docs: rm type hints in ignite.contrib.metrics (6)

ignite/contrib/metrics/average_precision.py

@@ -22,11 +22,11 @@ class AveragePrecision(EpochMetric):
     sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score>`_ .
 
     Args:
-        output_transform (callable, optional): a callable that is used to transform the
+        output_transform: a callable that is used to transform the
             :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
-        check_compute_fn (bool): Default False. If True, `average_precision_score
+        check_compute_fn: Default False. If True, `average_precision_score
             <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html
             #sklearn.metrics.average_precision_score>`_ is run on the first batch of data to ensure there are
             no issues. User will be warned in case there are any issues computing the function.

ignite/contrib/metrics/precision_recall_curve.py

@@ -23,11 +23,11 @@ class PrecisionRecallCurve(EpochMetric):
     sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve>`_ .
 
     Args:
-        output_transform (callable, optional): a callable that is used to transform the
+        output_transform: a callable that is used to transform the
             :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
-        check_compute_fn (bool): Default False. If True, `precision_recall_curve
+        check_compute_fn: Default False. If True, `precision_recall_curve
             <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html
             #sklearn.metrics.precision_recall_curve>`_ is run on the first batch of data to ensure there are
             no issues. User will be warned in case there are any issues computing the function.

ignite/contrib/metrics/regression/canberra_metric.py

@@ -22,17 +22,24 @@ class CanberraMetric(_BaseRegression):
     .. _scikit-learn distance metrics:
         https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html
 
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
+
     .. versionchanged:: 0.4.3
 
         - Fixed implementation: ``abs`` in denominator.
         - Works with DDP.
     """
 
-    def __init__(
-        self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu")
-    ) -> None:
-        super(CanberraMetric, self).__init__(output_transform, device)
-
     @reinit__is_reduced
     def reset(self) -> None:
         self._sum_of_errors = torch.tensor(0.0, device=self._device)

ignite/contrib/metrics/regression/fractional_absolute_error.py

@@ -20,6 +20,18 @@ class FractionalAbsoluteError(_BaseRegression):
     - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
 
     __ https://arxiv.org/abs/1809.03006
+
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
     """
 
     def reset(self) -> None:

ignite/contrib/metrics/regression/fractional_bias.py

@@ -21,6 +21,17 @@ class FractionalBias(_BaseRegression):
 
     __ https://arxiv.org/abs/1809.03006
 
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
     """
 
     def reset(self) -> None:

ignite/contrib/metrics/regression/geometric_mean_absolute_error.py

@@ -20,6 +20,18 @@ class GeometricMeanAbsoluteError(_BaseRegression):
     - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
 
     __ https://arxiv.org/abs/1809.03006
+
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
     """
 
     def reset(self) -> None:

ignite/contrib/metrics/regression/geometric_mean_relative_absolute_error.py

@@ -19,9 +19,19 @@ class GeometricMeanRelativeAbsoluteError(_BaseRegression):
     - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
     - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
 
-
     __ https://arxiv.org/abs/1809.03006
 
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
     """
 
     def reset(self) -> None:

ignite/contrib/metrics/regression/manhattan_distance.py

@@ -21,17 +21,24 @@ class ManhattanDistance(_BaseRegression):
 
     __ https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.DistanceMetric.html
 
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
+
     .. versionchanged:: 0.4.3
 
         - Fixed sklearn compatibility.
         - Workes with DDP.
     """
 
-    def __init__(
-        self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu")
-    ):
-        super(ManhattanDistance, self).__init__(output_transform, device)
-
     @reinit__is_reduced
     def reset(self) -> None:
         self._sum_of_errors = torch.tensor(0.0, device=self._device)

ignite/contrib/metrics/regression/maximum_absolute_error.py

@@ -21,6 +21,17 @@ class MaximumAbsoluteError(_BaseRegression):
 
     __ https://arxiv.org/abs/1809.03006
 
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
     """
 
     def reset(self) -> None:

ignite/contrib/metrics/regression/mean_absolute_relative_error.py

@@ -21,6 +21,17 @@ class MeanAbsoluteRelativeError(_BaseRegression):
 
     __ https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf
 
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
     """
 
     def reset(self) -> None:

ignite/contrib/metrics/regression/mean_error.py

@@ -21,6 +21,17 @@ class MeanError(_BaseRegression):
 
     __ https://arxiv.org/abs/1809.03006
 
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
     """
 
     def reset(self) -> None:

ignite/contrib/metrics/regression/mean_normalized_bias.py

@@ -21,6 +21,17 @@ class MeanNormalizedBias(_BaseRegression):
 
     __ https://arxiv.org/abs/1809.03006
 
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
     """
 
     def reset(self) -> None:

ignite/contrib/metrics/regression/median_absolute_error.py

@@ -31,6 +31,12 @@ class MedianAbsoluteError(_BaseRegressionEpoch):
 
     __ https://arxiv.org/abs/1809.03006
 
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
     """
 
     def __init__(self, output_transform: Callable = lambda x: x):

ignite/contrib/metrics/regression/median_absolute_percentage_error.py

@@ -31,6 +31,12 @@ class MedianAbsolutePercentageError(_BaseRegressionEpoch):
 
     __ https://arxiv.org/abs/1809.03006
 
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
     """
 
     def __init__(self, output_transform: Callable = lambda x: x):

ignite/contrib/metrics/regression/median_relative_absolute_error.py

@@ -31,6 +31,12 @@ class MedianRelativeAbsoluteError(_BaseRegressionEpoch):
 
     __ https://arxiv.org/abs/1809.03006
 
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
     """
 
     def __init__(self, output_transform: Callable = lambda x: x):

ignite/contrib/metrics/regression/r2_score.py

@@ -20,15 +20,22 @@ class R2Score(_BaseRegression):
     - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
     - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`.
 
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
+
     .. versionchanged:: 0.4.3
         Works with DDP.
     """
 
-    def __init__(
-        self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu"),
-    ):
-        super(R2Score, self).__init__(output_transform, device)
-
     @reinit__is_reduced
     def reset(self) -> None:
         self._num_examples = 0

ignite/contrib/metrics/regression/wave_hedges_distance.py

@@ -19,6 +19,18 @@ class WaveHedgesDistance(_BaseRegression):
     - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
 
     __ https://arxiv.org/abs/1809.03006
+
+    Parameters are inherited from ``Metric.__init__``.
+
+    Args:
+        output_transform: a callable that is used to transform the
+            :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
+            form expected by the metric. This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
+        device: specifies which device updates are accumulated on. Setting the
+            metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
+            non-blocking. By default, CPU.
     """
 
     def reset(self) -> None:

ignite/contrib/metrics/roc_auc.py

@@ -34,11 +34,11 @@ class ROC_AUC(EpochMetric):
     sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score>`_ .
 
     Args:
-        output_transform (callable, optional): a callable that is used to transform the
+        output_transform: a callable that is used to transform the
             :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
-        check_compute_fn (bool): Default False. If True, `roc_curve
+        check_compute_fn: Default False. If True, `roc_curve
             <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#
             sklearn.metrics.roc_auc_score>`_ is run on the first batch of data to ensure there are
             no issues. User will be warned in case there are any issues computing the function.
@@ -70,11 +70,11 @@ class RocCurve(EpochMetric):
     sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve>`_ .
 
     Args:
-        output_transform (callable, optional): a callable that is used to transform the
+        output_transform: a callable that is used to transform the
             :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
             form expected by the metric. This can be useful if, for example, you have a multi-output model and
             you want to compute the metric with respect to one of the outputs.
-        check_compute_fn (bool): Default False. If True, `sklearn.metrics.roc_curve
+        check_compute_fn: Default False. If True, `sklearn.metrics.roc_curve
             <http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#
             sklearn.metrics.roc_curve>`_ is run on the first batch of data to ensure there are
             no issues. User will be warned in case there are any issues computing the function.