def test_non_meta_estimators(name, Estimator, check):
# Common tests for non-meta estimators
with ignore_warnings(category=(DeprecationWarning, ConvergenceWarning,
UserWarning, FutureWarning)):
estimator = Estimator()
set_checking_parameters(estimator)
check(name, estimator)
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_equal(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_equal(old_hash, joblib.hash(est))
def test_estimators(estimator, check):
# Common tests for estimator instances
with ignore_warnings(category=(DeprecationWarning, ConvergenceWarning,
UserWarning, FutureWarning)):
set_checking_parameters(estimator)
name = estimator.__class__.__name__
check(name, estimator)
def test_non_meta_estimators(name, Estimator, check):
# Common tests for non-meta estimators
with ignore_warnings(category=(DeprecationWarning, ConvergenceWarning,
UserWarning, FutureWarning)):
estimator = Estimator()
set_checking_parameters(estimator)
check(name, estimator)
def test_non_meta_estimators():
# input validation etc for non-meta estimators
estimators = all_estimators()
for name, Estimator in estimators:
if issubclass(Estimator, BiclusterMixin):
continue
if name.startswith("_"):
continue
estimator = Estimator()
# check this on class
yield check_no_fit_attributes_set_in_init, name, Estimator
for check in _yield_all_checks(name, estimator):
set_checking_parameters(estimator)
yield check, name, estimator
def test_non_meta_estimators():
# input validation etc for non-meta estimators
estimators = all_estimators()
for name, Estimator in estimators:
if issubclass(Estimator, BiclusterMixin):
continue
if name.startswith("_"):
continue
estimator = Estimator()
# check this on class
yield check_no_attributes_set_in_init, name, estimator
for check in _yield_all_checks(name, estimator):
set_checking_parameters(estimator)
yield check, name, estimator
def test_non_meta_estimators(name, Estimator, check):
# Common tests for non-meta estimators
estimator = Estimator()
set_checking_parameters(estimator)
check(name, estimator)
def test_set_checking_parameters():
with pytest.warns(DeprecationWarning, match="removed in version 0.24"):
set_checking_parameters(DummyClassifier())
def test_non_meta_estimators(name, Estimator, check):
# Common tests for non-meta estimators
estimator = Estimator()
set_checking_parameters(estimator)
check(name, estimator)
