Thanks to visit codestin.com
Credit goes to github.com

Skip to content

MAINT Parameters validation for precision_recall_fscore_support #25681

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 6 commits into from
Feb 24, 2023
Merged

MAINT Parameters validation for precision_recall_fscore_support #25681

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
806a5f1
add param validation for precision_recall_fscore_support
Feb 23, 2023
ba223c4
Merge branch 'scikit-learn:main' into validate_params_precision_recal…
JuliaSchoepp Feb 23, 2023
5ee580d
do reformatting
Feb 23, 2023
d7eca9e
Merge branch 'validate_params_precision_recall_fscore_support' of git…
Feb 23, 2023
2f3d208
missing line break
jeremiedbb Feb 24, 2023
cd01d8c
add missing constraint
jeremiedbb Feb 24, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
23 changes: 20 additions & 3 deletions sklearn/metrics/_classification.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1451,6 +1451,25 @@ def _check_set_wise_labels(y_true, y_pred, average, labels, pos_label):
return labels


@validate_params(
{
"y_true": ["array-like", "sparse matrix"],
"y_pred": ["array-like", "sparse matrix"],
"beta": [Interval(Real, 0.0, None, closed="both")],
"labels": ["array-like", None],
"pos_label": [Real, str, "boolean", None],
"average": [
StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
None,
],
"warn_for": [list, tuple, set],
"sample_weight": ["array-like", None],
"zero_division": [
Options(Real, {0, 1}),
StrOptions({"warn"}),
],
}
)
def precision_recall_fscore_support(
y_true,
y_pred,
Expand Down Expand Up @@ -1539,7 +1558,7 @@ def precision_recall_fscore_support(
meaningful for multilabel classification where this differs from
:func:`accuracy_score`).

warn_for : tuple or set, for internal use
warn_for : list, tuple or set, for internal use
This determines which warnings will be made in the case that this
function is being used to return only one of its metrics.

Expand Down Expand Up @@ -1616,8 +1635,6 @@ def precision_recall_fscore_support(
array([2, 2, 2]))
"""
_check_zero_division(zero_division)
if beta < 0:
raise ValueError("beta should be >=0 in the F-beta score")
labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)

# Calculate tp_sum, pred_sum, true_sum ###
Expand Down
17 changes: 0 additions & 17 deletions sklearn/metrics/tests/test_classification.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -386,23 +386,6 @@ def test_average_precision_score_tied_values():
assert average_precision_score(y_true, y_score) != 1.0


@ignore_warnings
def test_precision_recall_fscore_support_errors():
y_true, y_pred, _ = make_prediction(binary=True)

# Bad beta
with pytest.raises(ValueError):
precision_recall_fscore_support(y_true, y_pred, beta=-0.1)

# Bad pos_label
with pytest.raises(ValueError):
precision_recall_fscore_support(y_true, y_pred, pos_label=2, average="binary")

# Bad average option
with pytest.raises(ValueError):
precision_recall_fscore_support([0, 1, 2], [1, 2, 0], average="mega")


def test_precision_recall_f_unused_pos_label():
# Check warning that pos_label unused when set to non-default value
# but average != 'binary'; even if data is binary.
Expand Down
1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -130,6 +130,7 @@ 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_fscore_support",
"sklearn.metrics.r2_score",
"sklearn.metrics.roc_curve",
"sklearn.metrics.zero_one_loss",
Expand Down