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MAINT Parameters validation for sklearn.metrics.f1_score #25557

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Merged
merged 9 commits into from
Feb 8, 2023
Merged

MAINT Parameters validation for sklearn.metrics.f1_score #25557

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e1e64a6
added validate_params to f1_score
Feb 7, 2023
12ff012
added validate_params to f1_score
Feb 7, 2023
f9e5604
added validate_params to f1_score
Feb 7, 2023
005eb7a
Merge branch 'main' into f1_score_vp
nwzish Feb 7, 2023
d445071
Merge branch 'main' into f1_score_vp
nwzish Feb 8, 2023
4a1f9eb
Merge branch 'main' into f1_score_vp
jeremiedbb Feb 8, 2023
456a637
Update _classification.py
jeremiedbb Feb 8, 2023
a1019cb
Update _classification.py
jeremiedbb Feb 8, 2023
4257a63
Update _classification.py
jeremiedbb Feb 8, 2023
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22 changes: 20 additions & 2 deletions sklearn/metrics/_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
# License: BSD 3 clause


from numbers import Integral, Real
import warnings
import numpy as np

Expand All @@ -40,7 +41,7 @@
from ..utils.multiclass import type_of_target
from ..utils.validation import _num_samples
from ..utils.sparsefuncs import count_nonzero
from ..utils._param_validation import StrOptions, validate_params
from ..utils._param_validation import StrOptions, Options, validate_params
from ..exceptions import UndefinedMetricWarning

from ._base import _check_pos_label_consistency
Expand Down Expand Up @@ -1038,6 +1039,23 @@ def zero_one_loss(y_true, y_pred, *, normalize=True, sample_weight=None):
return n_samples - score


@validate_params(
{
"y_true": ["array-like", "sparse matrix"],
"y_pred": ["array-like", "sparse matrix"],
"labels": ["array-like", None],
"pos_label": [Real, str, "boolean", None],
"average": [
StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
None,
],
"sample_weight": ["array-like", None],
"zero_division": [
Options(Integral, {0, 1}),
StrOptions({"warn"}),
],
}
)
def f1_score(
y_true,
y_pred,
Expand Down Expand Up @@ -1083,7 +1101,7 @@ def f1_score(
.. versionchanged:: 0.17
Parameter `labels` improved for multiclass problem.

pos_label : int, float, bool or str, default=1
pos_label : int, float, bool, str or None, default=1
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
Expand Down
1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Expand Up @@ -120,6 +120,7 @@ def _check_function_param_validation(
"sklearn.metrics.confusion_matrix",
"sklearn.metrics.d2_pinball_score",
"sklearn.metrics.det_curve",
"sklearn.metrics.f1_score",
"sklearn.metrics.hamming_loss",
"sklearn.metrics.mean_absolute_error",
"sklearn.metrics.mean_squared_error",
Expand Down