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# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import pytest
from sklearn import metrics
from sklearn.ensemble import (
BaggingClassifier,
BaggingRegressor,
IsolationForest,
StackingClassifier,
StackingRegressor,
)
from sklearn.utils._testing import assert_docstring_consistency, skip_if_no_numpydoc
CLASS_DOCSTRING_CONSISTENCY_CASES = [
{
"objects": [BaggingClassifier, BaggingRegressor, IsolationForest],
"include_params": ["max_samples"],
"exclude_params": None,
"include_attrs": False,
"exclude_attrs": None,
"include_returns": False,
"exclude_returns": None,
"descr_regex_pattern": r"The number of samples to draw from X to train each.*",
"ignore_types": ("max_samples"),
},
{
"objects": [StackingClassifier, StackingRegressor],
"include_params": ["cv", "n_jobs", "passthrough", "verbose"],
"exclude_params": None,
"include_attrs": True,
"exclude_attrs": ["final_estimator_"],
"include_returns": False,
"exclude_returns": None,
"descr_regex_pattern": None,
},
]
FUNCTION_DOCSTRING_CONSISTENCY_CASES = [
{
"objects": [
metrics.precision_recall_fscore_support,
metrics.f1_score,
metrics.fbeta_score,
metrics.precision_score,
metrics.recall_score,
],
"include_params": True,
"exclude_params": ["average", "zero_division"],
"include_attrs": False,
"exclude_attrs": None,
"include_returns": False,
"exclude_returns": None,
"descr_regex_pattern": None,
},
{
"objects": [
metrics.precision_recall_fscore_support,
metrics.f1_score,
metrics.fbeta_score,
metrics.precision_score,
metrics.recall_score,
],
"include_params": ["average"],
"exclude_params": None,
"include_attrs": False,
"exclude_attrs": None,
"include_returns": False,
"exclude_returns": None,
"descr_regex_pattern": " ".join(
(
r"""This parameter is required for multiclass/multilabel targets\.
If ``None``, the metrics for each class are returned\. Otherwise, this
determines the type of averaging performed on the data:
``'binary'``:
Only report results for the class specified by ``pos_label``\.
This is applicable only if targets \(``y_\{true,pred\}``\) are binary\.
``'micro'``:
Calculate metrics globally by counting the total true positives,
false negatives and false positives\.
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean\. This does not take label imbalance into account\.
``'weighted'``:
Calculate metrics for each label, and find their average weighted
by support \(the number of true instances for each label\)\. This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall\."""
r"[\s\w]*\.*" # optionally match additional sentence
r"""
``'samples'``:
Calculate metrics for each instance, and find their average \(only
meaningful for multilabel classification where this differs from
:func:`accuracy_score`\)\."""
).split()
),
},
]
@pytest.mark.parametrize("case", CLASS_DOCSTRING_CONSISTENCY_CASES)
@skip_if_no_numpydoc
def test_class_docstring_consistency(case):
"""Check docstrings parameters consistency between related classes."""
assert_docstring_consistency(**case)
@pytest.mark.parametrize("case", FUNCTION_DOCSTRING_CONSISTENCY_CASES)
@skip_if_no_numpydoc
def test_function_docstring_consistency(case):
"""Check docstrings parameters consistency between related functions."""
assert_docstring_consistency(**case)
|
