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"""
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The :mod:`sklearn.experimental` module provides importable modules that enable
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the use of experimental features or estimators.
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The features and estimators that are experimental aren't subject to
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deprecation cycles. Use them at your own risks!
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"""
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"""Enables Successive Halving search-estimators
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The API and results of these estimators might change without any deprecation
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cycle.
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Importing this file dynamically sets the
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:class:`~sklearn.model_selection.HalvingRandomSearchCV` and
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:class:`~sklearn.model_selection.HalvingGridSearchCV` as attributes of the
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`model_selection` module::
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>>> # explicitly require this experimental feature
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>>> from sklearn.experimental import enable_halving_search_cv # noqa
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>>> # now you can import normally from model_selection
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>>> from sklearn.model_selection import HalvingRandomSearchCV
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>>> from sklearn.model_selection import HalvingGridSearchCV
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The ``# noqa`` comment comment can be removed: it just tells linters like
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flake8 to ignore the import, which appears as unused.
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"""
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from ..model_selection._search_successive_halving import (
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HalvingRandomSearchCV,
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HalvingGridSearchCV,
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)
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from .. import model_selection
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# use settattr to avoid mypy errors when monkeypatching
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setattr(model_selection, "HalvingRandomSearchCV", HalvingRandomSearchCV)
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setattr(model_selection, "HalvingGridSearchCV", HalvingGridSearchCV)
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model_selection.__all__ += ["HalvingRandomSearchCV", "HalvingGridSearchCV"]
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"""This is now a no-op and can be safely removed from your code.
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It used to enable the use of
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:class:`~sklearn.ensemble.HistGradientBoostingClassifier` and
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:class:`~sklearn.ensemble.HistGradientBoostingRegressor` when they were still
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:term:`experimental`, but these estimators are now stable and can be imported
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normally from `sklearn.ensemble`.
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"""
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# Don't remove this file, we don't want to break users code just because the
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# feature isn't experimental anymore.
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import warnings
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warnings.warn(
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"Since version 1.0, "
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"it is not needed to import enable_hist_gradient_boosting anymore. "
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"HistGradientBoostingClassifier and HistGradientBoostingRegressor are now "
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"stable and can be normally imported from sklearn.ensemble."
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)
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"""Enables IterativeImputer
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The API and results of this estimator might change without any deprecation
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cycle.
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Importing this file dynamically sets :class:`~sklearn.impute.IterativeImputer`
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as an attribute of the impute module::
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>>> # explicitly require this experimental feature
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>>> from sklearn.experimental import enable_iterative_imputer # noqa
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>>> # now you can import normally from impute
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>>> from sklearn.impute import IterativeImputer
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"""
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from ..impute._iterative import IterativeImputer
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from .. import impute
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# use settattr to avoid mypy errors when monkeypatching
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setattr(impute, "IterativeImputer", IterativeImputer)
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impute.__all__ += ["IterativeImputer"]
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"""Tests for making sure experimental imports work as expected."""
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import textwrap
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from sklearn.utils._testing import assert_run_python_script
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def test_import_raises_warning():
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code = """
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import pytest
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with pytest.warns(UserWarning, match="it is not needed to import"):
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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"""
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assert_run_python_script(textwrap.dedent(code))
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"""Tests for making sure experimental imports work as expected."""
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import textwrap
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from sklearn.utils._testing import assert_run_python_script
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def test_imports_strategies():
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# Make sure different import strategies work or fail as expected.
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# Since Python caches the imported modules, we need to run a child process
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# for every test case. Else, the tests would not be independent
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# (manually removing the imports from the cache (sys.modules) is not
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# recommended and can lead to many complications).
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good_import = """
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from sklearn.experimental import enable_iterative_imputer
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from sklearn.impute import IterativeImputer
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"""
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assert_run_python_script(textwrap.dedent(good_import))
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good_import_with_ensemble_first = """
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import sklearn.ensemble
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from sklearn.experimental import enable_iterative_imputer
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from sklearn.impute import IterativeImputer
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"""
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assert_run_python_script(textwrap.dedent(good_import_with_ensemble_first))
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bad_imports = """
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import pytest
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with pytest.raises(ImportError, match='IterativeImputer is experimental'):
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from sklearn.impute import IterativeImputer
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import sklearn.experimental
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with pytest.raises(ImportError, match='IterativeImputer is experimental'):
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from sklearn.impute import IterativeImputer
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"""
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assert_run_python_script(textwrap.dedent(bad_imports))
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"""Tests for making sure experimental imports work as expected."""
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import textwrap
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from sklearn.utils._testing import assert_run_python_script
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def test_imports_strategies():
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# Make sure different import strategies work or fail as expected.
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# Since Python caches the imported modules, we need to run a child process
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# for every test case. Else, the tests would not be independent
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# (manually removing the imports from the cache (sys.modules) is not
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# recommended and can lead to many complications).
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good_import = """
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from sklearn.experimental import enable_halving_search_cv
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from sklearn.model_selection import HalvingGridSearchCV
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from sklearn.model_selection import HalvingRandomSearchCV
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"""
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assert_run_python_script(textwrap.dedent(good_import))
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good_import_with_model_selection_first = """
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import sklearn.model_selection
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from sklearn.experimental import enable_halving_search_cv
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from sklearn.model_selection import HalvingGridSearchCV
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from sklearn.model_selection import HalvingRandomSearchCV
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"""
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assert_run_python_script(textwrap.dedent(good_import_with_model_selection_first))
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bad_imports = """
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import pytest
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with pytest.raises(ImportError, match='HalvingGridSearchCV is experimental'):
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from sklearn.model_selection import HalvingGridSearchCV
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import sklearn.experimental
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with pytest.raises(ImportError, match='HalvingRandomSearchCV is experimental'):
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from sklearn.model_selection import HalvingRandomSearchCV
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"""
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assert_run_python_script(textwrap.dedent(bad_imports))
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