306 lines
10 KiB
Python
306 lines
10 KiB
Python
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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Raghav RV <rvraghav93@gmail.com>
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# License: BSD 3 clause
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import inspect
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import warnings
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import importlib
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from pkgutil import walk_packages
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from inspect import signature
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import numpy as np
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import sklearn
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from sklearn.utils import IS_PYPY
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from sklearn.utils._testing import check_docstring_parameters
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from sklearn.utils._testing import _get_func_name
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from sklearn.utils._testing import ignore_warnings
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from sklearn.utils import all_estimators
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from sklearn.utils.estimator_checks import _enforce_estimator_tags_y
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from sklearn.utils.estimator_checks import _enforce_estimator_tags_x
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from sklearn.utils.estimator_checks import _construct_instance
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from sklearn.utils.deprecation import _is_deprecated
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from sklearn.externals._pep562 import Pep562
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from sklearn.datasets import make_classification
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from sklearn.linear_model import LogisticRegression
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import pytest
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# walk_packages() ignores DeprecationWarnings, now we need to ignore
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# FutureWarnings
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with warnings.catch_warnings():
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warnings.simplefilter('ignore', FutureWarning)
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PUBLIC_MODULES = set([
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pckg[1] for pckg in walk_packages(
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prefix='sklearn.',
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# mypy error: Module has no attribute "__path__"
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path=sklearn.__path__) # type: ignore # mypy issue #1422
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if not ("._" in pckg[1] or ".tests." in pckg[1])
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])
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# functions to ignore args / docstring of
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_DOCSTRING_IGNORES = [
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'sklearn.utils.deprecation.load_mlcomp',
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'sklearn.pipeline.make_pipeline',
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'sklearn.pipeline.make_union',
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'sklearn.utils.extmath.safe_sparse_dot',
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'sklearn.utils._joblib'
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]
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# Methods where y param should be ignored if y=None by default
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_METHODS_IGNORE_NONE_Y = [
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'fit',
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'score',
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'fit_predict',
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'fit_transform',
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'partial_fit',
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'predict'
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]
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# numpydoc 0.8.0's docscrape tool raises because of collections.abc under
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# Python 3.7
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@pytest.mark.filterwarnings('ignore::FutureWarning')
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@pytest.mark.filterwarnings('ignore::DeprecationWarning')
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@pytest.mark.skipif(IS_PYPY, reason='test segfaults on PyPy')
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def test_docstring_parameters():
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# Test module docstring formatting
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# Skip test if numpydoc is not found
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pytest.importorskip('numpydoc',
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reason="numpydoc is required to test the docstrings")
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# XXX unreached code as of v0.22
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from numpydoc import docscrape
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incorrect = []
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for name in PUBLIC_MODULES:
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if name == 'sklearn.utils.fixes':
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# We cannot always control these docstrings
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continue
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with warnings.catch_warnings(record=True):
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module = importlib.import_module(name)
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classes = inspect.getmembers(module, inspect.isclass)
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# Exclude non-scikit-learn classes
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classes = [cls for cls in classes
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if cls[1].__module__.startswith('sklearn')]
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for cname, cls in classes:
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this_incorrect = []
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if cname in _DOCSTRING_IGNORES or cname.startswith('_'):
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continue
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if inspect.isabstract(cls):
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continue
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with warnings.catch_warnings(record=True) as w:
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cdoc = docscrape.ClassDoc(cls)
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if len(w):
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raise RuntimeError('Error for __init__ of %s in %s:\n%s'
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% (cls, name, w[0]))
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cls_init = getattr(cls, '__init__', None)
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if _is_deprecated(cls_init):
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continue
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elif cls_init is not None:
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this_incorrect += check_docstring_parameters(
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cls.__init__, cdoc)
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for method_name in cdoc.methods:
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method = getattr(cls, method_name)
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if _is_deprecated(method):
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continue
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param_ignore = None
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# Now skip docstring test for y when y is None
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# by default for API reason
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if method_name in _METHODS_IGNORE_NONE_Y:
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sig = signature(method)
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if ('y' in sig.parameters and
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sig.parameters['y'].default is None):
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param_ignore = ['y'] # ignore y for fit and score
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result = check_docstring_parameters(
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method, ignore=param_ignore)
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this_incorrect += result
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incorrect += this_incorrect
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functions = inspect.getmembers(module, inspect.isfunction)
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# Exclude imported functions
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functions = [fn for fn in functions if fn[1].__module__ == name]
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for fname, func in functions:
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# Don't test private methods / functions
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if fname.startswith('_'):
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continue
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if fname == "configuration" and name.endswith("setup"):
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continue
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name_ = _get_func_name(func)
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if (not any(d in name_ for d in _DOCSTRING_IGNORES) and
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not _is_deprecated(func)):
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incorrect += check_docstring_parameters(func)
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msg = '\n'.join(incorrect)
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if len(incorrect) > 0:
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raise AssertionError("Docstring Error:\n" + msg)
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@ignore_warnings(category=FutureWarning)
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def test_tabs():
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# Test that there are no tabs in our source files
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for importer, modname, ispkg in walk_packages(sklearn.__path__,
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prefix='sklearn.'):
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if IS_PYPY and ('_svmlight_format_io' in modname or
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'feature_extraction._hashing_fast' in modname):
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continue
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# because we don't import
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mod = importlib.import_module(modname)
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# TODO: Remove when minimum python version is 3.7
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# unwrap to get module because Pep562 backport wraps the original
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# module
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if isinstance(mod, Pep562):
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mod = mod._module
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try:
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source = inspect.getsource(mod)
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except IOError: # user probably should have run "make clean"
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continue
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assert '\t' not in source, ('"%s" has tabs, please remove them ',
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'or add it to the ignore list'
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% modname)
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def _construct_searchcv_instance(SearchCV):
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return SearchCV(LogisticRegression(), {"C": [0.1, 1]})
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N_FEATURES_MODULES_TO_IGNORE = {
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'calibration',
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'cluster',
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'compose',
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'covariance',
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'decomposition',
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'discriminant_analysis',
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'dummy',
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'ensemble',
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'feature_extraction',
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'feature_selection',
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'gaussian_process',
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'impute',
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'isotonic',
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'kernel_approximation',
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'kernel_ridge',
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'linear_model',
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'manifold',
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'mixture',
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'model_selection',
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'multiclass',
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'multioutput',
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'naive_bayes',
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'neighbors',
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'neural_network',
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'pipeline',
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'preprocessing',
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'random_projection',
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'semi_supervised',
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'svm',
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'tree'
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}
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@pytest.mark.parametrize('name, Estimator',
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all_estimators())
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def test_fit_docstring_attributes(name, Estimator):
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pytest.importorskip('numpydoc')
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from numpydoc import docscrape
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doc = docscrape.ClassDoc(Estimator)
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attributes = doc['Attributes']
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IGNORED = {'ClassifierChain', 'ColumnTransformer',
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'CountVectorizer', 'DictVectorizer', 'FeatureUnion',
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'GaussianRandomProjection',
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'MultiOutputClassifier', 'MultiOutputRegressor',
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'NoSampleWeightWrapper', 'OneVsOneClassifier',
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'OutputCodeClassifier', 'Pipeline', 'RFE', 'RFECV',
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'RegressorChain', 'SelectFromModel',
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'SparseCoder', 'SparseRandomProjection',
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'SpectralBiclustering', 'StackingClassifier',
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'StackingRegressor', 'TfidfVectorizer', 'VotingClassifier',
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'VotingRegressor', 'SequentialFeatureSelector',
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'HalvingGridSearchCV', 'HalvingRandomSearchCV'}
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if Estimator.__name__ in IGNORED or Estimator.__name__.startswith('_'):
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pytest.skip("Estimator cannot be fit easily to test fit attributes")
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if Estimator.__name__ in ("RandomizedSearchCV", "GridSearchCV"):
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est = _construct_searchcv_instance(Estimator)
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else:
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est = _construct_instance(Estimator)
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if Estimator.__name__ == 'SelectKBest':
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est.k = 2
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if Estimator.__name__ == 'DummyClassifier':
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est.strategy = "stratified"
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if 'PLS' in Estimator.__name__ or 'CCA' in Estimator.__name__:
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est.n_components = 1 # default = 2 is invalid for single target.
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# FIXME: TO BE REMOVED for 1.0 (avoid FutureWarning)
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if Estimator.__name__ == 'AffinityPropagation':
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est.random_state = 63
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# FIXME: TO BE REMOVED for 1.1 (avoid FutureWarning)
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if Estimator.__name__ == 'NMF':
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est.init = 'nndsvda'
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X, y = make_classification(n_samples=20, n_features=3,
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n_redundant=0, n_classes=2,
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random_state=2)
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y = _enforce_estimator_tags_y(est, y)
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X = _enforce_estimator_tags_x(est, X)
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if '1dlabels' in est._get_tags()['X_types']:
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est.fit(y)
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elif '2dlabels' in est._get_tags()['X_types']:
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est.fit(np.c_[y, y])
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else:
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est.fit(X, y)
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skipped_attributes = {'x_scores_', # For PLS, TODO remove in 1.1
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'y_scores_'} # For PLS, TODO remove in 1.1
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module = est.__module__.split(".")[1]
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if module in N_FEATURES_MODULES_TO_IGNORE:
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skipped_attributes.add("n_features_in_")
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for attr in attributes:
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if attr.name in skipped_attributes:
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continue
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desc = ' '.join(attr.desc).lower()
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# As certain attributes are present "only" if a certain parameter is
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# provided, this checks if the word "only" is present in the attribute
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# description, and if not the attribute is required to be present.
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if 'only ' in desc:
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continue
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# ignore deprecation warnings
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with ignore_warnings(category=FutureWarning):
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assert hasattr(est, attr.name)
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IGNORED = {'Birch', 'LarsCV', 'Lasso',
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'OrthogonalMatchingPursuit'}
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if Estimator.__name__ in IGNORED:
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pytest.xfail(
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reason="Estimator has too many undocumented attributes.")
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fit_attr = [k for k in est.__dict__.keys() if k.endswith('_')
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and not k.startswith('_')]
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fit_attr_names = [attr.name for attr in attributes]
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undocumented_attrs = set(fit_attr).difference(fit_attr_names)
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undocumented_attrs = set(undocumented_attrs).difference(skipped_attributes)
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assert not undocumented_attrs,\
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"Undocumented attributes: {}".format(undocumented_attrs)
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