def test_all_estimators():
# check if the filtering is working with a list or a single string
type_filter = "sampler"
all_estimators(type_filter=type_filter)
type_filter = ["sampler"]
estimators = all_estimators(type_filter=type_filter)
for estimator in estimators:
# check that all estimators are sampler
assert issubclass(estimator[1], SamplerMixin)
# check that an error is raised when the type is unknown
type_filter = "rnd"
with raises(ValueError, match="Parameter type_filter must be 'sampler'"):
all_estimators(type_filter=type_filter)
def test_all_estimators():
# check if the filtering is working with a list or a single string
type_filter = 'sampler'
all_estimators(type_filter=type_filter)
type_filter = ['sampler']
estimators = all_estimators(type_filter=type_filter)
for estimator in estimators:
# check that all estimators are sampler
assert issubclass(estimator[1], SamplerMixin)
# check that an error is raised when the type is unknown
type_filter = 'rnd'
with raises(ValueError, match="Parameter type_filter must be 'sampler'"):
all_estimators(type_filter=type_filter)
def test_all_estimators():
estimators = all_estimators(include_meta_estimators=True)
assert len(estimators) > 0
for name, Estimator in estimators:
# some can just not be sensibly default constructed
yield (_named_check(check_estimator, name),
Estimator)
def test_all_estimators():
# check if the filtering is working with a list or a single string
type_filter = 'sampler'
all_estimators(type_filter=type_filter)
type_filter = ['sampler']
estimators = all_estimators(type_filter=type_filter)
for estimator in estimators:
# check that all estimators are sampler
assert issubclass(estimator[1], SamplerMixin)
# check that an error is raised when the type is unknown
type_filter = 'rnd'
assert_raises_regex(ValueError,
"Parameter type_filter must be 'sampler'",
all_estimators,
type_filter=type_filter)
def test_non_meta_estimators():
# input validation etc for non-meta estimators
estimators = all_estimators()
for name, Estimator in estimators:
if name.startswith("_"):
continue
for check in _yield_all_checks(name, Estimator):
yield _named_check(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 name.startswith("_"):
continue
for check in _yield_all_checks(name, Estimator):
yield _named_check(check, name), name, Estimator
logger_name = Estimator().logger.name
class_module_name = Estimator.__module__
assert logger_name == class_module_name
def test_non_meta_estimators():
# input validation etc for non-meta estimators
estimators = all_estimators()
for name, Estimator in estimators:
if name.startswith("_"):
continue
for check in _yield_all_checks(name, Estimator):
yield _named_check(check, name), name, Estimator
logger_name = Estimator().logger.name
class_module_name = Estimator.__module__
assert logger_name == class_module_name
def _tested_estimators():
for name, Estimator in all_estimators():
try:
estimator = _construct_instance(Estimator)
set_random_state(estimator)
except SkipTest:
continue
if isinstance(estimator, NearMiss):
# For NearMiss, let's check the three algorithms
for version in (1, 2, 3):
yield clone(estimator).set_params(version=version)
else:
yield estimator
def get_all_methods():
estimators = all_estimators()
for name, Estimator in estimators:
if name.startswith("_"):
# skip private classes
continue
methods = []
for name in dir(Estimator):
if name.startswith("_"):
continue
method_obj = getattr(Estimator, name)
if hasattr(method_obj, "__call__") or isinstance(
method_obj, property):
methods.append(name)
methods.append(None)
for method in sorted(methods, key=lambda x: str(x)):
yield Estimator, method
def test_all_estimators():
estimators = all_estimators(include_meta_estimators=True)
assert_greater(len(estimators), 0)
for name, Estimator in estimators:
# some can just not be sensibly default constructed
yield (_named_check(check_estimator, name), Estimator)
def test_all_estimator_no_base_class():
# test that all_estimators doesn't find abstract classes.
for name, Estimator in all_estimators():
msg = ("Base estimators such as {0} should not be included"
" in all_estimators").format(name)
assert not name.lower().startswith('base'), msg
"""Common tests"""
# Authors: Guillaume Lemaitre <*****@*****.**>
# Christos Aridas
# License: MIT
import pytest
from imblearn.utils.estimator_checks import check_estimator
from imblearn.utils.estimator_checks import _yield_all_checks
from imblearn.utils.testing import all_estimators
@pytest.mark.parametrize("name, Estimator", all_estimators())
def test_all_estimator_no_base_class(name, Estimator):
# test that all_estimators doesn't find abstract classes.
msg = (
"Base estimators such as {0} should not be included"
" in all_estimators"
).format(name)
assert not name.lower().startswith("base"), msg
@pytest.mark.filterwarnings("ignore:'y' should be of types")
@pytest.mark.filterwarnings("ignore:The number of the samples to")
@pytest.mark.parametrize(
"name, Estimator", all_estimators(include_meta_estimators=True)
)
def test_all_estimators(name, Estimator):
# don't run twice the sampler tests. Meta-estimator do not have a
# fit_resample method.
check_estimator(Estimator, run_sampler_tests=False)
"""Common tests"""
# Authors: Guillaume Lemaitre <*****@*****.**>
# Christos Aridas
# License: MIT
import pytest
from imblearn.utils.estimator_checks import check_estimator, _yield_all_checks
from imblearn.utils.testing import all_estimators
@pytest.mark.parametrize('name, Estimator', all_estimators())
def test_all_estimator_no_base_class(name, Estimator):
# test that all_estimators doesn't find abstract classes.
msg = ("Base estimators such as {0} should not be included"
" in all_estimators").format(name)
assert not name.lower().startswith('base'), msg
@pytest.mark.filterwarnings("ignore:'ratio' is deprecated from 0.4")
@pytest.mark.filterwarnings("ignore:'sampling_strategy' as a dict for")
@pytest.mark.filterwarnings("ignore:Class EasyEnsemble is deprecated")
@pytest.mark.filterwarnings('ignore:Class BalanceCascade is deprecated')
@pytest.mark.filterwarnings('ignore:"kind" is deprecated in 0.4 and will be')
@pytest.mark.filterwarnings('ignore:"svm_estimator" is deprecated in 0.4 and')
@pytest.mark.filterwarnings('ignore:"out_step" is deprecated in 0.4 and')
@pytest.mark.filterwarnings('ignore:"m_neighbors" is deprecated in 0.4 and')
@pytest.mark.filterwarnings("ignore:'y' should be of types")
@pytest.mark.filterwarnings("ignore:The number of the samples to")
@pytest.mark.parametrize('name, Estimator',
all_estimators(include_meta_estimators=True))
"""Common tests"""
# Authors: Guillaume Lemaitre <*****@*****.**>
# Christos Aridas
# License: MIT
import pytest
from imblearn.utils.estimator_checks import check_estimator, _yield_all_checks
from imblearn.utils.testing import all_estimators
@pytest.mark.parametrize(
'name, Estimator',
all_estimators()
)
def test_all_estimator_no_base_class(name, Estimator):
# test that all_estimators doesn't find abstract classes.
msg = ("Base estimators such as {0} should not be included"
" in all_estimators").format(name)
assert not name.lower().startswith('base'), msg
@pytest.mark.filterwarnings("ignore:'ratio' is deprecated from 0.4")
@pytest.mark.filterwarnings("ignore:'sampling_strategy' as a dict for")
@pytest.mark.filterwarnings("ignore:Class EasyEnsemble is deprecated")
@pytest.mark.filterwarnings('ignore:"kind" is deprecated in 0.4 and will be')
@pytest.mark.filterwarnings('ignore:"svm_estimator" is deprecated in 0.4 and')
@pytest.mark.filterwarnings('ignore:"out_step" is deprecated in 0.4 and')
@pytest.mark.filterwarnings('ignore:"m_neighbors" is deprecated in 0.4 and')
@pytest.mark.filterwarnings("ignore:'y' should be of types")
@pytest.mark.parametrize(
def _tested_non_meta_estimators():
for name, Estimator in all_estimators():
if name.startswith("_"):
continue
yield name, Estimator
"""Common tests"""
# Authors: Guillaume Lemaitre <*****@*****.**>
# Christos Aridas
# License: MIT
import pytest
from imblearn.utils.estimator_checks import check_estimator, _yield_all_checks
from imblearn.utils.testing import all_estimators
@pytest.mark.parametrize(
'name, Estimator',
all_estimators()
)
def test_all_estimator_no_base_class(name, Estimator):
# test that all_estimators doesn't find abstract classes.
msg = ("Base estimators such as {0} should not be included"
" in all_estimators").format(name)
assert not name.lower().startswith('base'), msg
@pytest.mark.filterwarnings("ignore:'ratio' is deprecated from 0.4")
@pytest.mark.filterwarnings("ignore:'sampling_strategy' as a dict for")
@pytest.mark.filterwarnings("ignore:Class EasyEnsemble is deprecated")
@pytest.mark.filterwarnings('ignore:Class BalanceCascade is deprecated')
@pytest.mark.filterwarnings('ignore:"kind" is deprecated in 0.4 and will be')
@pytest.mark.filterwarnings('ignore:"svm_estimator" is deprecated in 0.4 and')
@pytest.mark.filterwarnings('ignore:"out_step" is deprecated in 0.4 and')
@pytest.mark.filterwarnings('ignore:"m_neighbors" is deprecated in 0.4 and')
@pytest.mark.filterwarnings("ignore:'y' should be of types")
def test_all_estimator_no_base_class():
# test that all_estimators doesn't find abstract classes.
for name, Estimator in all_estimators():
msg = ("Base estimators such as {0} should not be included"
" in all_estimators").format(name)
assert_false(name.lower().startswith('base'), msg=msg)
def _tested_non_meta_estimators():
for name, Estimator in all_estimators():
if name.startswith("_"):
continue
yield name, Estimator
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.estimator_checks import parametrize_with_checks as \
parametrize_with_checks_sklearn
from sklearn.utils.estimator_checks import _construct_instance
from sklearn.utils._testing import ignore_warnings
from sklearn.utils._testing import set_random_state
from sklearn.utils._testing import SkipTest
from imblearn.utils.estimator_checks import parametrize_with_checks
from imblearn.utils.estimator_checks import _set_checking_parameters
from imblearn.utils.estimator_checks import _yield_all_checks
from imblearn.utils.testing import all_estimators
from imblearn.under_sampling import NearMiss
@pytest.mark.parametrize("name, Estimator", all_estimators())
def test_all_estimator_no_base_class(name, Estimator):
# test that all_estimators doesn't find abstract classes.
msg = (f"Base estimators such as {name} should not be included"
f" in all_estimators")
assert not name.lower().startswith("base"), msg
def _tested_estimators():
for name, Estimator in all_estimators():
try:
estimator = _construct_instance(Estimator)
set_random_state(estimator)
except SkipTest:
continue
def valid_components(self):
if not hasattr(self, "valid_components_"):
self.valid_components_ = np.array(all_estimators())
return self.valid_components_
