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Commit 309f135

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NicolasHugNicolasHug
authored
MNT Remove HistGradientBoosting from experimental (#19799)
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‎asv_benchmarks/benchmarks/ensemble.py

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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import (RandomForestClassifier,
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GradientBoostingClassifier,
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HistGradientBoostingClassifier)

‎benchmarks/bench_hist_gradient_boosting.py

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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.model_selection import train_test_split
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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_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.datasets import make_classification

‎benchmarks/bench_hist_gradient_boosting_adult.py

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from sklearn.model_selection import train_test_split
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from sklearn.datasets import fetch_openml
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from sklearn.metrics import accuracy_score, roc_auc_score
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.ensemble._hist_gradient_boosting.utils import (
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get_equivalent_estimator)

‎benchmarks/bench_hist_gradient_boosting_categorical_only.py

Copy file name to clipboardExpand all lines: benchmarks/bench_hist_gradient_boosting_categorical_only.py
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from sklearn.preprocessing import KBinsDiscretizer
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from sklearn.datasets import make_classification
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.ensemble._hist_gradient_boosting.utils import (
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get_equivalent_estimator)

‎benchmarks/bench_hist_gradient_boosting_higgsboson.py

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from joblib import Memory
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, roc_auc_score
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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_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.ensemble._hist_gradient_boosting.utils import (
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get_equivalent_estimator)

‎benchmarks/bench_hist_gradient_boosting_threading.py

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from threadpoolctl import threadpool_limits
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import sklearn
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from sklearn.model_selection import train_test_split
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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_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.datasets import make_classification

‎doc/conf.py

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@@ -358,7 +358,6 @@ def __call__(self, directory):
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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 enable_iterative_imputer # noqa
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from sklearn.experimental import enable_halving_search_cv # noqa
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‎doc/developers/maintainer.rst

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@@ -363,10 +363,17 @@ deprecation cycle.
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To create an experimental module, you can just copy and modify the content of
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`enable_hist_gradient_boosting.py
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<https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/experimental/enable_hist_gradient_boosting.py>`_,
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<https://github.com/scikit-learn/scikit-learn/blob/c9c89cfc85dd8dfefd7921c16c87327d03140a06/sklearn/experimental/enable_hist_gradient_boosting.py>`__,
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or
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`enable_iterative_imputer.py
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<https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/experimental/enable_iterative_imputer.py>`_.
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<https://github.com/scikit-learn/scikit-learn/blob/c9c89cfc85dd8dfefd7921c16c87327d03140a06/sklearn/experimental/enable_iterative_imputer.py>`_.
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.. note::
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These are permalink as in 0.24, where these estimators are still
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experimental. They might be stable at the time of reading - hence the
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permalink. See below for instructions on the transition from experimental
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to stable.
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Note that the public import path must be to a public subpackage (like
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``sklearn/ensemble`` or ``sklearn/impute``), not just a ``.py`` module.
@@ -379,14 +386,15 @@ in the future when the features aren't experimental anymore.
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To avoid type checker (e.g. mypy) errors a direct import of experimental
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estimators should be done in the parent module, protected by the
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``if typing.TYPE_CHECKING`` check. See `sklearn/ensemble/__init__.py
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<https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/ensemble/__init__.py>`_,
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<https://github.com/scikit-learn/scikit-learn/blob/c9c89cfc85dd8dfefd7921c16c87327d03140a06/sklearn/ensemble/__init__.py>`_,
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or `sklearn/impute/__init__.py
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<https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/impute/__init__.py>`_
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<https://github.com/scikit-learn/scikit-learn/blob/c9c89cfc85dd8dfefd7921c16c87327d03140a06/sklearn/impute/__init__.py>`_
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for an example.
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Please also write basic tests following those in
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`test_enable_hist_gradient_boosting.py
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<https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/experimental/tests/test_enable_hist_gradient_boosting.py>`_.
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<https://github.com/scikit-learn/scikit-learn/blob/c9c89cfc85dd8dfefd7921c16c87327d03140a06/sklearn/experimental/tests/test_enable_hist_gradient_boosting.py>`__.
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Make sure every user-facing code you write explicitly mentions that the feature
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is experimental, and add a ``# noqa`` comment to avoid pep8-related warnings::
@@ -402,3 +410,14 @@ sklearn.experimental import *`` **does not work**.
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Note that some experimental classes / functions are not included in the
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:mod:`sklearn.experimental` module: ``sklearn.datasets.fetch_openml``.
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Once the feature become stable, remove all `enable_my_experimental_feature`
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in the scikit-learn code (even feature highlights etc.) and make the
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`enable_my_experimental_feature` a no-op that just raises a warning:
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`enable_hist_gradient_boosting.py
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<https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/experimental/enable_hist_gradient_boosting.py>`__.
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The file should stay there indefinitely as we don't want to break users code:
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we just incentivize them to remove that import with the warning.
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Also update the tests accordingly: `test_enable_hist_gradient_boosting.py
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<https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/experimental/tests/test_enable_hist_gradient_boosting.py>`__.

‎doc/modules/ensemble.rst

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@@ -467,7 +467,7 @@ trees.
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.. note::
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Scikit-learn 0.21 introduces two new experimental implementations of
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Scikit-learn 0.21 introduces two new implementations of
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gradient boosting trees, namely :class:`HistGradientBoostingClassifier`
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and :class:`HistGradientBoostingRegressor`, inspired by
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`LightGBM <https://github.com/Microsoft/LightGBM>`__ (See [LightGBM]_).
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Histogram-Based Gradient Boosting
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=================================
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Scikit-learn 0.21 introduced two new experimental implementations of
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Scikit-learn 0.21 introduced two new implementations of
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gradient boosting trees, namely :class:`HistGradientBoostingClassifier`
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and :class:`HistGradientBoostingRegressor`, inspired by
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`LightGBM <https://github.com/Microsoft/LightGBM>`__ (See [LightGBM]_).
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:class:`GradientBoostingClassifier` and :class:`GradientBoostingRegressor`
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are not yet supported, for instance some loss functions.
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These estimators are still **experimental**: their predictions
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and their API might change without any deprecation cycle. To use them, you
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need to explicitly import ``enable_hist_gradient_boosting``::
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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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>>> from sklearn.ensemble import HistGradientBoostingClassifier
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.. topic:: Examples:
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* :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
@@ -941,7 +932,6 @@ Most of the parameters are unchanged from
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One exception is the ``max_iter`` parameter that replaces ``n_estimators``, and
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controls the number of iterations of the boosting process::
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>>> from sklearn.experimental import enable_hist_gradient_boosting
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>>> from sklearn.ensemble import HistGradientBoostingClassifier
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>>> from sklearn.datasets import make_hastie_10_2
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@@ -992,7 +982,6 @@ with missing values should go to the left or right child, based on the
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potential gain. When predicting, samples with missing values are assigned to
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the left or right child consequently::
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>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
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>>> from sklearn.ensemble import HistGradientBoostingClassifier
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>>> import numpy as np
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constraint, while -1 and 1 indicate a negative and positive constraint,
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respectively::
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>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
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>>> from sklearn.ensemble import HistGradientBoostingRegressor
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... # positive, negative, and no constraint on the 3 features

‎doc/whats_new/v0.21.rst

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@@ -420,6 +420,11 @@ Support for Python 3.4 and below has been officially dropped.
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>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
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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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.. note::
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Update: since version 1.0, these estimators are not experimental
426+
anymore and you don't need to use `from sklearn.experimental import
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enable_hist_gradient_boosting`.
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:pr:`12807` by :user:`Nicolas Hug<NicolasHug>`.
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‎doc/whats_new/v1.0.rst

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target. Additional private refactoring was performed.
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:pr:`19162` by :user:`Guillaume Lemaitre <glemaitre>`.
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- |Enhancement| :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and
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:class:`~sklearn.ensemble.HistGradientBoostingRegressor` are no longer
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experimental. They are now considered stable and are subject to the same
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deprecation cycles as all other estimators. :pr:`19799` by `Nicolas Hug`_.
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:mod:`sklearn.feature_extraction`
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.................................
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‎examples/ensemble/plot_gradient_boosting_categorical.py

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# As a baseline, we create an estimator where the categorical features are
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# dropped:
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.pipeline import make_pipeline
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from sklearn.compose import make_column_transformer

‎examples/ensemble/plot_monotonic_constraints.py

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This example was inspired by the `XGBoost documentation
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<https://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html>`_.
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"""
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.inspection import plot_partial_dependence
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import numpy as np

‎examples/ensemble/plot_stack_predictors.py

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rf_pipeline
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# %%
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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gbdt_pipeline = make_pipeline(

‎examples/inspection/plot_partial_dependence.py

Copy file name to clipboardExpand all lines: examples/inspection/plot_partial_dependence.py
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# Let's now fit a :class:`~sklearn.ensemble.HistGradientBoostingRegressor` and
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# compute the partial dependence on the same features.
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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print("Training HistGradientBoostingRegressor...")

‎examples/linear_model/plot_poisson_regression_non_normal_loss.py

Copy file name to clipboardExpand all lines: examples/linear_model/plot_poisson_regression_non_normal_loss.py
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# least-squares loss. Here we only fit trees with the Poisson loss to keep this
259259
# example concise.
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.preprocessing import OrdinalEncoder
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‎examples/release_highlights/plot_release_highlights_0_22_0.py

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# support for missing values (NaNs). This means that there is no need for
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# imputing data when training or predicting.
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingClassifier
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X = np.array([0, 1, 2, np.nan]).reshape(-1, 1)

‎examples/release_highlights/plot_release_highlights_0_23_0.py

Copy file name to clipboardExpand all lines: examples/release_highlights/plot_release_highlights_0_23_0.py
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@@ -36,7 +36,6 @@
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import numpy as np
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import PoissonRegressor
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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n_samples, n_features = 1000, 20
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124123
from matplotlib import pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.inspection import plot_partial_dependence
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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n_samples = 500

‎sklearn/ensemble/__init__.py

Copy file name to clipboardExpand all lines: sklearn/ensemble/__init__.py
+4-9Lines changed: 4 additions & 9 deletions
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The :mod:`sklearn.ensemble` module includes ensemble-based methods for
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classification, regression and anomaly detection.
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"""
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import typing
6-
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from ._base import BaseEnsemble
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from ._forest import RandomForestClassifier
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from ._forest import RandomForestRegressor
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from ._voting import VotingRegressor
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from ._stacking import StackingClassifier
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from ._stacking import StackingRegressor
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if typing.TYPE_CHECKING:
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# Avoid errors in type checkers (e.g. mypy) for experimental estimators.
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# TODO: remove this check once the estimator is no longer experimental.
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from ._hist_gradient_boosting.gradient_boosting import ( # noqa
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HistGradientBoostingRegressor, HistGradientBoostingClassifier
30-
)
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from ._hist_gradient_boosting.gradient_boosting import (
23+
HistGradientBoostingRegressor, HistGradientBoostingClassifier
24+
)
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__all__ = ["BaseEnsemble",
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"RandomForestClassifier", "RandomForestRegressor",
@@ -37,4 +31,5 @@
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"GradientBoostingRegressor", "AdaBoostClassifier",
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"AdaBoostRegressor", "VotingClassifier", "VotingRegressor",
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"StackingClassifier", "StackingRegressor",
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'HistGradientBoostingClassifier', 'HistGradientBoostingRegressor',
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]

‎sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py

Copy file name to clipboardExpand all lines: sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
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This implementation is inspired by
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`LightGBM <https://github.com/Microsoft/LightGBM>`_.
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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,
894-
you need to explicitly import ``enable_hist_gradient_boosting``::
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896-
>>> # explicitly require this experimental feature
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>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
898-
>>> # now you can import normally from ensemble
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>>> from sklearn.ensemble import HistGradientBoostingRegressor
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Read more in the :ref:`User Guide <histogram_based_gradient_boosting>`.
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.. versionadded:: 0.21
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Examples
10421031
--------
1043-
>>> # To use this experimental feature, we need to explicitly ask for it:
1044-
>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
10451032
>>> from sklearn.ensemble import HistGradientBoostingRegressor
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>>> from sklearn.datasets import load_diabetes
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>>> X, y = load_diabetes(return_X_y=True)
@@ -1156,17 +1143,6 @@ class HistGradientBoostingClassifier(ClassifierMixin,
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This implementation is inspired by
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`LightGBM <https://github.com/Microsoft/LightGBM>`_.
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1159-
.. note::
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1161-
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_hist_gradient_boosting``::
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1165-
>>> # explicitly require this experimental feature
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>>> from sklearn.experimental import enable_hist_gradient_boosting # noqa
1167-
>>> # now you can import normally from ensemble
1168-
>>> from sklearn.ensemble import HistGradientBoostingClassifier
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Read more in the :ref:`User Guide <histogram_based_gradient_boosting>`.
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.. versionadded:: 0.21
@@ -1304,8 +1280,6 @@ class HistGradientBoostingClassifier(ClassifierMixin,
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13051281
Examples
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--------
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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_hist_gradient_boosting # noqa
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>>> from sklearn.ensemble import HistGradientBoostingClassifier
13101284
>>> from sklearn.datasets import load_iris
13111285
>>> X, y = load_iris(return_X_y=True)

‎sklearn/ensemble/_hist_gradient_boosting/tests/test_compare_lightgbm.py

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import numpy as np
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import pytest
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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_hist_gradient_boosting # noqa
97
from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper

‎sklearn/ensemble/_hist_gradient_boosting/tests/test_gradient_boosting.py

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from sklearn.exceptions import NotFittedError
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from sklearn.compose import make_column_transformer
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# To use this experimental feature, we need to explicitly ask for it:
17-
from sklearn.experimental import enable_hist_gradient_boosting # noqa
1816
from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.ensemble import HistGradientBoostingClassifier
2018
from sklearn.ensemble._hist_gradient_boosting.loss import _LOSSES

‎sklearn/ensemble/_hist_gradient_boosting/tests/test_monotonic_contraints.py

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compute_node_value
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)
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from sklearn.ensemble._hist_gradient_boosting.histogram import HistogramBuilder
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from sklearn.experimental import enable_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.ensemble import HistGradientBoostingClassifier
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‎sklearn/ensemble/_hist_gradient_boosting/tests/test_warm_start.py

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from sklearn.base import clone
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from sklearn.datasets import make_classification, make_regression
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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_hist_gradient_boosting # noqa
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.ensemble import HistGradientBoostingClassifier
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from sklearn.metrics import check_scoring

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