""" General tests for all estimators in sklearn. """ # Authors: Andreas Mueller # Gael Varoquaux gael.varoquaux@normalesup.org # License: BSD 3 clause import os import warnings import sys import re import pkgutil from inspect import isgenerator from functools import partial import pytest from sklearn.utils import all_estimators from sklearn.utils._testing import ignore_warnings from sklearn.exceptions import ConvergenceWarning from sklearn.utils.estimator_checks import check_estimator import sklearn from sklearn.base import BiclusterMixin from sklearn.linear_model._base import LinearClassifierMixin from sklearn.linear_model import LogisticRegression from sklearn.model_selection import GridSearchCV from sklearn.utils import IS_PYPY from sklearn.utils._testing import SkipTest from sklearn.utils.estimator_checks import ( _mark_xfail_checks, _construct_instance, _set_checking_parameters, _set_check_estimator_ids, check_parameters_default_constructible, check_class_weight_balanced_linear_classifier, parametrize_with_checks) 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 @ignore_warnings("Passing a class is depr", category=FutureWarning) # 0.24 def test_estimator_cls_parameterize_with_checks(): # TODO: remove test in 0.24 # Non-regression test for #16707 to ensure that parametrize_with_checks # works with estimator classes param_checks = parametrize_with_checks([LogisticRegression]) # Using the generator does not raise list(param_checks.args[1]) def test_mark_xfail_checks_with_unconsructable_estimator(): class MyEstimator: def __init__(self): raise ValueError("This is bad") estimator, check = _mark_xfail_checks(MyEstimator, 42, None) assert estimator == MyEstimator assert check == 42 @pytest.mark.parametrize( 'name, Estimator', all_estimators() ) def test_parameters_default_constructible(name, Estimator): # Test that estimators are default-constructible check_parameters_default_constructible(name, Estimator) def _sample_func(x, y=1): pass @pytest.mark.parametrize("val, expected", [ (partial(_sample_func, y=1), "_sample_func(y=1)"), (_sample_func, "_sample_func"), (partial(_sample_func, 'world'), "_sample_func"), (LogisticRegression(C=2.0), "LogisticRegression(C=2.0)"), (LogisticRegression(random_state=1, solver='newton-cg', class_weight='balanced', warm_start=True), "LogisticRegression(class_weight='balanced',random_state=1," "solver='newton-cg',warm_start=True)") ]) def test_set_check_estimator_ids(val, expected): assert _set_check_estimator_ids(val) == expected def _tested_estimators(): for name, Estimator in all_estimators(): if issubclass(Estimator, BiclusterMixin): continue try: estimator = _construct_instance(Estimator) except SkipTest: continue yield estimator @parametrize_with_checks(list(_tested_estimators())) def test_estimators(estimator, check, request): # Common tests for estimator instances with ignore_warnings(category=(FutureWarning, ConvergenceWarning, UserWarning, FutureWarning)): _set_checking_parameters(estimator) check(estimator) @ignore_warnings("Passing a class is depr", category=FutureWarning) # 0.24 def test_check_estimator_generate_only(): # TODO in 0.24: remove checks on passing a class estimator_cls_gen_checks = check_estimator(LogisticRegression, generate_only=True) all_instance_gen_checks = check_estimator(LogisticRegression(), generate_only=True) assert isgenerator(estimator_cls_gen_checks) assert isgenerator(all_instance_gen_checks) estimator_cls_checks = list(estimator_cls_gen_checks) all_instance_checks = list(all_instance_gen_checks) # all classes checks include check_parameters_default_constructible assert len(estimator_cls_checks) == len(all_instance_checks) + 1 # TODO: meta-estimators like GridSearchCV has required parameters # that do not have default values. This is expected to change in the future with pytest.raises(SkipTest): for estimator, check in check_estimator(GridSearchCV, generate_only=True): check(estimator) @ignore_warnings(category=(DeprecationWarning, FutureWarning)) # ignore deprecated open(.., 'U') in numpy distutils def test_configure(): # Smoke test the 'configure' step of setup, this tests all the # 'configure' functions in the setup.pys in scikit-learn # This test requires Cython which is not necessarily there when running # the tests of an installed version of scikit-learn or when scikit-learn # is installed in editable mode by pip build isolation enabled. pytest.importorskip("Cython") cwd = os.getcwd() setup_path = os.path.abspath(os.path.join(sklearn.__path__[0], '..')) setup_filename = os.path.join(setup_path, 'setup.py') if not os.path.exists(setup_filename): pytest.skip('setup.py not available') # XXX unreached code as of v0.22 try: os.chdir(setup_path) old_argv = sys.argv sys.argv = ['setup.py', 'config'] with warnings.catch_warnings(): # The configuration spits out warnings when not finding # Blas/Atlas development headers warnings.simplefilter('ignore', UserWarning) with open('setup.py') as f: exec(f.read(), dict(__name__='__main__')) finally: sys.argv = old_argv os.chdir(cwd) def _tested_linear_classifiers(): classifiers = all_estimators(type_filter='classifier') with warnings.catch_warnings(record=True): for name, clazz in classifiers: required_parameters = getattr(clazz, "_required_parameters", []) if len(required_parameters): # FIXME continue if ('class_weight' in clazz().get_params().keys() and issubclass(clazz, LinearClassifierMixin)): yield name, clazz @pytest.mark.parametrize("name, Classifier", _tested_linear_classifiers()) def test_class_weight_balanced_linear_classifiers(name, Classifier): check_class_weight_balanced_linear_classifier(name, Classifier) @ignore_warnings def test_import_all_consistency(): # Smoke test to check that any name in a __all__ list is actually defined # in the namespace of the module or package. pkgs = pkgutil.walk_packages(path=sklearn.__path__, prefix='sklearn.', onerror=lambda _: None) submods = [modname for _, modname, _ in pkgs] for modname in submods + ['sklearn']: if ".tests." in modname: continue if IS_PYPY and ('_svmlight_format_io' in modname or 'feature_extraction._hashing_fast' in modname): continue package = __import__(modname, fromlist="dummy") for name in getattr(package, '__all__', ()): assert hasattr(package, name),\ "Module '{0}' has no attribute '{1}'".format(modname, name) def test_root_import_all_completeness(): EXCEPTIONS = ('utils', 'tests', 'base', 'setup', 'conftest') for _, modname, _ in pkgutil.walk_packages(path=sklearn.__path__, onerror=lambda _: None): if '.' in modname or modname.startswith('_') or modname in EXCEPTIONS: continue assert modname in sklearn.__all__ def test_all_tests_are_importable(): # Ensure that for each contentful subpackage, there is a test directory # within it that is also a subpackage (i.e. a directory with __init__.py) HAS_TESTS_EXCEPTIONS = re.compile(r'''(?x) \.externals(\.|$)| \.tests(\.|$)| \._ ''') lookup = {name: ispkg for _, name, ispkg in pkgutil.walk_packages(sklearn.__path__, prefix='sklearn.')} missing_tests = [name for name, ispkg in lookup.items() if ispkg and not HAS_TESTS_EXCEPTIONS.search(name) and name + '.tests' not in lookup] assert missing_tests == [], ('{0} do not have `tests` subpackages. ' 'Perhaps they require ' '__init__.py or an add_subpackage directive ' 'in the parent ' 'setup.py'.format(missing_tests)) # TODO: remove in 0.24 def test_class_support_deprecated(): # Make sure passing classes to check_estimator or parametrize_with_checks # is deprecated msg = "Passing a class is deprecated" with pytest.warns(FutureWarning, match=msg): check_estimator(LogisticRegression) with pytest.warns(FutureWarning, match=msg): parametrize_with_checks([LogisticRegression]) # Make sure check_parameters_default_constructible accepts instances now check_parameters_default_constructible('name', LogisticRegression())