8000 MAINT Parameters validation for metrics.precision_recall_curve() by sortofamudkip · Pull Request #25698 · scikit-learn/scikit-learn · GitHub
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MAINT Parameters validation for metrics.precision_recall_curve() #25698

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Merged
merged 2 commits into from
Feb 27, 2023
Merged

MAINT Parameters validation for metrics.precision_recall_curve() #25698

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31c9255
added parameter validation testing metrics.precision_recall_curve in …
Feb 24, 2023
c0a1c20
fix docstring
jeremiedbb Feb 27, 2023
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12 changes: 10 additions & 2 deletions sklearn/metrics/_ranking.py
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Original file line number Diff line number Diff line change
Expand Up @@ -814,6 +814,14 @@ def _binary_clf_curve(y_true, y_score, pos_label=None, sample_weight=None):
return fps, tps, y_score[threshold_idxs]


@validate_params(
{
"y_true": ["array-like"],
"probas_pred": ["array-like"],
"pos_label": [Real, str, "boolean", None],
"sample_weight": ["array-like", None],
}
)
def precision_recall_curve(y_true, probas_pred, *, pos_label=None, sample_weight=None):
"""Compute precision-recall pairs for different probability thresholds.

Expand All @@ -839,11 +847,11 @@ def precision_recall_curve(y_true, probas_pred, *, pos_label=None, sample_weight

Parameters
----------
y_true : ndarray of shape (n_samples,)
y_true : array-like of shape (n_samples,)
True binary labels. If labels are not either {-1, 1} or {0, 1}, then
pos_label should be explicitly given.

probas_pred : ndarray of shape (n_samples,)
probas_pred : array-like of shape (n_samples,)
Target scores, can either be probability estimates of the positive
class, or non-thresholded measure of decisions (as returned by
`decision_function` on some classifiers).
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1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -134,6 +134,7 @@ def _check_function_param_validation(
"sklearn.metrics.multilabel_confusion_matrix",
"sklearn.metrics.mutual_info_score",
"sklearn.metrics.pairwise.additive_chi2_kernel",
"sklearn.metrics.precision_recall_curve",
"sklearn.metrics.precision_recall_fscore_support",
"sklearn.metrics.r2_score",
"sklearn.metrics.roc_curve",
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0