scikit-learn · ogrisel · Dec 6, 2024 · Dec 2, 2024 · Dec 2, 2024 · Dec 2, 2024
diff --git a/examples/frozen/README.txt b/examples/frozen/README.txt
@@ -0,0 +1,7 @@
+.. _frozen_examples:
+
+Frozen Estimators
+-----------------
+
+Examples concerning the :mod:`sklearn.frozen` module.
+
diff --git a/examples/release_highlights/plot_release_highlights_1_6_0.py b/examples/release_highlights/plot_release_highlights_1_6_0.py
@@ -0,0 +1,212 @@
+# ruff: noqa
+"""
+=======================================
+Release Highlights for scikit-learn 1.6
+=======================================
+
+.. currentmodule:: sklearn
+
+We are pleased to announce the release of scikit-learn 1.6! Many bug fixes
+and improvements were added, as well as some key new features. Below we
+detail the highlights of this release. **For an exhaustive list of
+all the changes**, please refer to the :ref:`release notes <release_notes_1_6>`.
+
+To install the latest version (with pip)::
+
+    pip install --upgrade scikit-learn
+
+or with conda::
+
+    conda install -c conda-forge scikit-learn
+
+"""
+
+# %%
+# FrozenEstimator: Freezing an estimator
+# --------------------------------------
+#
+# This meta-estimator allows you to take an estimator and freeze its fit method, meaning
+# that calling `fit` does not perform any operations; also, `fit_predict` and
+# `fit_transform` call `predict` and `transform` respectively without calling `fit`. The
+# original estimator's other methods and properties are left unchanged. An interesting
+# use case for this is to use a pre-fitted model as a transformer step in a pipeline
+# or to pass a pre-fitted model to some of the meta-estimators. Here's a short example:
+
+import time
+from sklearn.datasets import make_classification
+from sklearn.frozen import FrozenEstimator
+from sklearn.linear_model import SGDClassifier
+from sklearn.model_selection import FixedThresholdClassifier
+
+X, y = make_classification(n_samples=1000, random_state=0)
+
+start = time.time()
+classifier = SGDClassifier().fit(X, y)
+print(f"Fitting the classifier took {(time.time() - start) * 1_000:.2f} milliseconds")
+
+start = time.time()
+threshold_classifier = FixedThresholdClassifier(
+    estimator=FrozenEstimator(classifier), threshold=0.9
+).fit(X, y)
+print(
+    f"Fitting the threshold classifier took {(time.time() - start) * 1_000:.2f} "
+    "milliseconds"
+)
+
+# %%
+# Fitting the threshold classifier skipped fitting the inner `SGDClassifier`. For more
+# details refer to the example :ref:`sphx_glr_auto_examples_frozen_plot_frozen_examples.py`.
+
+# %%
+# Transforming data other than X in a Pipeline
+# --------------------------------------------
+#
+# The :class:`~pipeline.Pipeline` now supports transforming passed data other than `X`
+# if necessary. This can be done by setting the new `transform_input` parameter. This
+# is particularly useful when passing a validation set through the pipeline.
+#
+# As an example, imagine `EstimatorWithValidationSet` is an estimator which accepts
+# a validation set. We can now have a pipeline which will transform the validation set
+# and pass it to the estimator::
+#
+#     sklearn.set_config(enable_metadata_routing=True)
+#     est_gs = GridSearchCV(
+#         Pipeline(
+#             (
+#                 StandardScaler(),
+#                 EstimatorWithValidationSet(...).set_fit_request(X_val=True, y_val=True),
+#             ),
+#             # telling pipeline to transform these inputs up to the step which is
+#             # requesting them.
+#             transform_input=["X_val"],
+#         ),
+#         param_grid={"estimatorwithvalidationset__param_to_optimize": list(range(5))},
+#         cv=5,
+#     ).fit(X, y, X_val, y_val)
+#
+# In the above code, the key parts are the call to `set_fit_request` to specify that
+# `X_val` and `y_val` are required by the `EstimatorWithValidationSet.fit` method, and
+# the `transform_input` parameter to tell the pipeline to transform `X_val` before
+# passing it to `EstimatorWithValidationSet.fit`.
+#
+# Note that at this time scikit-learn estimators have not yet been extended to accept
+# user specified validation sets. This feature is released early to collect feedback
+# from third-party libraries who might benefit from it.
+
+# %%
+# Multiclass support for `LogisticRegression(solver="newton-cholesky")`
+# ---------------------------------------------------------------------
+#
+# The `"newton-cholesky"` solver (originally introduced in scikit-learn version
+# 1.2) was previously limited to binary
+# :class:`~linear_model.LogisticRegression` and some other generalized linear
+# regression estimators (namely :class:`~linear_model.PoissonRegressor`,
+# :class:`~linear_model.GammaRegressor` and
+# :class:`~linear_model.TweedieRegressor`).
+#
+# This new release includes support for multiclass (multinomial)
+# :class:`~linear_model.LogisticRegression`.
+#
+# This solver is particularly useful when the number of features is small to
+# medium. It has been empirically shown to converge more reliably and faster
+# than other solvers on some medium sized datasets with one-hot encoded
+# categorical features as can be seen in the `benchmark results of the
+# pull-request
+# <https://github.com/scikit-learn/scikit-learn/pull/28840#issuecomment-2065368727>`_.
+
+# %%
+# Missing value support for Extra Trees
+# -------------------------------------
+#
+# The classes :class:`ensemble.ExtraTreesClassifier` and
+# :class:`ensemble.ExtraTreesRegressor` now support missing values. More details in the
+# :ref:`User Guide <tree_missing_value_support>`.
+import numpy as np
+from sklearn.ensemble import ExtraTreesClassifier
+
+X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
+y = [0, 0, 1, 1]
+
+forest = ExtraTreesClassifier(random_state=0).fit(X, y)
+forest.predict(X)
+
+# %%
+# Download any dataset from the web
+# ---------------------------------
+#
+# The function :func:`datasets.fetch_file` allows downloading a file from any given URL.
+# This convenience function provides built-in local disk caching, sha256 digest
+# integrity check and an automated retry mechanism on network error.
+#
+# The goal is to provide the same convenience and reliability as dataset fetchers while
+# giving the flexibility to work with data from arbitrary online sources and file
+# formats.
+#
+# The dowloaded file can then be loaded with generic or domain specific functions such
+# as `pandas.read_csv`, `pandas.read_parquet`, etc.
+
+# %%
+# Array API support
+# -----------------
+#
+# Many more estimators and functions have been updated to support array API compatible
+# inputs since version 1.5, in particular the meta-estimators for hyperparameter tuning
+# from the :mod:`sklearn.model_selection` module and the metrics from the
+# :mod:`sklearn.metrics` module.
+#
+# Please refer to the :ref:`array API support<array_api>` page for instructions to use
+# scikit-learn with array API compatible libraries such as PyTorch or CuPy.
+
+# %%
+# Almost complete Metadata Routing support
+# ----------------------------------------
+#
+# Support for routing metadata has been added to all remaining estimators and
+# functions except AdaBoost. See :ref:`Metadata Routing User Guide <metadata_routing>`
+# for more details.
+
+# %%
+# Free-threaded CPython 3.13 support
+# ----------------------------------
+#
+# scikit-learn has preliminary support for free-threaded CPython, in particular
+# free-threaded wheels are available for all of our supported platforms.
+#
+# Free-threaded (also known as nogil) CPython 3.13 is an experimental version of
+# CPython 3.13 which aims at enabling efficient multi-threaded use cases by
+# removing the Global Interpreter Lock (GIL).
+#
+# For more details about free-threaded CPython see `py-free-threading doc <https://py-free-threading.github.io>`_,
+# in particular `how to install a free-threaded CPython <https://py-free-threading.github.io/installing_cpython/>`_
+# and `Ecosystem compatibility tracking <https://py-free-threading.github.io/tracking/>`_.
+#
+# Feel free to try free-threaded CPython on your use case and report any issues!
+
+# %%
+# Improvements to the developer API for third party libraries
+# -----------------------------------------------------------
+#
+# We have been working on improving the developer API for third party libraries.
+# This is still a work in progress, but a fair amount of work has been done in this
+# release. This release includes:
+#
+# - :func:`sklearn.utils.validation.validate_data` is introduced and replaces the
+#   previously private `BaseEstimator._validate_data` method. This function extends
+#   :func:`~sklearn.utils.validation.check_array` and adds support for remembering
+#   input feature counts and names.
+# - Estimator tags are now revamped and a part of the public API via
+#   :class:`sklearn.utils.Tags`. Estimators should now override the
+#   :meth:`BaseEstimator.__sklearn_tags__` method instead of implementing a `_more_tags`
+#   method. If you'd like to support multiple scikit-learn versions, you can implement
+#   both methods in your class.
+# - As a consequence of developing a public tag API, we've removed the `_xfail_checks`
+#   tag and tests which are expected to fail are directly passed to
+#   :func:`~sklearn.utils.estimator_checks.check_estimator` and
+#   :func:`~sklearn.utils.estimator_checks.parametrize_with_checks`. See their
+#   corresponding API docs for more details.
+# - Many tests in the common test suite are updated and raise more helpful error
+#   messages. We've also added some new tests, which should help you more easily fix
+#   potential issues with your estimators.
+#
+# An updated version of our :ref:`develop` is also available, which we recommend you
+# check out.