def test_check_estimator_clones():
# check that check_estimator doesn't modify the estimator it receives
from sklearn.datasets import load_iris
iris = load_iris()
for Estimator in [
GaussianMixture, LinearRegression, RandomForestClassifier, NMF,
SGDClassifier, MiniBatchKMeans
]:
with ignore_warnings(category=FutureWarning):
# when 'est = SGDClassifier()'
est = Estimator()
_set_checking_parameters(est)
set_random_state(est)
# without fitting
old_hash = joblib.hash(est)
check_estimator(est)
assert old_hash == joblib.hash(est)
with ignore_warnings(category=FutureWarning):
# when 'est = SGDClassifier()'
est = Estimator()
_set_checking_parameters(est)
set_random_state(est)
# with fitting
est.fit(iris.data + 10, iris.target)
old_hash = joblib.hash(est)
check_estimator(est)
assert old_hash == joblib.hash(est)
def test_estimators(estimator, check, request):
# Common tests for estimator instances
with ignore_warnings(category=(FutureWarning,
ConvergenceWarning,
UserWarning, FutureWarning)):
_set_checking_parameters(estimator)
check(estimator)
def test_transformers_get_feature_names_out(transformer):
_set_checking_parameters(transformer)
with ignore_warnings(category=(FutureWarning)):
check_transformer_get_feature_names_out(transformer.__class__.__name__,
transformer)
check_transformer_get_feature_names_out_pandas(
transformer.__class__.__name__, transformer)
def test_check_param_validation(estimator):
name = estimator.__class__.__name__
if name in PARAM_VALIDATION_ESTIMATORS_TO_IGNORE:
pytest.skip(
f"Skipping check_param_validation for {name}: Does not use the "
"appropriate API for parameter validation yet.")
_set_checking_parameters(estimator)
check_param_validation(name, estimator)
def test_pandas_column_name_consistency(estimator):
_set_checking_parameters(estimator)
with ignore_warnings(category=(FutureWarning)):
with pytest.warns(None) as record:
check_dataframe_column_names_consistency(
estimator.__class__.__name__, estimator)
for warning in record:
assert "was fitted without feature names" not in str(
warning.message)
def test_estimators(estimator, check, request):
# Common tests for estimator instances
with ignore_warnings(category=(FutureWarning, ConvergenceWarning,
UserWarning, FutureWarning)):
_set_checking_parameters(estimator)
xfail_checks = _safe_tags(estimator, '_xfail_test')
check_name = _set_check_estimator_ids(check)
if xfail_checks:
if check_name in xfail_checks:
msg = xfail_checks[check_name]
request.applymarker(pytest.mark.xfail(reason=msg))
check(estimator)
def test_check_n_features_in_after_fitting(estimator):
_set_checking_parameters(estimator)
check_n_features_in_after_fitting(estimator.__class__.__name__, estimator)
def test_pandas_column_name_consistency(estimator):
_set_checking_parameters(estimator)
with ignore_warnings(category=(FutureWarning)):
check_dataframe_column_names_consistency(estimator.__class__.__name__,
estimator)
