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DOC Consistent param type for pos_label #26237

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Apr 25, 2023
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DOC Consistent param type for pos_label #26237

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8 changes: 4 additions & 4 deletions sklearn/calibration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -928,7 +928,7 @@ def calibration_curve(
y_prob : array-like of shape (n_samples,)
Probabilities of the positive class.

pos_label : int or str, default=None
pos_label : int, float, bool or str, default=None
The label of the positive class.

.. versionadded:: 1.1
Expand Down Expand Up @@ -1042,7 +1042,7 @@ class CalibrationDisplay(_BinaryClassifierCurveDisplayMixin):
estimator_name : str, default=None
Name of estimator. If None, the estimator name is not shown.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The positive class when computing the calibration curve.
By default, `estimators.classes_[1]` is considered as the
positive class.
Expand Down Expand Up @@ -1208,7 +1208,7 @@ def from_estimator(
- `'quantile'`: The bins have the same number of samples and depend
on predicted probabilities.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The positive class when computing the calibration curve.
By default, `estimators.classes_[1]` is considered as the
positive class.
Expand Down Expand Up @@ -1326,7 +1326,7 @@ def from_predictions(
- `'quantile'`: The bins have the same number of samples and depend
on predicted probabilities.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The positive class when computing the calibration curve.
By default, `estimators.classes_[1]` is considered as the
positive class.
Expand Down
6 changes: 3 additions & 3 deletions sklearn/metrics/_plot/det_curve.py
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Expand Up @@ -26,7 +26,7 @@ class DetCurveDisplay(_BinaryClassifierCurveDisplayMixin):
estimator_name : str, default=None
Name of estimator. If None, the estimator name is not shown.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The label of the positive class.

Attributes
Expand Down Expand Up @@ -117,7 +117,7 @@ def from_estimator(
to 'auto', :term:`predict_proba` is tried first and if it does not
exist :term:`decision_function` is tried next.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The label of the positive class. When `pos_label=None`, if `y_true`
is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an
error will be raised.
Expand Down Expand Up @@ -210,7 +210,7 @@ def from_predictions(
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The label of the positive class. When `pos_label=None`, if `y_true`
is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an
error will be raised.
Expand Down
6 changes: 3 additions & 3 deletions sklearn/metrics/_plot/precision_recall_curve.py
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Expand Up @@ -28,7 +28,7 @@ class PrecisionRecallDisplay(_BinaryClassifierCurveDisplayMixin):
estimator_name : str, default=None
Name of estimator. If None, then the estimator name is not shown.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The class considered as the positive class. If None, the class will not
be shown in the legend.

Expand Down Expand Up @@ -194,7 +194,7 @@ def from_estimator(
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The class considered as the positive class when computing the
precision and recall metrics. By default, `estimators.classes_[1]`
is considered as the positive class.
Expand Down Expand Up @@ -306,7 +306,7 @@ def from_predictions(
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The class considered as the positive class when computing the
precision and recall metrics.

Expand Down
6 changes: 3 additions & 3 deletions sklearn/metrics/_plot/roc_curve.py
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Expand Up @@ -28,7 +28,7 @@ class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin):
estimator_name : str, default=None
Name of estimator. If None, the estimator name is not shown.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The class considered as the positive class when computing the roc auc
metrics. By default, `estimators.classes_[1]` is considered
as the positive class.
Expand Down Expand Up @@ -213,7 +213,7 @@ def from_estimator(
:term:`predict_proba` is tried first and if it does not exist
:term:`decision_function` is tried next.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The class considered as the positive class when computing the roc auc
metrics. By default, `estimators.classes_[1]` is considered
as the positive class.
Expand Down Expand Up @@ -328,7 +328,7 @@ def from_predictions(
on a plotted ROC curve. This is useful in order to create lighter
ROC curves.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The label of the positive class. When `pos_label=None`, if `y_true`
is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an
error will be raised.
Expand Down
12 changes: 6 additions & 6 deletions sklearn/utils/_response.py
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Expand Up @@ -47,7 +47,7 @@ def _get_response_values(
preference. The method returned corresponds to the first method in
the list and which is implemented by `estimator`.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The class considered as the positive class when computing
the metrics. By default, `estimators.classes_[1]` is
considered as the positive class.
Expand All @@ -58,7 +58,7 @@ def _get_response_values(
Target scores calculated from the provided response_method
and `pos_label`.

pos_label : str, int or None
pos_label : int, float, bool, str or None
The class considered as the positive class when computing
the metrics. Returns `None` if `estimator` is a regressor.

Expand Down Expand Up @@ -133,24 +133,24 @@ def _get_response_values_binary(estimator, X, response_method, pos_label=None):
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input values.

response_method: {'auto', 'predict_proba', 'decision_function'}
response_method : {'auto', 'predict_proba', 'decision_function'}
Specifies whether to use :term:`predict_proba` or
:term:`decision_function` as the target response. If set to 'auto',
:term:`predict_proba` is tried first and if it does not exist
:term:`decision_function` is tried next.

pos_label : str or int, default=None
pos_label : int, float, bool or str, default=None
The class considered as the positive class when computing
the metrics. By default, `estimators.classes_[1]` is
considered as the positive class.

Returns
-------
y_pred: ndarray of shape (n_samples,)
y_pred : ndarray of shape (n_samples,)
Target scores calculated from the provided response_method
and pos_label.

pos_label: str or int
pos_label : int, float, bool or str
The class considered as the positive class when computing
the metrics.
"""
Expand Down
4 changes: 2 additions & 2 deletions sklearn/utils/validation.py
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Expand Up @@ -2162,14 +2162,14 @@ def _check_pos_label_consistency(pos_label, y_true):

Parameters
----------
pos_label : int, str or None
pos_label : int, float, bool, str or None
The positive label.
y_true : ndarray of shape (n_samples,)
The target vector.

Returns
-------
pos_label : int
pos_label : int, float, bool or str
If `pos_label` can be inferred, it will be returned.

Raises
Expand Down