# Authors: Alexandre Gramfort # Raghav RV # License: BSD 3 clause import inspect import warnings import importlib from pkgutil import walk_packages from inspect import signature import numpy as np import sklearn from sklearn.utils import IS_PYPY from sklearn.utils._testing import check_docstring_parameters from sklearn.utils._testing import _get_func_name from sklearn.utils._testing import ignore_warnings from sklearn.utils._testing import all_estimators from sklearn.utils.estimator_checks import _enforce_estimator_tags_y from sklearn.utils.estimator_checks import _enforce_estimator_tags_x from sklearn.utils.deprecation import _is_deprecated from sklearn.externals._pep562 import Pep562 from sklearn.datasets import make_classification import pytest # walk_packages() ignores DeprecationWarnings, now we need to ignore # FutureWarnings with warnings.catch_warnings(): warnings.simplefilter('ignore', FutureWarning) PUBLIC_MODULES = set([ pckg[1] for pckg in walk_packages( prefix='sklearn.', # mypy error: Module has no attribute "__path__" path=sklearn.__path__) # type: ignore # mypy issue #1422 if not ("._" in pckg[1] or ".tests." in pckg[1]) ]) # functions to ignore args / docstring of _DOCSTRING_IGNORES = [ 'sklearn.utils.deprecation.load_mlcomp', 'sklearn.pipeline.make_pipeline', 'sklearn.pipeline.make_union', 'sklearn.utils.extmath.safe_sparse_dot', 'sklearn.utils._joblib' ] # Methods where y param should be ignored if y=None by default _METHODS_IGNORE_NONE_Y = [ 'fit', 'score', 'fit_predict', 'fit_transform', 'partial_fit', 'predict' ] # numpydoc 0.8.0's docscrape tool raises because of collections.abc under # Python 3.7 @pytest.mark.filterwarnings('ignore::FutureWarning') @pytest.mark.filterwarnings('ignore::DeprecationWarning') @pytest.mark.skipif(IS_PYPY, reason='test segfaults on PyPy') def test_docstring_parameters(): # Test module docstring formatting # Skip test if numpydoc is not found pytest.importorskip('numpydoc', reason="numpydoc is required to test the docstrings") # XXX unreached code as of v0.22 from numpydoc import docscrape incorrect = [] for name in PUBLIC_MODULES: if name == 'sklearn.utils.fixes': # We cannot always control these docstrings continue with warnings.catch_warnings(record=True): module = importlib.import_module(name) classes = inspect.getmembers(module, inspect.isclass) # Exclude imported classes classes = [cls for cls in classes if cls[1].__module__ == name] for cname, cls in classes: this_incorrect = [] if cname in _DOCSTRING_IGNORES or cname.startswith('_'): continue if inspect.isabstract(cls): continue with warnings.catch_warnings(record=True) as w: cdoc = docscrape.ClassDoc(cls) if len(w): raise RuntimeError('Error for __init__ of %s in %s:\n%s' % (cls, name, w[0])) cls_init = getattr(cls, '__init__', None) if _is_deprecated(cls_init): continue elif cls_init is not None: this_incorrect += check_docstring_parameters( cls.__init__, cdoc) for method_name in cdoc.methods: method = getattr(cls, method_name) if _is_deprecated(method): continue param_ignore = None # Now skip docstring test for y when y is None # by default for API reason if method_name in _METHODS_IGNORE_NONE_Y: sig = signature(method) if ('y' in sig.parameters and sig.parameters['y'].default is None): param_ignore = ['y'] # ignore y for fit and score result = check_docstring_parameters( method, ignore=param_ignore) this_incorrect += result incorrect += this_incorrect functions = inspect.getmembers(module, inspect.isfunction) # Exclude imported functions functions = [fn for fn in functions if fn[1].__module__ == name] for fname, func in functions: # Don't test private methods / functions if fname.startswith('_'): continue if fname == "configuration" and name.endswith("setup"): continue name_ = _get_func_name(func) if (not any(d in name_ for d in _DOCSTRING_IGNORES) and not _is_deprecated(func)): incorrect += check_docstring_parameters(func) msg = '\n'.join(incorrect) if len(incorrect) > 0: raise AssertionError("Docstring Error:\n" + msg) @ignore_warnings(category=FutureWarning) def test_tabs(): # Test that there are no tabs in our source files for importer, modname, ispkg in walk_packages(sklearn.__path__, prefix='sklearn.'): if IS_PYPY and ('_svmlight_format_io' in modname or 'feature_extraction._hashing_fast' in modname): continue # because we don't import mod = importlib.import_module(modname) # TODO: Remove when minimum python version is 3.7 # unwrap to get module because Pep562 backport wraps the original # module if isinstance(mod, Pep562): mod = mod._module try: source = inspect.getsource(mod) except IOError: # user probably should have run "make clean" continue assert '\t' not in source, ('"%s" has tabs, please remove them ', 'or add it to the ignore list' % modname) @pytest.mark.parametrize('name, Estimator', all_estimators()) def test_fit_docstring_attributes(name, Estimator): pytest.importorskip('numpydoc') from numpydoc import docscrape doc = docscrape.ClassDoc(Estimator) attributes = doc['Attributes'] IGNORED = {'ClassifierChain', 'ColumnTransformer', 'CountVectorizer', 'DictVectorizer', 'FeatureUnion', 'GaussianRandomProjection', 'GridSearchCV', 'MultiOutputClassifier', 'MultiOutputRegressor', 'NoSampleWeightWrapper', 'OneVsOneClassifier', 'OneVsRestClassifier', 'OutputCodeClassifier', 'Pipeline', 'RFE', 'RFECV', 'RandomizedSearchCV', 'RegressorChain', 'SelectFromModel', 'SparseCoder', 'SparseRandomProjection', 'SpectralBiclustering', 'StackingClassifier', 'StackingRegressor', 'TfidfVectorizer', 'VotingClassifier', 'VotingRegressor'} if Estimator.__name__ in IGNORED or Estimator.__name__.startswith('_'): pytest.skip("Estimator cannot be fit easily to test fit attributes") est = Estimator() if Estimator.__name__ == 'SelectKBest': est.k = 2 if Estimator.__name__ == 'DummyClassifier': est.strategy = "stratified" # TO BE REMOVED for v0.25 (avoid FutureWarning) if Estimator.__name__ == 'AffinityPropagation': est.random_state = 63 X, y = make_classification(n_samples=20, n_features=3, n_redundant=0, n_classes=2, random_state=2) y = _enforce_estimator_tags_y(est, y) X = _enforce_estimator_tags_x(est, X) if '1dlabels' in est._get_tags()['X_types']: est.fit(y) elif '2dlabels' in est._get_tags()['X_types']: est.fit(np.c_[y, y]) else: est.fit(X, y) skipped_attributes = {'n_features_in_'} for attr in attributes: if attr.name in skipped_attributes: continue desc = ' '.join(attr.desc).lower() # As certain attributes are present "only" if a certain parameter is # provided, this checks if the word "only" is present in the attribute # description, and if not the attribute is required to be present. if 'only ' not in desc: assert hasattr(est, attr.name) IGNORED = {'BayesianRidge', 'Birch', 'CCA', 'CategoricalNB', 'ElasticNet', 'ElasticNetCV', 'GaussianProcessClassifier', 'GradientBoostingRegressor', 'HistGradientBoostingClassifier', 'HistGradientBoostingRegressor', 'IsolationForest', 'KNeighborsClassifier', 'KNeighborsRegressor', 'KNeighborsTransformer', 'KernelCenterer', 'KernelDensity', 'LarsCV', 'Lasso', 'LassoLarsCV', 'LassoLarsIC', 'LatentDirichletAllocation', 'LocalOutlierFactor', 'MDS', 'MiniBatchKMeans', 'MLPClassifier', 'MLPRegressor', 'MultiTaskElasticNet', 'MultiTaskElasticNetCV', 'MultiTaskLasso', 'MultiTaskLassoCV', 'NearestNeighbors', 'NuSVR', 'OneClassSVM', 'OrthogonalMatchingPursuit', 'PLSCanonical', 'PLSRegression', 'PLSSVD', 'PassiveAggressiveClassifier', 'Perceptron', 'RBFSampler', 'RadiusNeighborsClassifier', 'RadiusNeighborsRegressor', 'RadiusNeighborsTransformer', 'RandomTreesEmbedding', 'SVR', 'SkewedChi2Sampler'} if Estimator.__name__ in IGNORED: pytest.xfail( reason="Classifier has too many undocumented attributes.") fit_attr = [k for k in est.__dict__.keys() if k.endswith('_') and not k.startswith('_')] fit_attr_names = [attr.name for attr in attributes] undocumented_attrs = set(fit_attr).difference(fit_attr_names) undocumented_attrs = set(undocumented_attrs).difference(skipped_attributes) assert not undocumented_attrs,\ "Undocumented attributes: {}".format(undocumented_attrs)