def check_dont_overwrite_parameters(name, estimator_orig):
# check that fit method only changes or sets private attributes
if hasattr(estimator_orig.__init__, "deprecated_original"):
# to not check deprecated classes
return
estimator = clone(estimator_orig)
rnd = np.random.RandomState(0)
X = 3 * rnd.uniform(size=(20, 3))
X = pairwise_estimator_convert_X(X, estimator_orig)
y = (X[:, 0] < 2).astype(np.int)
y = multioutput_estimator_convert_y_2d(estimator, y)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
if hasattr(estimator, "n_clusters"):
estimator.n_clusters = 1
set_random_state(estimator, 1)
dict_before_fit = estimator.__dict__.copy()
estimator.fit(X, y)
dict_after_fit = estimator.__dict__
public_keys_after_fit = [
key for key in dict_after_fit.keys() if is_public_parameter(key)
]
attrs_added_by_fit = [
key for key in public_keys_after_fit
if key not in dict_before_fit.keys()
]
# check that fit doesn't add any public attribute
assert not attrs_added_by_fit, (
'Estimator adds public attribute(s) during'
' the fit method.'
' Estimators are only allowed to add private attributes'
' either started with _ or ended'
' with _ but %s added' % ', '.join(attrs_added_by_fit))
# check that fit doesn't change any public attribute
attrs_changed_by_fit = [
key for key in public_keys_after_fit
if (dict_before_fit[key] is not dict_after_fit[key])
]
assert not attrs_changed_by_fit, (
'Estimator changes public attribute(s) during'
' the fit method. Estimators are only allowed'
' to change attributes started'
' or ended with _, but'
' %s changed' % ', '.join(attrs_changed_by_fit))
def test_dont_overwrite_parameters(estimator, build_dataset,
with_preprocessor):
# Adapted from scikit-learn
# check that fit method only changes or sets private attributes
input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)
estimator = clone(estimator)
estimator.set_params(preprocessor=preprocessor)
if hasattr(estimator, "n_components"):
estimator.n_components = 1
dict_before_fit = estimator.__dict__.copy()
estimator.fit(*remove_y_quadruplets(estimator, input_data, labels))
dict_after_fit = estimator.__dict__
public_keys_after_fit = [
key for key in dict_after_fit.keys() if is_public_parameter(key)
]
attrs_added_by_fit = [
key for key in public_keys_after_fit
if key not in dict_before_fit.keys()
]
# check that fit doesn't add any public attribute
assert not attrs_added_by_fit, (
"Estimator adds public attribute(s) during"
" the fit method."
" Estimators are only allowed to add private "
"attributes"
" either started with _ or ended"
" with _ but %s added" % ', '.join(attrs_added_by_fit))
# check that fit doesn't change any public attribute
attrs_changed_by_fit = [
key for key in public_keys_after_fit
if (dict_before_fit[key] is not dict_after_fit[key])
]
assert not attrs_changed_by_fit, (
"Estimator changes public attribute(s) during"
" the fit method. Estimators are only allowed"
" to change attributes started"
" or ended with _, but"
" %s changed" % ', '.join(attrs_changed_by_fit))
def test_dont_overwrite_parameters(estimator, build_dataset,
with_preprocessor):
# Adapted from scikit-learn
# check that fit method only changes or sets private attributes
input_data, labels, preprocessor, _ = build_dataset(with_preprocessor)
estimator = clone(estimator)
estimator.set_params(preprocessor=preprocessor)
if hasattr(estimator, "num_dims"):
estimator.num_dims = 1
dict_before_fit = estimator.__dict__.copy()
estimator.fit(*remove_y_quadruplets(estimator, input_data, labels))
dict_after_fit = estimator.__dict__
public_keys_after_fit = [key for key in dict_after_fit.keys()
if is_public_parameter(key)]
attrs_added_by_fit = [key for key in public_keys_after_fit
if key not in dict_before_fit.keys()]
# check that fit doesn't add any public attribute
assert not attrs_added_by_fit, (
"Estimator adds public attribute(s) during"
" the fit method."
" Estimators are only allowed to add private "
"attributes"
" either started with _ or ended"
" with _ but %s added" % ', '.join(attrs_added_by_fit))
# check that fit doesn't change any public attribute
attrs_changed_by_fit = [key for key in public_keys_after_fit
if (dict_before_fit[key]
is not dict_after_fit[key])]
assert not attrs_changed_by_fit, (
"Estimator changes public attribute(s) during"
" the fit method. Estimators are only allowed"
" to change attributes started"
" or ended with _, but"
" %s changed" % ', '.join(attrs_changed_by_fit))
def test_is_public_parameter():
with pytest.warns(DeprecationWarning, match="removed in version 0.24"):
is_public_parameter('hello')