8000 MNT minimally revert strict_mode by glemaitre · Pull Request #18905 · scikit-learn/scikit-learn · GitHub
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MNT minimally revert strict_mode #18905

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merged 3 commits into from
Nov 25, 2020
Merged

MNT minimally revert strict_mode #18905

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8294f22
MNT minimally revert strict_mode
glemaitre Nov 25, 2020
a998242
remove test
glemaitre Nov 25, 2020
26dce48
Update sklearn/utils/estimator_checks.py
glemaitre Nov 25, 2020
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75 changes: 1 addition & 74 deletions sklearn/tests/test_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,25 +15,22 @@
from functools import partial

import pytest
import numpy as np

from sklearn.utils import all_estimators
from sklearn.utils._testing import ignore_warnings
from sklearn.exceptions import ConvergenceWarning, SkipTestWarning
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.estimator_checks import check_estimator

import sklearn
from sklearn.base import BiclusterMixin

from sklearn.decomposition import PCA
from sklearn.decomposition import NMF
from sklearn.linear_model._base import LinearClassifierMixin
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.pipeline import make_pipeline
from sklearn.svm import NuSVC

from sklearn.utils import IS_PYPY
from sklearn.utils._testing import SkipTest
Expand All @@ -45,7 +42,6 @@
parametrize_with_checks,
check_n_features_in_after_fitting,
)
from sklearn.utils.validation import check_non_negative, check_array


def test_all_estimator_no_base_class():
Expand Down Expand Up @@ -217,75 +213,6 @@ def test_class_support_removed():
parametrize_with_checks([LogisticRegression])


class MyNMFWithBadErrorMessage(NMF):
# Same as NMF but raises an uninformative error message if X has negative
# value. This estimator would fail the check suite in strict mode,
# specifically it would fail check_fit_non_negative
# FIXME : should be removed in 0.26
def __init__(self):
super().__init__()
self.init = 'nndsvda'
self.max_iter = 500

def fit(self, X, y=None, **params):
X = check_array(X, accept_sparse=('csr', 'csc'),
dtype=[np.float64, np.float32])
try:
check_non_negative(X, whom='')
except ValueError:
raise ValueError("Some non-informative error msg")

return super().fit(X, y, **params)


def test_strict_mode_check_estimator():
# Tests various conditions for the strict mode of check_estimator()
# Details are in the comments

# LogisticRegression has no _xfail_checks, so when strict_mode is on, there
# should be no skipped tests.
with pytest.warns(None) as catched_warnings:
check_estimator(LogisticRegression(), strict_mode=True)
assert not any(isinstance(w, SkipTestWarning) for w in catched_warnings)
# When strict mode is off, check_n_features should be skipped because it's
# a fully strict check
msg_check_n_features_in = 'check_n_features_in is fully strict '
with pytest.warns(SkipTestWarning, match=msg_check_n_features_in):
check_estimator(LogisticRegression(), strict_mode=False)

# NuSVC has some _xfail_checks. They should be skipped regardless of
# strict_mode
with pytest.warns(SkipTestWarning,
match='fails for the decision_function method'):
check_estimator(NuSVC(), strict_mode=True)
# When strict mode is off, check_n_features_in is skipped along with the
# rest of the xfail_checks
with pytest.warns(SkipTestWarning, match=msg_check_n_features_in):
check_estimator(NuSVC(), strict_mode=False)

# MyNMF will fail check_fit_non_negative() in strict mode because it yields
# a bad error message
with pytest.raises(
AssertionError, match="The error message should contain"
):
check_estimator(MyNMFWithBadErrorMessage(), strict_mode=True)
# However, it should pass the test suite in non-strict mode because when
# strict mode is off, check_fit_non_negative() will not check the exact
# error messsage. (We still assert that the warning from
# check_n_features_in is raised)
with pytest.warns(SkipTestWarning, match=msg_check_n_features_in):
check_estimator(MyNMFWithBadErrorMessage(), strict_mode=False)


@parametrize_with_checks([LogisticRegression(),
NuSVC(),
MyNMFWithBadErrorMessage()],
strict_mode=False)
def test_strict_mode_parametrize_with_checks(estimator, check):
# Ideally we should assert that the strict checks are Xfailed...
check(estimator)


def _generate_search_cv_instances():
for SearchCV, (Estimator, param_grid) in zip(
[GridSearchCV, RandomizedSearchCV],
Expand Down
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