10000 TST replace assert_warns* by pytest.warns in model_selection/tests by nuka137 · Pull Request #19458 · scikit-learn/scikit-learn · GitHub
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TST replace assert_warns* by pytest.warns in model_selection/tests #19458

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merged 4 commits into from
Feb 19, 2021
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TST replace assert_warns* by pytest.warns in model_selection/tests #19458

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Remove the use of assert_warns and assert_warns_message from tests in…
nuka137 Feb 15, 2021
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nuka137 Feb 16, 2021
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nuka137 Feb 16, 2021
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nuka137 Feb 19, 2021
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2 changes: 1 addition & 1 deletion sklearn/model_selection/_validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1473,7 +1473,7 @@ def _translate_train_sizes(train_sizes, n_max_training_samples):
if n_ticks > train_sizes_abs.shape[0]:
warnings.warn("Removed duplicate entries from 'train_sizes'. Number "
"of ticks will be less than the size of "
"'train_sizes' %d instead of %d)."
"'train_sizes': %d instead of %d."
% (train_sizes_abs.shape[0], n_ticks), RuntimeWarning)

return train_sizes_abs
Expand Down
20 changes: 14 additions & 6 deletions sklearn/model_selection/tests/test_search.py
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Expand Up @@ -14,8 +14,6 @@
import pytest

from sklearn.utils._testing import (
assert_warns,
assert_warns_message,
assert_raise_message,
assert_array_equal,
assert_array_almost_equal,
Expand Down Expand Up @@ -1433,7 +1431,12 @@ def test_grid_search_failing_classifier():
# error in this test.
gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score=0.0)
assert_warns(FitFailedWarning, gs.fit, X, y)
warning_message = (
"Estimator fit failed. The score on this train-test partition "
"for these parameters will be set to 0.0.*."
)
with pytest.warns(FitFailedWarning, match=warning_message):
gs.fit(X, y)
n_candidates = len(gs.cv_results_['params'])

# Ensure that grid scores were set to zero as required for those fits
Expand All @@ -1449,7 +1452,12 @@ def get_cand_scores(i):

gs = GridSearchCV(clf, [{'parameter': [0, 1, 2]}], scoring='accuracy',
refit=False, error_score=float('nan'))
assert_warns(FitFailedWarning, gs.fit, X, y)
warning_message = (
"Estimator fit failed. The score on this train-test partition "
"for these parameters will be set to nan."
)
with pytest.warns(FitFailedWarning, match=warning_message):
gs.fit(X, y)
n_candidates = len(gs.cv_results_['params'])
assert all(np.all(np.isnan(get_cand_scores(cand_i)))
for cand_i in range(n_candidates)
Expand Down Expand Up @@ -1492,8 +1500,8 @@ def test_parameters_sampler_replacement():
'than n_iter=%d. Running %d iterations. For '
'exhaustive searches, use GridSearchCV.'
% (grid_size, n_iter, grid_size))
assert_warns_message(UserWarning, expected_warning,
list, sampler)
with pytest.warns(UserWarning, match=expected_warning):
list(sampler)

# degenerates to GridSearchCV if n_iter the same as grid_size
sampler = ParameterSampler(params, n_iter=8)
Expand Down
5 changes: 2 additions & 3 deletions sklearn/model_selection/tests/test_split.py
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Expand Up @@ -14,7 +14,6 @@
from sklearn.utils._testing import assert_raises_regexp
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_warns_message
from sklearn.utils._testing import assert_raise_message
from sklearn.utils._testing import ignore_warnings
from sklearn.utils.validation import _num_samples
Expand Down Expand Up @@ -193,8 +192,8 @@ def test_kfold_valueerrors():
y = np.array([3, 3, -1, -1, 3])

skf_3 = StratifiedKFold(3)
assert_warns_message(Warning, "The least populated class",
next, skf_3.split(X2, y))
with pytest.warns(Warning, match="The least populated class"):
next(skf_3.split(X2, y))

# Check that despite the warning the folds are still computed even
# though all the classes are not necessarily represented at on each
Expand Down
41 changes: 26 additions & 15 deletions sklearn/model_selection/tests/test_validation.py
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Expand Up @@ -17,8 +17,6 @@
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_raises
from sklearn.utils._testing import assert_raise_message
from sklearn.utils._testing import assert_warns
from sklearn.utils._testing import assert_warns_message
from sklearn.utils._testing import assert_raises_regex
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_array_equal
Expand Down Expand Up @@ -857,13 +855,12 @@ def split(self, X, y=None, groups=None):

X, y = load_iris(return_X_y=True)

warning_message = ('Number of classes in training fold (2) does '
'not match total number of classes (3). '
warning_message = (r'Number of classes in training fold \(2\) does '
r'not match total number of classes \(3\). '
'Results may not be appropriate for your use case.')
assert_warns_message(RuntimeWarning, warning_message,
cross_val_predict,
LogisticRegression(solver="liblinear"),
X, y, method='predict_proba', cv=KFold(2))
with pytest.warns(RuntimeWarning, match=warning_message):
cross_val_predict(LogisticRegression(solver="liblinear"),
X, y, method='predict_proba', cv=KFold(2))


def test_cross_val_predict_decision_function_shape():
Expand Down Expand Up @@ -1210,9 +1207,13 @@ def test_learning_curve_remove_duplicate_sample_sizes():
n_redundant=0, n_classes=2,
n_clusters_per_class=1, random_state=0)
estimator = MockImprovingEstimator(2)
train_sizes, _, _ = assert_warns(
RuntimeWarning, learning_curve, estimator, X, y, cv=3,
train_sizes=np.linspace(0.33, 1.0, 3))
warning_message = (
"Removed duplicate entries from 'train_sizes'. Number of ticks "
"will be less than the size of 'train_sizes': 2 instead of 3."
)
with pytest.warns(RuntimeWarning, match=warning_message):
train_sizes, _, _ = learning_curve(
estimator, X, y, cv=3, train_sizes=np.linspace(0.33, 1.0, 3))
assert_array_equal(train_sizes, [1, 2])


Expand Down Expand Up @@ -1753,8 +1754,13 @@ def test_fit_and_score_failing():
# passing error score to trigger the warning message
fit_and_score_kwargs = {'error_score': 0}
# check if the warning message type is as expected
assert_warns(FitFailedWarning, _fit_and_score, *fit_and_score_args,
**fit_and_score_kwargs)
warning_message = (
"Estimator fit failed. The score on this train-test partition for "
"these parameters will be set to %f."
% (fit_and_score_kwargs['error_score'])
)
with pytest.warns(FitFailedWarning, match=warning_message):
_fit_and_score(*fit_and_score_args, **fit_and_score_kwargs)
# since we're using FailingClassfier, our error will be the following
error_message = "ValueError: Failing classifier failed as required"
# the warning message we're expecting to see
Expand All @@ -1769,8 +1775,13 @@ def test_warn_trace(msg):
mtb = split[0] + '\n' + split[-1]
return warning_message in mtb
# check traceback is included
assert_warns_message(FitFailedWarning, test_warn_trace, _fit_and_score,
*fit_and_score_args, **fit_and_score_kwargs)
warning_message = (
"Estimator fit failed. The score on this train-test partition for "
"these parameters will be set to %f."
% (fit_and_score_kwargs['error_score'])
)
with pytest.warns(FitFailedWarning, match=warning_message):
_fit_and_score(*fit_and_score_args, **fit_and_score_kwargs)

fit_and_score_kwargs = {'error_score': 'raise'}
# check if exception was raised, with default error_score='raise'
Expand Down
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