362 lines
12 KiB
Python
362 lines
12 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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# make it possible to discover experimental estimators when calling `all_estimators`
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from sklearn.experimental import enable_iterative_imputer # noqa
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from sklearn.experimental import enable_halving_search_cv # noqa
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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.fixes import sp_version, parse_version
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from sklearn.utils.deprecation import _is_deprecated
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from sklearn.datasets import make_classification
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import FunctionTransformer
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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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# mypy error: Module has no attribute "__path__"
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sklearn_path = sklearn.__path__ # type: ignore # mypy issue #1422
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PUBLIC_MODULES = set(
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[
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pckg[1]
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for pckg in walk_packages(prefix="sklearn.", path=sklearn_path)
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if not ("._" in pckg[1] or ".tests." in pckg[1])
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]
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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(
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"numpydoc", reason="numpydoc is required to test the docstrings"
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)
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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.endswith(".conftest"):
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# pytest tooling, not part of the scikit-learn API
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continue
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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 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(
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"Error for __init__ of %s in %s:\n%s" % (cls, name, w[0])
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)
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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(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 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(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 not _is_deprecated(
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func
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):
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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__, prefix="sklearn."):
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if IS_PYPY and (
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"_svmlight_format_io" in modname
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or "feature_extraction._hashing_fast" in modname
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):
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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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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, (
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'"%s" has tabs, please remove them ',
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"or add it to the ignore list" % modname,
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)
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def _construct_searchcv_instance(SearchCV):
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return SearchCV(LogisticRegression(), {"C": [0.1, 1]})
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def _construct_compose_pipeline_instance(Estimator):
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# Minimal / degenerate instances: only useful to test the docstrings.
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if Estimator.__name__ == "ColumnTransformer":
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return Estimator(transformers=[("transformer", "passthrough", [0, 1])])
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elif Estimator.__name__ == "Pipeline":
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return Estimator(steps=[("clf", LogisticRegression())])
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elif Estimator.__name__ == "FeatureUnion":
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return Estimator(transformer_list=[("transformer", FunctionTransformer())])
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def _construct_sparse_coder(Estimator):
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# XXX: hard-coded assumption that n_features=3
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dictionary = np.array(
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[[0, 1, 0], [-1, -1, 2], [1, 1, 1], [0, 1, 1], [0, 2, 1]],
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dtype=np.float64,
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)
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return Estimator(dictionary=dictionary)
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@pytest.mark.parametrize("name, Estimator", 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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if Estimator.__name__ in (
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"HalvingRandomSearchCV",
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"RandomizedSearchCV",
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"HalvingGridSearchCV",
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"GridSearchCV",
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):
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est = _construct_searchcv_instance(Estimator)
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elif Estimator.__name__ in (
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"ColumnTransformer",
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"Pipeline",
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"FeatureUnion",
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):
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est = _construct_compose_pipeline_instance(Estimator)
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elif Estimator.__name__ == "SparseCoder":
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est = _construct_sparse_coder(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.set_params(k=2)
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elif Estimator.__name__ == "DummyClassifier":
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est.set_params(strategy="stratified")
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elif Estimator.__name__ == "CCA" or Estimator.__name__.startswith("PLS"):
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# default = 2 is invalid for single target
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est.set_params(n_components=1)
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elif Estimator.__name__ in (
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"GaussianRandomProjection",
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"SparseRandomProjection",
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):
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# default="auto" raises an error with the shape of `X`
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est.set_params(n_components=2)
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elif Estimator.__name__ == "TSNE":
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# default raises an error, perplexity must be less than n_samples
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est.set_params(perplexity=2)
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# FIXME: TO BE REMOVED for 1.3 (avoid FutureWarning)
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if Estimator.__name__ == "SequentialFeatureSelector":
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est.set_params(n_features_to_select="auto")
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# FIXME: TO BE REMOVED for 1.3 (avoid FutureWarning)
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if Estimator.__name__ == "FastICA":
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est.set_params(whiten="unit-variance")
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# FIXME: TO BE REMOVED for 1.3 (avoid FutureWarning)
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if Estimator.__name__ == "MiniBatchDictionaryLearning":
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est.set_params(batch_size=5)
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# TODO(1.4): TO BE REMOVED for 1.4 (avoid FutureWarning)
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if Estimator.__name__ in ("KMeans", "MiniBatchKMeans"):
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est.set_params(n_init="auto")
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# TODO(1.4): TO BE REMOVED for 1.4 (avoid FutureWarning)
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if Estimator.__name__ in (
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"MultinomialNB",
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"ComplementNB",
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"BernoulliNB",
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"CategoricalNB",
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):
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est.set_params(force_alpha=True)
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if Estimator.__name__ == "QuantileRegressor":
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solver = "highs" if sp_version >= parse_version("1.6.0") else "interior-point"
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est.set_params(solver=solver)
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# TODO(1.4): TO BE REMOVED for 1.4 (avoid FutureWarning)
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if Estimator.__name__ == "MDS":
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est.set_params(normalized_stress="auto")
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# In case we want to deprecate some attributes in the future
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skipped_attributes = {}
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if Estimator.__name__.endswith("Vectorizer"):
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# Vectorizer require some specific input data
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if Estimator.__name__ in (
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"CountVectorizer",
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"HashingVectorizer",
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"TfidfVectorizer",
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):
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X = [
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"This is the first document.",
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"This document is the second document.",
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"And this is the third one.",
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"Is this the first document?",
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]
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elif Estimator.__name__ == "DictVectorizer":
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X = [{"foo": 1, "bar": 2}, {"foo": 3, "baz": 1}]
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y = None
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else:
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X, y = make_classification(
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n_samples=20,
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n_features=3,
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n_redundant=0,
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n_classes=2,
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random_state=2,
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)
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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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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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fit_attr = _get_all_fitted_attributes(est)
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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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if undocumented_attrs:
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raise AssertionError(
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f"Undocumented attributes for {Estimator.__name__}: {undocumented_attrs}"
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)
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def _get_all_fitted_attributes(estimator):
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"Get all the fitted attributes of an estimator including properties"
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# attributes
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fit_attr = list(estimator.__dict__.keys())
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# properties
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with warnings.catch_warnings():
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warnings.filterwarnings("error", category=FutureWarning)
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for name in dir(estimator.__class__):
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obj = getattr(estimator.__class__, name)
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if not isinstance(obj, property):
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continue
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# ignore properties that raises an AttributeError and deprecated
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# properties
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try:
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getattr(estimator, name)
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except (AttributeError, FutureWarning):
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continue
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fit_attr.append(name)
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return [k for k in fit_attr if k.endswith("_") and not k.startswith("_")]
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