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TST Changes assert_raises to raises in sklearn/utils/test_estimator_checks.py #20138

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Merged
merged 14 commits into from
May 27, 2021
Merged

TST Changes assert_raises to raises in sklearn/utils/test_estimator_checks.py #20138

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c62106c
Add permutation imp and change dataset to digits
Feb 10, 2021
0dca5ac
Fix flake8 errors
Feb 10, 2021
7305359
Remove tight layout from plots
Feb 11, 2021
8e156a4
Reintroduce faces dataset and add the MDI usage warning
Feb 23, 2021
16d3444
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
Feb 23, 2021
71e544c
Fix bullet points and the reference
Feb 24, 2021
b68e600
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
Apr 12, 2021
2762e0a
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
Apr 19, 2021
b4cdcc1
finalize merge
Apr 26, 2021
6be3ed2
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
Apr 28, 2021
251d038
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
May 17, 2021
20937af
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
May 22, 2021
861ef68
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
May 26, 2021
a6d6f5c
assert_raises to raises
May 26, 2021
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154 changes: 77 additions & 77 deletions sklearn/utils/tests/test_estimator_checks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,8 +12,7 @@
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils import deprecated
from sklearn.utils._testing import (
assert_raises,
assert_raises_regex,
raises,
assert_warns,
ignore_warnings,
MinimalClassifier,
Expand Down Expand Up @@ -413,7 +412,8 @@ def test_not_an_array_array_function():
raise SkipTest("array_function protocol not supported in numpy <1.17")
not_array = _NotAnArray(np.ones(10))
msg = "Don't want to call array_function sum!"
assert_raises_regex(TypeError, msg, np.sum, not_array)
with raises(TypeError, match=msg):
np.sum(not_array)
# always returns True
assert np.may_share_memory(not_array, None)

Expand All @@ -437,92 +437,93 @@ def test_check_estimator():

# check that we have a set_params and can clone
msg = "Passing a class was deprecated"
assert_raises_regex(TypeError, msg, check_estimator, object)
with raises(TypeError, match=msg):
check_estimator(object)
msg = (
"Parameter 'p' of estimator 'HasMutableParameters' is of type "
"object which is not allowed"
)
# check that the "default_constructible" test checks for mutable parameters
check_estimator(HasImmutableParameters()) # should pass
assert_raises_regex(
AssertionError, msg, check_estimator, HasMutableParameters()
)
with raises(AssertionError, match=msg):
check_estimator(HasMutableParameters())
# check that values returned by get_params match set_params
msg = "get_params result does not match what was passed to set_params"
assert_raises_regex(AssertionError, msg, check_estimator,
ModifiesValueInsteadOfRaisingError())
with raises(AssertionError, match=msg):
check_estimator(ModifiesValueInsteadOfRaisingError())
assert_warns(UserWarning, check_estimator, RaisesErrorInSetParams())
assert_raises_regex(AssertionError, msg, check_estimator,
ModifiesAnotherValue())
with raises(AssertionError, match=msg):
check_estimator(ModifiesAnotherValue())
# check that we have a fit method
msg = "object has no attribute 'fit'"
assert_raises_regex(AttributeError, msg, check_estimator, BaseEstimator())
with raises(AttributeError, match=msg):
check_estimator(BaseEstimator())
# check that fit does input validation
msg = "Did not raise"
assert_raises_regex(AssertionError, msg, check_estimator,
BaseBadClassifier())
with raises(AssertionError, match=msg):
check_estimator(BaseBadClassifier())
# check that sample_weights in fit accepts pandas.Series type
try:
from pandas import Series # noqa
msg = ("Estimator NoSampleWeightPandasSeriesType raises error if "
"'sample_weight' parameter is of type pandas.Series")
assert_raises_regex(
ValueError, msg, check_estimator, NoSampleWeightPandasSeriesType())
with raises(ValueError, match=msg):
check_estimator(NoSampleWeightPandasSeriesType())
except ImportError:
pass
# check that predict does input validation (doesn't accept dicts in input)
msg = "Estimator doesn't check for NaN and inf in predict"
assert_raises_regex(AssertionError, msg, check_estimator,
NoCheckinPredict())
with raises(AssertionError, match=msg):
check_estimator(NoCheckinPredict())
# check that estimator state does not change
# at transform/predict/predict_proba time
msg = 'Estimator changes __dict__ during predict'
assert_raises_regex(AssertionError, msg, check_estimator, ChangesDict())
with raises(AssertionError, match=msg):
check_estimator(ChangesDict())
# check that `fit` only changes attribures that
# are private (start with an _ or end with a _).
msg = ('Estimator ChangesWrongAttribute should not change or mutate '
'the parameter wrong_attribute from 0 to 1 during fit.')
assert_raises_regex(AssertionError, msg,
check_estimator, ChangesWrongAttribute())
with raises(AssertionError, match=msg):
check_estimator(ChangesWrongAttribute())
check_estimator(ChangesUnderscoreAttribute())
# check that `fit` doesn't add any public attribute
msg = (r'Estimator adds public attribute\(s\) during the fit method.'
' Estimators are only allowed to add private attributes'
' either started with _ or ended'
' with _ but wrong_attribute added')
assert_raises_regex(AssertionError, msg,
check_estimator, SetsWrongAttribute())
with raises(AssertionError, match=msg):
check_estimator(SetsWrongAttribute())
# check for sample order invariance
name = NotInvariantSampleOrder.__name__
method = 'predict'
msg = ("{method} of {name} is not invariant when applied to a dataset"
"with different sample order.").format(method=method, name=name)
assert_raises_regex(AssertionError, msg,
check_estimator, NotInvariantSampleOrder())
with raises(AssertionError, match=msg):
check_estimator(NotInvariantSampleOrder())
# check for invariant method
name = NotInvariantPredict.__name__
method = 'predict'
msg = ("{method} of {name} is not invariant when applied "
"to a subset.").format(method=method, name=name)
assert_raises_regex(AssertionError, msg,
check_estimator, NotInvariantPredict())
with raises(AssertionError, match=msg):
check_estimator(NotInvariantPredict())
# check for sparse matrix input handling
name = NoSparseClassifier.__name__
msg = "Estimator %s doesn't seem to fail gracefully on sparse data" % name
assert_raises_regex(
AssertionError, msg, check_estimator, NoSparseClassifier()
)
with raises(AssertionError, match=msg):
check_estimator(NoSparseClassifier())

# Large indices test on bad estimator
msg = ('Estimator LargeSparseNotSupportedClassifier doesn\'t seem to '
r'support \S{3}_64 matrix, and is not failing gracefully.*')
assert_raises_regex(AssertionError, msg, check_estimator,
LargeSparseNotSupportedClassifier())
with raises(AssertionError, match=msg):
check_estimator(LargeSparseNotSupportedClassifier())

# does error on binary_only untagged estimator
msg = 'Only 2 classes are supported'
assert_raises_regex(ValueError, msg, check_estimator,
UntaggedBinaryClassifier())
with raises(ValueError, match=msg):
check_estimator(UntaggedBinaryClassifier())

# non-regression test for estimators transforming to sparse data
check_estimator(SparseTransformer())
Expand All @@ -537,8 +538,8 @@ def test_check_estimator():

# Check regressor with requires_positive_y estimator tag
msg = 'negative y values not supported!'
assert_raises_regex(ValueError, msg, check_estimator,
RequiresPositiveYRegressor())
with raises(ValueError, match=msg):
check_estimator(RequiresPositiveYRegressor())

# Does not raise error on classifier with poor_score tag
check_estimator(PoorScoreLogisticRegression())
Expand All @@ -547,16 +548,17 @@ def test_check_estimator():
def test_check_outlier_corruption():
# should raise AssertionError
decision = np.array([0., 1., 1.5, 2.])
assert_raises(AssertionError, check_outlier_corruption, 1, 2, decision)
with raises(AssertionError):
check_outlier_corruption(1, 2, decision)
# should pass
decision = np.array([0., 1., 1., 2.])
check_outlier_corruption(1, 2, decision)


def test_check_estimator_transformer_no_mixin():
# check that TransformerMixin is not required for transformer tests to run
assert_raises_regex(AttributeError, '.*fit_transform.*',
check_estimator, BadTransformerWithoutMixin())
with raises(AttributeError, '.*fit_transform.*'):
check_estimator(BadTransformerWithoutMixin())


def test_check_estimator_clones():
Expand Down Expand Up @@ -593,8 +595,8 @@ def test_check_estimators_unfitted():
# check that a ValueError/AttributeError is raised when calling predict
# on an unfitted estimator
msg = "Did not raise"
assert_raises_regex(AssertionError, msg, check_estimators_unfitted,
"estimator", NoSparseClassifier())
with raises(AssertionError, match=msg):
check_estimators_unfitted("estimator", NoSparseClassifier())

# check that CorrectNotFittedError inherit from either ValueError
# or AttributeError
Expand All @@ -610,19 +612,22 @@ class NonConformantEstimatorNoParamSet(BaseEstimator):
def __init__(self, you_should_set_this_=None):
pass

assert_raises_regex(AssertionError,
"Estimator estimator_name should not set any"
" attribute apart from parameters during init."
r" Found attributes \['you_should_not_set_this_'\].",
check_no_attributes_set_in_init,
'estimator_name',
NonConformantEstimatorPrivateSet())
assert_raises_regex(AttributeError,
"Estimator estimator_name should store all "
"parameters as an attribute during init.",
check_no_attributes_set_in_init,
'estimator_name',
NonConformantEstimatorNoParamSet())
msg = (
"Estimator estimator_name should not set any"
" attribute apart from parameters during init."
r" Found attributes \['you_should_not_set_this_'\]."
)
with raises(AssertionError, match=msg):
check_no_attributes_set_in_init('estimator_name',
NonConformantEstimatorPrivateSet())

msg = (
"Estimator estimator_name should store all parameters as an attribute"
" during init"
)
with raises(AttributeError, match=msg):
check_no_attributes_set_in_init('estimator_name',
NonConformantEstimatorNoParamSet())


def test_check_estimator_pairwise():
Expand All @@ -639,32 +644,24 @@ def test_check_estimator_pairwise():


def test_check_classifier_data_not_an_array():
assert_raises_regex(AssertionError,
'Not equal to tolerance',
check_classifier_data_not_an_array,
'estimator_name',
EstimatorInconsistentForPandas())
with raises(AssertionError, match='Not equal to tolerance'):
check_classifier_data_not_an_array('estimator_name',
EstimatorInconsistentForPandas())


def test_check_regressor_data_not_an_array():
assert_raises_regex(AssertionError,
'Not equal to tolerance',
check_regressor_data_not_an_array,
'estimator_name',
EstimatorInconsistentForPandas())
with raises(AssertionError, match='Not equal to tolerance'):
check_regressor_data_not_an_array('estimator_name',
EstimatorInconsistentForPandas())


def test_check_estimator_get_tags_default_keys():
estimator = EstimatorMissingDefaultTags()
err_msg = (r"EstimatorMissingDefaultTags._get_tags\(\) is missing entries"
r" for the following default tags: {'allow_nan'}")
assert_raises_regex(
AssertionError,
err_msg,
check_estimator_get_tags_default_keys,
estimator.__class__.__name__,
estimator,
)
with raises(AssertionError, match=err_msg):
check_estimator_get_tags_default_keys(estimator.__class__.__name__,
estimator)

# noop check when _get_tags is not available
estimator = MinimalTransformer()
Expand All @@ -688,12 +685,15 @@ def run_tests_without_pytest():

def test_check_class_weight_balanced_linear_classifier():
# check that ill-computed balanced weights raises an exception
assert_raises_regex(AssertionError,
"Classifier estimator_name is not computing"
" class_weight=balanced properly.",
check_class_weight_balanced_linear_classifier,
'estimator_name',
BadBalancedWeightsClassifier)
msg = (
"Classifier estimator_name is not computing class_weight=balanced "
"properly"
)
with raises(AssertionError, match=msg):
check_class_weight_balanced_linear_classifier(
'estimator_name',
BadBalancedWeightsClassifier
)


def test_all_estimators_all_public():
Expand Down