231 lines
8.5 KiB
Python
231 lines
8.5 KiB
Python
"""
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General tests for all estimators in sklearn.
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"""
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# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
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# Gael Varoquaux gael.varoquaux@normalesup.org
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# License: BSD 3 clause
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import os
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import warnings
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import sys
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import re
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import pkgutil
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import functools
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import pytest
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from sklearn.utils.testing import clean_warning_registry
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from sklearn.utils.testing import all_estimators
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_in
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from sklearn.utils.testing import ignore_warnings
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from sklearn.exceptions import ConvergenceWarning, SkipTestWarning
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import sklearn
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from sklearn.base import RegressorMixin
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from sklearn.cluster.bicluster import BiclusterMixin
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from sklearn.linear_model.base import LinearClassifierMixin
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from sklearn.linear_model import Ridge
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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from sklearn.utils import IS_PYPY
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from sklearn.utils.estimator_checks import (
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_yield_all_checks,
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_safe_tags,
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set_checking_parameters,
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check_parameters_default_constructible,
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check_no_attributes_set_in_init,
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check_class_weight_balanced_linear_classifier)
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def test_all_estimator_no_base_class():
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# test that all_estimators doesn't find abstract classes.
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for name, Estimator in all_estimators():
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msg = ("Base estimators such as {0} should not be included"
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" in all_estimators").format(name)
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assert not name.lower().startswith('base'), msg
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@pytest.mark.parametrize(
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'name, Estimator',
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all_estimators()
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)
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def test_parameters_default_constructible(name, Estimator):
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# Test that estimators are default-constructible
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check_parameters_default_constructible(name, Estimator)
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def _tested_estimators():
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for name, Estimator in all_estimators():
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if issubclass(Estimator, BiclusterMixin):
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continue
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if name.startswith("_"):
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continue
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# FIXME _skip_test should be used here (if we could)
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required_parameters = getattr(Estimator, "_required_parameters", [])
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if len(required_parameters):
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if required_parameters in (["estimator"], ["base_estimator"]):
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if issubclass(Estimator, RegressorMixin):
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estimator = Estimator(Ridge())
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else:
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estimator = Estimator(LinearDiscriminantAnalysis())
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else:
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warnings.warn("Can't instantiate estimator {} which requires "
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"parameters {}".format(name,
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required_parameters),
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SkipTestWarning)
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continue
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else:
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estimator = Estimator()
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yield name, estimator
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def _generate_checks_per_estimator(check_generator, estimators):
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with ignore_warnings(category=(DeprecationWarning, FutureWarning)):
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for name, estimator in estimators:
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for check in check_generator(name, estimator):
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yield estimator, check
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def _rename_partial(val):
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if isinstance(val, functools.partial):
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kwstring = "".join(["{}={}".format(k, v)
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for k, v in val.keywords.items()])
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return "{}({})".format(val.func.__name__, kwstring)
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# FIXME once we have short reprs we can use them here!
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if hasattr(val, "get_params") and not isinstance(val, type):
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return type(val).__name__
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@pytest.mark.parametrize(
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"estimator, check",
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_generate_checks_per_estimator(_yield_all_checks,
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_tested_estimators()),
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ids=_rename_partial
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)
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def test_estimators(estimator, check):
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# Common tests for estimator instances
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with ignore_warnings(category=(DeprecationWarning, ConvergenceWarning,
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UserWarning, FutureWarning)):
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set_checking_parameters(estimator)
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name = estimator.__class__.__name__
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check(name, estimator)
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@pytest.mark.parametrize("name, estimator",
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_tested_estimators())
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def test_no_attributes_set_in_init(name, estimator):
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# input validation etc for all estimators
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with ignore_warnings(category=(DeprecationWarning, ConvergenceWarning,
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UserWarning, FutureWarning)):
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tags = _safe_tags(estimator)
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if tags['_skip_test']:
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warnings.warn("Explicit SKIP via _skip_test tag for "
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"{}.".format(name),
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SkipTestWarning)
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return
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# check this on class
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check_no_attributes_set_in_init(name, estimator)
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@ignore_warnings(category=DeprecationWarning)
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# ignore deprecated open(.., 'U') in numpy distutils
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def test_configure():
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# Smoke test the 'configure' step of setup, this tests all the
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# 'configure' functions in the setup.pys in scikit-learn
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cwd = os.getcwd()
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setup_path = os.path.abspath(os.path.join(sklearn.__path__[0], '..'))
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setup_filename = os.path.join(setup_path, 'setup.py')
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if not os.path.exists(setup_filename):
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return
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try:
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os.chdir(setup_path)
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old_argv = sys.argv
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sys.argv = ['setup.py', 'config']
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clean_warning_registry()
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with warnings.catch_warnings():
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# The configuration spits out warnings when not finding
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# Blas/Atlas development headers
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warnings.simplefilter('ignore', UserWarning)
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with open('setup.py') as f:
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exec(f.read(), dict(__name__='__main__'))
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finally:
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sys.argv = old_argv
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os.chdir(cwd)
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def _tested_linear_classifiers():
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classifiers = all_estimators(type_filter='classifier')
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clean_warning_registry()
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with warnings.catch_warnings(record=True):
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for name, clazz in classifiers:
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required_parameters = getattr(clazz, "_required_parameters", [])
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if len(required_parameters):
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# FIXME
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continue
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if ('class_weight' in clazz().get_params().keys() and
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issubclass(clazz, LinearClassifierMixin)):
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yield name, clazz
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@pytest.mark.parametrize("name, Classifier",
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_tested_linear_classifiers())
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def test_class_weight_balanced_linear_classifiers(name, Classifier):
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check_class_weight_balanced_linear_classifier(name, Classifier)
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@ignore_warnings
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def test_import_all_consistency():
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# Smoke test to check that any name in a __all__ list is actually defined
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# in the namespace of the module or package.
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pkgs = pkgutil.walk_packages(path=sklearn.__path__, prefix='sklearn.',
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onerror=lambda _: None)
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submods = [modname for _, modname, _ in pkgs]
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for modname in submods + ['sklearn']:
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if ".tests." in modname:
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continue
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if IS_PYPY and ('_svmlight_format' in modname or
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'feature_extraction._hashing' in modname):
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continue
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package = __import__(modname, fromlist="dummy")
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for name in getattr(package, '__all__', ()):
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if getattr(package, name, None) is None:
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raise AttributeError(
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"Module '{0}' has no attribute '{1}'".format(
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modname, name))
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def test_root_import_all_completeness():
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EXCEPTIONS = ('utils', 'tests', 'base', 'setup')
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for _, modname, _ in pkgutil.walk_packages(path=sklearn.__path__,
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onerror=lambda _: None):
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if '.' in modname or modname.startswith('_') or modname in EXCEPTIONS:
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continue
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assert_in(modname, sklearn.__all__)
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def test_all_tests_are_importable():
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# Ensure that for each contentful subpackage, there is a test directory
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# within it that is also a subpackage (i.e. a directory with __init__.py)
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HAS_TESTS_EXCEPTIONS = re.compile(r'''(?x)
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\.externals(\.|$)|
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\.tests(\.|$)|
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\._
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''')
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lookup = {name: ispkg
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for _, name, ispkg
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in pkgutil.walk_packages(sklearn.__path__, prefix='sklearn.')}
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missing_tests = [name for name, ispkg in lookup.items()
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if ispkg
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and not HAS_TESTS_EXCEPTIONS.search(name)
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and name + '.tests' not in lookup]
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assert_equal(missing_tests, [],
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'{0} do not have `tests` subpackages. Perhaps they require '
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'__init__.py or an add_subpackage directive in the parent '
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'setup.py'.format(missing_tests))
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