def _yield_all_checks(name, estimator):
for check in _yield_non_meta_checks(name, estimator):
yield check
if is_classifier(estimator):
for check in _yield_classifier_checks(name, estimator):
yield check
if is_regressor(estimator):
for check in _yield_regressor_checks(name, estimator):
yield check
if hasattr(estimator, 'transform'):
for check in _yield_transformer_checks(name, estimator):
yield check
if isinstance(estimator, ClusterMixin):
for check in _yield_clustering_checks(name, estimator):
yield check
if is_outlier_detector(estimator):
for check in _yield_outliers_checks(name, estimator):
yield check
yield check_fit2d_predict1d
yield check_methods_subset_invariance
yield check_fit2d_1sample
yield check_fit2d_1feature
yield check_fit1d
yield check_get_params_invariance
yield check_set_params
yield check_dict_unchanged
yield check_dont_overwrite_parameters
def yield_all_checks(name, estimator):
tags = estimator._get_tags()
if "2darray" not in tags["X_types"]:
warnings.warn("Can't test estimator {} which requires input "
" of type {}".format(name, tags["X_types"]),
SkipTestWarning)
return
if tags["_skip_test"]:
warnings.warn("Explicit SKIP via _skip_test tag for estimator "
"{}.".format(name),
SkipTestWarning)
return
yield from _yield_checks(name, estimator)
if is_classifier(estimator):
yield from _yield_classifier_checks(name, estimator)
if is_regressor(estimator):
yield from _yield_regressor_checks(name, estimator)
if hasattr(estimator, 'transform'):
if not tags["allow_variable_length"]:
# Transformer tests ensure that shapes are the same at fit and
# transform time, hence we need to skip them for estimators that
# allow variable-length inputs
yield from _yield_transformer_checks(name, estimator)
if isinstance(estimator, ClusterMixin):
yield from _yield_clustering_checks(name, estimator)
if is_outlier_detector(estimator):
yield from _yield_outliers_checks(name, estimator)
# We are not strict on presence/absence of the 3rd dimension
# yield check_fit2d_predict1d
if not tags["non_deterministic"]:
yield check_methods_subset_invariance
yield check_fit2d_1sample
yield check_fit2d_1feature
yield check_fit1d
yield check_get_params_invariance
yield check_set_params
yield check_dict_unchanged
yield check_dont_overwrite_parameters
yield check_fit_idempotent
if (is_classifier(estimator) or
is_regressor(estimator) or
isinstance(estimator, ClusterMixin)):
if tags["allow_variable_length"]:
yield check_different_length_fit_predict_transform
def yield_all_checks(name, estimator):
tags = _safe_tags(estimator)
if "2darray" not in tags["X_types"]:
warnings.warn(
"Can't test estimator {} which requires input "
" of type {}".format(name, tags["X_types"]), SkipTestWarning)
return
if tags["_skip_test"]:
warnings.warn(
"Explicit SKIP via _skip_test tag for estimator "
"{}.".format(name), SkipTestWarning)
return
for check in _yield_checks(name, estimator):
yield check
if is_classifier(estimator):
for check in _yield_classifier_checks(name, estimator):
yield check
if is_regressor(estimator):
for check in _yield_regressor_checks(name, estimator):
yield check
if hasattr(estimator, 'transform'):
for check in _yield_transformer_checks(name, estimator):
yield check
if isinstance(estimator, ClusterMixin):
for check in _yield_clustering_checks(name, estimator):
yield check
if is_outlier_detector(estimator):
for check in _yield_outliers_checks(name, estimator):
yield check
yield check_fit2d_predict1d
if not tags["non_deterministic"]:
yield check_methods_subset_invariance
yield check_fit2d_1sample
yield check_fit2d_1feature
yield check_fit1d
yield check_get_params_invariance
yield check_set_params
yield check_dict_unchanged
yield check_dont_overwrite_parameters
yield check_fit_idempotent