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

Code before:
#
# Here an excerpt of using :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(
#         TableVectorizer(
#             numeric=make_pipeline(
#                 QuantileTransformer(),
#                 SplineTransformer(n_knots=10),
#             ),
#             high_cardinality=TargetEncoder(cv=5),
#         ),
#         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)
#
#
# Here is a [full notebook of this example on Google
# Colab](https://colab.research.google.com/drive/1ztH8gUPv31hSjEeR_8pw20qShTwViGRx?usp=sharing).
# For this example, using the colab GPU vs using a single CPU core lead to a
# 10x speedup which is quite typical for such workloads.

# %%
# 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 to
# enable efficient multi-threaded use cases by removing the Global Interpreter Lock
# (GIL).
#
# Please try your use cases with free-threaded CPython and `report issues
# <https://github.com/scikit-learn/scikit-learn/issues/new/choose>`_!
#
# 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/>`_.
#
# The long term goal of free-threaded Python is to more efficiently leverage

Review comment (suggestion):
```suggestion
# A [full notebook of this example on Google
```

Just to avoid starting two paragraphs with "here".

Code after:
#
# 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)
#
#
# See the `full notebook on Google Colab
# <https://colab.research.google.com/drive/1ztH8gUPv31hSjEeR_8pw20qShTwViGRx?usp=sharing>`_
# for more details. On this particular example, using the Colab GPU vs using a
# single CPU core leads to a 10x speedup which is quite typical for such workloads.

# %%
# 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.
#
# We would be very interested by user feedback. Here are a few things you can
# try:
#
# - install free-threaded CPython 3.14, run your favourite
#   scikit-learn script and check that nothing breaks unexpectedly.
#   Note that CPython 3.14 (rather than 3.13) is strongly advised because a
#   number of free-threaded bugs have been fixed since CPython 3.13.
# - if you use some estimators with a `n_jobs` parameter, try changing the
#   default backend to threading with `joblib.parallel_config` as in the
#   snippet below. This could potentially speed-up your code because the
#   default joblib backend is process-based and incurs more overhead than
#   threads.
#
#   .. code-block:: python
#