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API Make IterativeImputer experimental (scikit-learn#13824)
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‎doc/conf.py

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@@ -263,9 +263,9 @@
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'sphx_glr_plot_compare_methods_001.png': 349}
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# enable experimental module so that the new GBDTs estimators can be
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# enable experimental module so that experimental estimators can be
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# discovered properly by sphinx
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.experimental import * # noqa
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def make_carousel_thumbs(app, exception):

‎doc/modules/classes.rst

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:toctree: generated/
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experimental.enable_hist_gradient_boosting
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experimental.enable_iterative_imputer
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.. _feature_extraction_ref:

‎doc/modules/impute.rst

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@@ -105,7 +105,16 @@ of ``y``. This is done for each feature in an iterative fashion, and then is
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repeated for ``max_iter`` imputation rounds. The results of the final
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imputation round are returned.
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.. note::
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This estimator is still **experimental** for now: the predictions
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and the API might change without any deprecation cycle. To use it,
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you need to explicitly import ``enable_iterative_imputer``.
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::
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>>> import numpy as np
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>>> from sklearn.experimental import enable_iterative_imputer
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>>> from sklearn.impute import IterativeImputer
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>>> imp = IterativeImputer(max_iter=10, random_state=0)
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>>> imp.fit([[1, 2], [3, 6], [4, 8], [np.nan, 3], [7, np.nan]]) # doctest: +NORMALIZE_WHITESPACE

‎doc/whats_new/v0.21.rst

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@@ -216,7 +216,7 @@ Support for Python 3.4 and below has been officially dropped.
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>>> # explicitly require this experimental feature
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>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
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>>> # now you can import normally from ensemble
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>>> # now you can import normally from sklearn.ensemble
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>>> from sklearn.ensemble import HistGradientBoostingClassifier
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:pr:`12807` by :user:`Nicolas Hug<NicolasHug>`.
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:pr:`12177` by :user:`Sergey Feldman <sergeyf>` and :user:`Ben Lawson
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<benlawson>`.
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The API of IterativeImputer is experimental and subject to change without any
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deprecation cycle. To use them, you need to explicitly import
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``enable_iterative_imputer``::
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>>> from sklearn.experimental import enable_iterative_imputer # noqa
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>>> # now you can import normally from sklearn.impute
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>>> from sklearn.impute import IterativeImputer
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- |Feature| The :class:`impute.SimpleImputer` and
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:class:`impute.IterativeImputer` have a new parameter ``'add_indicator'``,
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which simply stacks a :class:`impute.MissingIndicator` transform into the

‎examples/impute/plot_iterative_imputer_variants_comparison.py

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import matplotlib.pyplot as plt
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import pandas as pd
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# To use this experimental feature, we need to explicitly ask for it:
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from sklearn.experimental import enable_iterative_imputer # noqa
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from sklearn.datasets import fetch_california_housing
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from sklearn.impute import SimpleImputer
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from sklearn.impute import IterativeImputer

‎examples/impute/plot_missing_values.py

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import numpy as np
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import matplotlib.pyplot as plt
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# To use the experimental IterativeImputer, we need to explicitly ask for it:
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from sklearn.experimental import enable_iterative_imputer # noqa
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from sklearn.datasets import load_diabetes
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from sklearn.datasets import load_boston
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from sklearn.ensemble import RandomForestRegressor

‎sklearn/experimental/enable_iterative_imputer.py

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"""Enables IterativeImputer
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The API and results of this estimators 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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impute.IterativeImputer = IterativeImputer
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impute.__all__ += ['IterativeImputer']

‎sklearn/experimental/tests/test_enable_iterative_imputer.py

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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):
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from sklearn.impute import IterativeImputer
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import sklearn.experimental
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with pytest.raises(ImportError):
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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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