ruff: src public method

pyproject.toml

@@ -109,7 +109,6 @@ unfixable = ["F401"]
 "__init__.py" = ["D100"]
 "src/**" = [
  "D101", # todo # Missing docstring in public class
- "D102", # todo # Missing docstring in public method
  "D103", # todo # Missing docstring in public function
  "D105", # todo # Missing docstring in magic method
  "D205", # todo # 1 blank line required between summary line and description

src/torchmetrics/classification/accuracy.py

@@ -491,6 +491,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTask.from_str(task)
  kwargs.update(
  {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}

src/torchmetrics/classification/auroc.py

@@ -108,6 +108,7 @@ def __init__(
  self.max_fpr = max_fpr
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
  return _binary_auroc_compute(state, self.thresholds, self.max_fpr)
 
@@ -207,6 +208,7 @@ def __init__(
  self.validate_args = validate_args
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
  return _multiclass_auroc_compute(state, self.num_classes, self.average, self.thresholds)
 
@@ -308,6 +310,7 @@ def __init__(
  self.validate_args = validate_args
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
  return _multilabel_auroc_compute(state, self.num_labels, self.average, self.thresholds, self.ignore_index)
 
@@ -353,6 +356,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTask.from_str(task)
  kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
  if task == ClassificationTask.BINARY:

src/torchmetrics/classification/average_precision.py

@@ -98,6 +98,7 @@ class BinaryAveragePrecision(BinaryPrecisionRecallCurve):
  full_state_update: bool = False
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
  return _binary_average_precision_compute(state, self.thresholds)
 
@@ -201,6 +202,7 @@ def __init__(
  self.validate_args = validate_args
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
  return _multiclass_average_precision_compute(state, self.num_classes, self.average, self.thresholds)
 
@@ -307,6 +309,7 @@ def __init__(
  self.validate_args = validate_args
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  state = [dim_zero_cat(self.preds), dim_zero_cat(self.target)] if self.thresholds is None else self.confmat
  return _multilabel_average_precision_compute(
  state, self.num_labels, self.average, self.thresholds, self.ignore_index
@@ -357,6 +360,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTask.from_str(task)
  kwargs.update({"thresholds": thresholds, "ignore_index": ignore_index, "validate_args": validate_args})
  if task == ClassificationTask.BINARY:

src/torchmetrics/classification/calibration_error.py

@@ -114,6 +114,7 @@ def __init__(
  self.add_state("accuracies", [], dist_reduce_fx="cat")
 
  def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
+ """Update metric states with predictions and targets."""
  if self.validate_args:
  _binary_calibration_error_tensor_validation(preds, target, self.ignore_index)
  preds, target = _binary_confusion_matrix_format(
@@ -124,6 +125,7 @@ def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
  self.accuracies.append(accuracies)
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  confidences = dim_zero_cat(self.confidences)
  accuracies = dim_zero_cat(self.accuracies)
  return _ce_compute(confidences, accuracies, self.n_bins, norm=self.norm)
@@ -217,6 +219,7 @@ def __init__(
  self.add_state("accuracies", [], dist_reduce_fx="cat")
 
  def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
+ """Update metric states with predictions and targets."""
  if self.validate_args:
  _multiclass_calibration_error_tensor_validation(preds, target, self.num_classes, self.ignore_index)
  preds, target = _multiclass_confusion_matrix_format(
@@ -227,6 +230,7 @@ def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
  self.accuracies.append(accuracies)
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  confidences = dim_zero_cat(self.confidences)
  accuracies = dim_zero_cat(self.accuracies)
  return _ce_compute(confidences, accuracies, self.n_bins, norm=self.norm)
@@ -268,6 +272,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTaskNoMultilabel.from_str(task)
  kwargs.update({"n_bins": n_bins, "norm": norm, "ignore_index": ignore_index, "validate_args": validate_args})
  if task == ClassificationTaskNoMultilabel.BINARY:

src/torchmetrics/classification/cohen_kappa.py

@@ -102,6 +102,7 @@ def __init__(
  self.validate_args = validate_args
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  return _cohen_kappa_reduce(self.confmat, self.weights)
 
 
@@ -184,6 +185,7 @@ def __init__(
  self.validate_args = validate_args
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  return _cohen_kappa_reduce(self.confmat, self.weights)
 
 
@@ -222,6 +224,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTaskNoMultilabel.from_str(task)
  kwargs.update({"weights": weights, "ignore_index": ignore_index, "validate_args": validate_args})
  if task == ClassificationTaskNoMultilabel.BINARY:

src/torchmetrics/classification/confusion_matrix.py

@@ -398,6 +398,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTask.from_str(task)
  kwargs.update({"normalize": normalize, "ignore_index": ignore_index, "validate_args": validate_args})
  if task == ClassificationTask.BINARY:

src/torchmetrics/classification/exact_match.py

@@ -118,6 +118,7 @@ def __init__(
  )
 
  def update(self, preds, target) -> None:
+ """Update metric states with predictions and targets."""
  if self.validate_args:
  _multiclass_stat_scores_tensor_validation(
  preds, target, self.num_classes, self.multidim_average, self.ignore_index
@@ -132,6 +133,7 @@ def update(self, preds, target) -> None:
  self.total += total
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct
  return _exact_match_reduce(correct, self.total)
 
@@ -250,6 +252,7 @@ def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
  self.total += total
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  correct = dim_zero_cat(self.correct) if isinstance(self.correct, list) else self.correct
  return _exact_match_reduce(correct, self.total)
 
@@ -289,6 +292,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTaskNoBinary.from_str(task)
  kwargs.update(
  {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}

src/torchmetrics/classification/f_beta.py

@@ -117,6 +117,7 @@ def __init__(
  self.beta = beta
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  tp, fp, tn, fn = self._final_state()
  return _fbeta_reduce(tp, fp, tn, fn, self.beta, average="binary", multidim_average=self.multidim_average)
 
@@ -245,6 +246,7 @@ def __init__(
  self.beta = beta
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  tp, fp, tn, fn = self._final_state()
  return _fbeta_reduce(tp, fp, tn, fn, self.beta, average=self.average, multidim_average=self.multidim_average)
 
@@ -369,6 +371,7 @@ def __init__(
  self.beta = beta
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  tp, fp, tn, fn = self._final_state()
  return _fbeta_reduce(tp, fp, tn, fn, self.beta, average=self.average, multidim_average=self.multidim_average)
 
@@ -726,6 +729,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  assert multidim_average is not None
  kwargs.update(
  {"multidim_average": multidim_average, "ignore_index": ignore_index, "validate_args": validate_args}
@@ -777,6 +781,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTask.from_str(task)
  assert multidim_average is not None
  kwargs.update(

src/torchmetrics/classification/hamming.py

@@ -94,6 +94,7 @@ class BinaryHammingDistance(BinaryStatScores):
  full_state_update: bool = False
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  tp, fp, tn, fn = self._final_state()
  return _hamming_distance_reduce(tp, fp, tn, fn, average="binary", multidim_average=self.multidim_average)
 
@@ -199,6 +200,7 @@ class MulticlassHammingDistance(MulticlassStatScores):
  full_state_update: bool = False
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  tp, fp, tn, fn = self._final_state()
  return _hamming_distance_reduce(tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average)
 
@@ -302,6 +304,7 @@ class MultilabelHammingDistance(MultilabelStatScores):
  full_state_update: bool = False
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  tp, fp, tn, fn = self._final_state()
  return _hamming_distance_reduce(
  tp, fp, tn, fn, average=self.average, multidim_average=self.multidim_average, multilabel=True
@@ -345,6 +348,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTask.from_str(task)
  assert multidim_average is not None
  kwargs.update(

src/torchmetrics/classification/hinge.py

@@ -99,6 +99,7 @@ def __init__(
  self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
 
  def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
+ """Update metric state."""
  if self.validate_args:
  _binary_hinge_loss_tensor_validation(preds, target, self.ignore_index)
  preds, target = _binary_confusion_matrix_format(
@@ -109,6 +110,7 @@ def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
  self.total += total
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  return _hinge_loss_compute(self.measures, self.total)
 
 
@@ -203,6 +205,7 @@ def __init__(
  self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
 
  def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
+ """Update metric state."""
  if self.validate_args:
  _multiclass_hinge_loss_tensor_validation(preds, target, self.num_classes, self.ignore_index)
  preds, target = _multiclass_confusion_matrix_format(preds, target, self.ignore_index, convert_to_labels=False)
@@ -211,6 +214,7 @@ def update(self, preds: Tensor, target: Tensor) -> None: # type: ignore
  self.total += total
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  return _hinge_loss_compute(self.measures, self.total)
 
 
@@ -253,6 +257,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTaskNoMultilabel.from_str(task)
  kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
  if task == ClassificationTaskNoMultilabel.BINARY:

src/torchmetrics/classification/jaccard.py

@@ -89,6 +89,7 @@ def __init__(
  )
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  return _jaccard_index_reduce(self.confmat, average="binary")
 
 
@@ -172,6 +173,7 @@ def __init__(
  self.average = average
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  return _jaccard_index_reduce(self.confmat, average=self.average)
 
 
@@ -259,6 +261,7 @@ def __init__(
  self.average = average
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  return _jaccard_index_reduce(self.confmat, average=self.average)
 
 
@@ -296,6 +299,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTask.from_str(task)
  kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
  if task == ClassificationTask.BINARY:

src/torchmetrics/classification/matthews_corrcoef.py

@@ -80,6 +80,7 @@ def __init__(
  super().__init__(threshold, ignore_index, normalize=None, validate_args=validate_args, **kwargs)
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  return _matthews_corrcoef_reduce(self.confmat)
 
 
@@ -144,6 +145,7 @@ def __init__(
  super().__init__(num_classes, ignore_index, normalize=None, validate_args=validate_args, **kwargs)
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  return _matthews_corrcoef_reduce(self.confmat)
 
 
@@ -207,6 +209,7 @@ def __init__(
  super().__init__(num_labels, threshold, ignore_index, normalize=None, validate_args=validate_args, **kwargs)
 
  def compute(self) -> Tensor:
+ """Compute metric."""
  return _matthews_corrcoef_reduce(self.confmat)
 
 
@@ -238,6 +241,7 @@ def __new__(
  validate_args: bool = True,
  **kwargs: Any,
  ) -> Metric:
+ """Initialize task metric."""
  task = ClassificationTask.from_str(task)
  kwargs.update({"ignore_index": ignore_index, "validate_args": validate_args})
  if task == ClassificationTask.BINARY: