def test_transformer_n_iter():
transformers = all_estimators(type_filter='transformer')
for name, Estimator in transformers:
if issubclass(Estimator, ProjectedGradientNMF):
# The ProjectedGradientNMF class is deprecated
with ignore_warnings():
estimator = Estimator()
else:
estimator = Estimator()
# Dependent on external solvers and hence accessing the iter
# param is non-trivial.
external_solver = [
'Isomap', 'KernelPCA', 'LocallyLinearEmbedding', 'RandomizedLasso',
'LogisticRegressionCV'
]
if hasattr(estimator, "max_iter") and name not in external_solver:
if isinstance(estimator, ProjectedGradientNMF):
# The ProjectedGradientNMF class is deprecated
with ignore_warnings():
yield _set_test_name(check_transformer_n_iter,
name), name, estimator
else:
yield _set_test_name(check_transformer_n_iter,
name), 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
for check in _yield_all_checks(name, Estimator):
if issubclass(Estimator, ProjectedGradientNMF):
# The ProjectedGradientNMF class is deprecated
with ignore_warnings():
yield _set_test_name(check, name), name, Estimator
else:
yield _set_test_name(check, name), 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
for check in _yield_all_checks(name, Estimator):
if issubclass(Estimator, ProjectedGradientNMF):
# The ProjectedGradientNMF class is deprecated
with ignore_warnings():
yield _set_test_name(check, name), name, Estimator
else:
yield _set_test_name(check, name), name, Estimator
def test_non_transformer_estimators_n_iter():
# Test that all estimators of type which are non-transformer
# and which have an attribute of max_iter, return the attribute
# of n_iter atleast 1.
for est_type in ['regressor', 'classifier', 'cluster']:
regressors = all_estimators(type_filter=est_type)
for name, Estimator in regressors:
# LassoLars stops early for the default alpha=1.0 for
# the iris dataset.
if name == 'LassoLars':
estimator = Estimator(alpha=0.)
else:
estimator = Estimator()
if hasattr(estimator, "max_iter"):
# These models are dependent on external solvers like
# libsvm and accessing the iter parameter is non-trivial.
if name in ([
'Ridge', 'SVR', 'NuSVR', 'NuSVC', 'RidgeClassifier',
'SVC', 'RandomizedLasso', 'LogisticRegressionCV'
]):
continue
# Tested in test_transformer_n_iter below
elif (name in CROSS_DECOMPOSITION
or name in ['LinearSVC', 'LogisticRegression']):
continue
else:
# Multitask models related to ENet cannot handle
# if y is mono-output.
yield (_set_test_name(
check_non_transformer_estimators_n_iter,
name), name, estimator, 'Multi' in name)
def test_non_transformer_estimators_n_iter():
# Test that all estimators of type which are non-transformer
# and which have an attribute of max_iter, return the attribute
# of n_iter atleast 1.
for est_type in ['regressor', 'classifier', 'cluster']:
regressors = all_estimators(type_filter=est_type)
for name, Estimator in regressors:
# LassoLars stops early for the default alpha=1.0 for
# the iris dataset.
if name == 'LassoLars':
estimator = Estimator(alpha=0.)
else:
estimator = Estimator()
if hasattr(estimator, "max_iter"):
# These models are dependent on external solvers like
# libsvm and accessing the iter parameter is non-trivial.
if name in (['Ridge', 'SVR', 'NuSVR', 'NuSVC',
'RidgeClassifier', 'SVC', 'RandomizedLasso',
'LogisticRegressionCV']):
continue
# Tested in test_transformer_n_iter below
elif (name in CROSS_DECOMPOSITION or
name in ['LinearSVC', 'LogisticRegression']):
continue
else:
# Multitask models related to ENet cannot handle
# if y is mono-output.
yield (_set_test_name(
check_non_transformer_estimators_n_iter, name),
name, estimator, 'Multi' in name)
def test_get_params_invariance():
# Test for estimators that support get_params, that
# get_params(deep=False) is a subset of get_params(deep=True)
# Related to issue #4465
estimators = all_estimators(include_meta_estimators=False,
include_other=True)
for name, Estimator in estimators:
if hasattr(Estimator, 'get_params'):
# If class is deprecated, ignore deprecated warnings
if hasattr(Estimator.__init__, "deprecated_original"):
with ignore_warnings():
yield _set_test_name(check_get_params_invariance,
name), name, Estimator
else:
yield _set_test_name(check_get_params_invariance,
name), name, Estimator
def test_get_params_invariance():
# Test for estimators that support get_params, that
# get_params(deep=False) is a subset of get_params(deep=True)
# Related to issue #4465
estimators = all_estimators(include_meta_estimators=False,
include_other=True)
for name, Estimator in estimators:
if hasattr(Estimator, 'get_params'):
# If class is deprecated, ignore deprecated warnings
if hasattr(Estimator.__init__, "deprecated_original"):
with ignore_warnings():
yield _set_test_name(
check_get_params_invariance, name), name, Estimator
else:
yield _set_test_name(
check_get_params_invariance, name), name, Estimator
def test_all_estimators():
# Test that estimators are default-constructible, cloneable
# and have working repr.
estimators = all_estimators(include_meta_estimators=True)
# Meta sanity-check to make sure that the estimator introspection runs
# properly
assert_greater(len(estimators), 0)
for name, Estimator in estimators:
# some can just not be sensibly default constructed
yield (_set_test_name(check_parameters_default_constructible,
name), name, Estimator)
def test_all_estimators():
# Test that estimators are default-constructible, cloneable
# and have working repr.
estimators = all_estimators(include_meta_estimators=True)
# Meta sanity-check to make sure that the estimator introspection runs
# properly
assert_greater(len(estimators), 0)
for name, Estimator in estimators:
# some can just not be sensibly default constructed
yield (_set_test_name(check_parameters_default_constructible, name),
name, Estimator)
def test_class_weight_balanced_linear_classifiers():
classifiers = all_estimators(type_filter='classifier')
clean_warning_registry()
with warnings.catch_warnings(record=True):
linear_classifiers = [
(name, clazz) for name, clazz in classifiers
if ('class_weight' in clazz().get_params().keys()
and issubclass(clazz, LinearClassifierMixin))
]
for name, Classifier in linear_classifiers:
yield _set_test_name(check_class_weight_balanced_linear_classifier,
name), name, Classifier
def test_transformer_n_iter():
transformers = all_estimators(type_filter='transformer')
for name, Estimator in transformers:
if issubclass(Estimator, ProjectedGradientNMF):
# The ProjectedGradientNMF class is deprecated
with ignore_warnings():
estimator = Estimator()
else:
estimator = Estimator()
# Dependent on external solvers and hence accessing the iter
# param is non-trivial.
external_solver = ['Isomap', 'KernelPCA', 'LocallyLinearEmbedding',
'RandomizedLasso', 'LogisticRegressionCV']
if hasattr(estimator, "max_iter") and name not in external_solver:
if isinstance(estimator, ProjectedGradientNMF):
# The ProjectedGradientNMF class is deprecated
with ignore_warnings():
yield _set_test_name(
check_transformer_n_iter, name), name, estimator
else:
yield _set_test_name(
check_transformer_n_iter, name), name, estimator
def test_class_weight_balanced_linear_classifiers():
classifiers = all_estimators(type_filter='classifier')
clean_warning_registry()
with warnings.catch_warnings(record=True):
linear_classifiers = [
(name, clazz)
for name, clazz in classifiers
if ('class_weight' in clazz().get_params().keys() and
issubclass(clazz, LinearClassifierMixin))]
for name, Classifier in linear_classifiers:
yield _set_test_name(check_class_weight_balanced_linear_classifier,
name), name, Classifier