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MAINT Parameters validation for sklearn.metrics.average_precision_score #25313

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MAINT Parameters validation for sklearn.metrics.average_precision_score #25313

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0b6834d
param validation for multilabel_confusion_matrix
dlitsidis Dec 29, 2022
74bf0bd
Update sklearn/tests/test_public_functions.py
jeremiedbb Dec 29, 2022
482434b
param validation for det_curve
dlitsidis Dec 30, 2022
25ed408
Merge branch 'main' into <param-valid>
dlitsidis Dec 30, 2022
bd411e9
update for det_curve
dlitsidis Dec 30, 2022
0009fd9
Merge remote-tracking branch 'origin/<param-valid>' into <param-valid>
dlitsidis Dec 30, 2022
6361a85
Update _ranking.py
jeremiedbb Dec 31, 2022
a5d94d2
param validation for fetch_california_housing
dlitsidis Jan 1, 2023
2812f5c
Merge branch 'main' into <param-valid>
jeremiedbb Jan 2, 2023
564cdae
unrelated
jeremiedbb Jan 2, 2023
45693f3
Merge branch 'main' into <param-valid>
jeremiedbb Jan 2, 2023
4985c30
param validation for average_precision_score
dlitsidis Jan 6, 2023
e400c6e
update for average_precision_score
dlitsidis Jan 6, 2023
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9 changes: 9 additions & 0 deletions sklearn/datasets/_california_housing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,7 @@
from ._base import RemoteFileMetadata
from ._base import load_descr
from ..utils import Bunch
from ..utils._param_validation import validate_params


# The original data can be found at:
Expand All @@ -50,6 +51,14 @@
logger = logging.getLogger(__name__)


@validate_params(
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I think that this changes should be done in another PR. Could you revert this file.

{
"data_home": [str, None],
"download_if_missing": ["boolean"],
"return_X_y": ["boolean"],
"as_frame": ["boolean"],
}
)
def fetch_california_housing(
*, data_home=None, download_if_missing=True, return_X_y=False, as_frame=False
):
Expand Down
15 changes: 12 additions & 3 deletions sklearn/metrics/_ranking.py
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Expand Up @@ -34,7 +34,7 @@
from ..utils.multiclass import type_of_target
from ..utils.extmath import stable_cumsum
from ..utils.sparsefuncs import count_nonzero
from ..utils._param_validation import validate_params
from ..utils._param_validation import validate_params, StrOptions
from ..exceptions import UndefinedMetricWarning
from ..preprocessing import label_binarize
from ..utils._encode import _encode, _unique
Expand Down Expand Up @@ -112,6 +112,15 @@ def auc(x, y):
return area


@validate_params(
{
"y_true": ["array-like"],
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It is true that we are going to call column_or_1d that will transform any array-like into a NumPy array. Could you update the docstring of y_true and y_score to change ndarray by array-like

"y_score": ["array-like"],
"average": [StrOptions({"micro", "samples", "weighted", "macro"}), None],
"pos_label": [Real, str, "boolean"],
"sample_weight": ["array-like", None],
}
)
def average_precision_score(
y_true, y_score, *, average="macro", pos_label=1, sample_weight=None
):
Expand All @@ -137,10 +146,10 @@ def average_precision_score(

Parameters
----------
y_true : ndarray of shape (n_samples,) or (n_samples, n_classes)
y_true : array-like of shape (n_samples,) or (n_samples, n_classes)
True binary labels or binary label indicators.

y_score : ndarray of shape (n_samples,) or (n_samples, n_classes)
y_score : array-like of shape (n_samples,) or (n_samples, n_classes)
Target scores, can either be probability estimates of the positive
class, confidence values, or non-thresholded measure of decisions
(as returned by :term:`decision_function` on some classifiers).
Expand Down
2 changes: 2 additions & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -100,13 +100,15 @@ def _check_function_param_validation(
"sklearn.cluster.kmeans_plusplus",
"sklearn.covariance.empirical_covariance",
"sklearn.covariance.shrunk_covariance",
"sklearn.datasets.fetch_california_housing",
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Can you revert this change.

"sklearn.datasets.make_sparse_coded_signal",
"sklearn.decomposition.sparse_encode",
"sklearn.feature_extraction.grid_to_graph",
"sklearn.feature_extraction.img_to_graph",
"sklearn.feature_extraction.image.extract_patches_2d",
"sklearn.metrics.accuracy_score",
"sklearn.metrics.auc",
"sklearn.metrics.average_precision_score",
"sklearn.metrics.cohen_kappa_score",
"sklearn.metrics.confusion_matrix",
"sklearn.metrics.det_curve",
Expand Down