Aluode/PerceptionLabPortable

1# Authors: The scikit-learn developers
2# SPDX-License-Identifier: BSD-3-Clause
3
4import pytest
5
6from sklearn import metrics
7from sklearn.ensemble import (
8    BaggingClassifier,
9    BaggingRegressor,
10    IsolationForest,
11    StackingClassifier,
12    StackingRegressor,
13)
14from sklearn.utils._testing import assert_docstring_consistency, skip_if_no_numpydoc
15
16CLASS_DOCSTRING_CONSISTENCY_CASES = [
17    {
18        "objects": [BaggingClassifier, BaggingRegressor, IsolationForest],
19        "include_params": ["max_samples"],
20        "exclude_params": None,
21        "include_attrs": False,
22        "exclude_attrs": None,
23        "include_returns": False,
24        "exclude_returns": None,
25        "descr_regex_pattern": r"The number of samples to draw from X to train each.*",
26        "ignore_types": ("max_samples"),
27    },
28    {
29        "objects": [StackingClassifier, StackingRegressor],
30        "include_params": ["cv", "n_jobs", "passthrough", "verbose"],
31        "exclude_params": None,
32        "include_attrs": True,
33        "exclude_attrs": ["final_estimator_"],
34        "include_returns": False,
35        "exclude_returns": None,
36        "descr_regex_pattern": None,
37    },
38]
39
40FUNCTION_DOCSTRING_CONSISTENCY_CASES = [
41    {
42        "objects": [
43            metrics.precision_recall_fscore_support,
44            metrics.f1_score,
45            metrics.fbeta_score,
46            metrics.precision_score,
47            metrics.recall_score,
48        ],
49        "include_params": True,
50        "exclude_params": ["average", "zero_division"],
51        "include_attrs": False,
52        "exclude_attrs": None,
53        "include_returns": False,
54        "exclude_returns": None,
55        "descr_regex_pattern": None,
56    },
57    {
58        "objects": [
59            metrics.precision_recall_fscore_support,
60            metrics.f1_score,
61            metrics.fbeta_score,
62            metrics.precision_score,
63            metrics.recall_score,
64        ],
65        "include_params": ["average"],
66        "exclude_params": None,
67        "include_attrs": False,
68        "exclude_attrs": None,
69        "include_returns": False,
70        "exclude_returns": None,
71        "descr_regex_pattern": " ".join(
72            (
73                r"""This parameter is required for multiclass/multilabel targets\.
74            If ``None``, the metrics for each class are returned\. Otherwise, this
75            determines the type of averaging performed on the data:
76            ``'binary'``:
77                Only report results for the class specified by ``pos_label``\.
78                This is applicable only if targets \(``y_\{true,pred\}``\) are binary\.
79            ``'micro'``:
80                Calculate metrics globally by counting the total true positives,
81                false negatives and false positives\.
82            ``'macro'``:
83                Calculate metrics for each label, and find their unweighted
84                mean\.  This does not take label imbalance into account\.
85            ``'weighted'``:
86                Calculate metrics for each label, and find their average weighted
87                by support \(the number of true instances for each label\)\. This
88                alters 'macro' to account for label imbalance; it can result in an
89                F-score that is not between precision and recall\."""
90                r"[\s\w]*\.*"  # optionally match additional sentence
91                r"""
92            ``'samples'``:
93                Calculate metrics for each instance, and find their average \(only
94                meaningful for multilabel classification where this differs from
95                :func:`accuracy_score`\)\."""
96            ).split()
97        ),
98    },
99]
100
101
102@pytest.mark.parametrize("case", CLASS_DOCSTRING_CONSISTENCY_CASES)
103@skip_if_no_numpydoc
104def test_class_docstring_consistency(case):
105    """Check docstrings parameters consistency between related classes."""
106    assert_docstring_consistency(**case)
107
108
109@pytest.mark.parametrize("case", FUNCTION_DOCSTRING_CONSISTENCY_CASES)
110@skip_if_no_numpydoc
111def test_function_docstring_consistency(case):
112    """Check docstrings parameters consistency between related functions."""
113    assert_docstring_consistency(**case)
114