
             target=target,

tests/unittests/regression/test_r2.py

@@ -83,7 +83,6 @@ def test_r2(self, adjusted, multioutput, preds, target, ref_metric, num_outputs,
         )
 
     def test_r2_functional(self, adjusted, multioutput, preds, target, ref_metric, num_outputs):
-        # todo: `num_outputs` is unused
         self.run_functional_metric_test(
             preds,
             target,