Repository: scikit-learn/scikit-learn
File: examples/release_highlights/plot_release_highlights_1_8_0.py (Python)

Code before:
"""

# %%
# Array API support
# -----------------
# In scikit-learn 1.8, several estimators and functions have been updated to
# support array API compatible inputs, for example PyTorch tensors and CuPy arrays.
#
# Array API support was added to the following estimators:
# :class:`preprocessing.StandardScaler`, :class:`preprocessing.PolynomialFeatures`,
# :class:`linear_model.RidgeCV`, :class:`mixture.GaussianMixture` and
# :class:`calibration.CalibratedClassifierCV`.
#
# Array API support was also added to several metrics in :mod:`sklearn.metrics`
# module, see :ref:`array_api_supported` for more details.
#
# Please refer the :ref:`array API support<array_api>` page for instructions
# to use scikit-learn with array API compatible libraries such as PyTorch or CuPy.
#
# TODO do we want to write a snippet?
# - which estimators would we feature?
# - we don't have PyTorch in doc build for now ...
# - we don't have GPU in the doc build but we could show a snippet with numpy
#   and commented out code to switch to PyToch on GPU
# - alternative: show only highlighted code without executing it?
# - alternative: add link to Colab notebook?

# %%
# Free-threaded CPython 3.14 support
# ----------------------------------
#
# scikit-learn has support for free-threaded CPython, in particular
# free-threaded wheels are available for all of our supported platforms on Python
# 3.14.
#
# Free-threaded (also known as nogil) CPython is a version of CPython that aims at
# enabling efficient multi-threaded use cases by removing the Global Interpreter
# Lock (GIL).
#
# If you want to try out free-threaded Python, the recommendation is to use
# Python 3.14, that has fixed a number of issues compared to Python 3.13. Feel
# free to try free-threaded on your use case and report any issues!
#
# 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/>`_.

# %%
# Temperature scaling in `CalibratedClassifierCV`
# -----------------------------------------------
# TODO

Review comment (suggestion):
That's a good idea. We could prepare such a notebook against a nightly build of scikit-learn and update it after the release to point to the stable wheels instead.

Code after:
"""

# %%
# Array API support (enables GPU computations)
# --------------------------------------------
# The progressive adoption of the Python array API standard in
# scikit-learn means that PyTorch and CuPy input arrays
# are used directly. This means that in scikit-learn estimators
# and functions non-CPU devices, such as GPUs, can be used
# to perform the computation. As a result performance is improved
# and integration with these libraries is easier.
#
# In scikit-learn 1.8, several estimators and functions have been updated to
# support array API compatible inputs, for example PyTorch tensors and CuPy
# arrays.
#
# Array API support was added to the following estimators:
# :class:`preprocessing.StandardScaler`,
# :class:`preprocessing.PolynomialFeatures`, :class:`linear_model.RidgeCV`,
# :class:`linear_model.RidgeClassifierCV`, :class:`mixture.GaussianMixture` and
# :class:`calibration.CalibratedClassifierCV`.
#
# Array API support was also added to several metrics in :mod:`sklearn.metrics`
# module, see :ref:`array_api_supported` for more details.
#
# 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.
# Note: Array API support is experimental and must be explicitly enabled both
# in SciPy and scikit-learn.
#
# Here is an excerpt of using a feature engineering preprocessor on the CPU,
# followed by :class:`calibration.CalibratedClassifierCV`
# and :class:`linear_model.RidgeCV` together on a GPU with the help of PyTorch:
#
# .. code-block:: python
#
#     ridge_pipeline_gpu = make_pipeline(
#         # Ensure that all features (including categorical features) are preprocessed
#         # on the CPU and mapped to a numerical representation.
#         feature_preprocessor,
#         # Move the results to the GPU and perform computations there
#         FunctionTransformer(
#             lambda x: torch.tensor(x.to_numpy().astype(np.float32), device="cuda"))
#         ,
#         CalibratedClassifierCV(
#             RidgeClassifierCV(alphas=alphas), method="temperature"
#         ),
#     )
#     with sklearn.config_context(array_api_dispatch=True):
#         cv_results = cross_validate(ridge_pipeline_gpu, features, target)
#