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

Code before:
# 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/>`_.
#
# The long term goal of free-threaded Python is to more efficiently leverage
# multi-core CPUs by using thread workers instead of subprocess workers for parallel
# computation when passing `n_jobs>1` in functions or estimators.
# Efficiency gains are expected by removing the need for inter-process communication.
# Note however that process-based parallelism is still the default joblib backend at
# the time of writing. You can call `joblib.parallel_config(backend="threading")` to
# change the default backend to "threading". Be aware that properly testing that
# everything is running smoothly when doing so is still an ongoing effort and that
# there are open issues to fix before considering making this the default.

# %%
# Temperature scaling in `CalibratedClassifierCV`
# -----------------------------------------------
# Probability calibration with temperature scaling is available in
# :class:`calibration.CalibratedClassifierCV` by setting `method = "temperature"`.
# This method offers a natural way to obtain (better-)calibrated multi-class
# probabilities with just one free parameter, in contrast to using a
# "One-vs-Rest" scheme that adds more parameters for each single class.

from sklearn.calibration import CalibratedClassifierCV
from sklearn.datasets import make_classification
from sklearn.frozen import FrozenEstimator
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC

X, y = make_classification(
    n_samples=1000,
    n_features=10,
    n_informative=10,
    n_redundant=0,
    n_classes=5,
    n_clusters_per_class=1,
    class_sep=2.0,
    random_state=42,
)
X_train, X_calib, y_train, y_calib = train_test_split(X, y, random_state=42)
clf = LinearSVC(random_state=42)
clf.fit(X_train, y_train)
ts = CalibratedClassifierCV(FrozenEstimator(clf), method="temperature", ensemble=False)

Review comment (suggestion):
```suggestion
# Probability calibration of classifiers with temperature scaling is available in
```

Code after:
#         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
#
#       grid_search = GridSearchCV(clf, param_grid=param_grid, n_jobs=4)
#       with joblib.parallel_config(backend="threading"):
#           grid_search.fit(X, y)
#
# - don't hesitate to report any issue or unexpected performance behaviour by
#   opening a `GitHub issue <https://github.com/scikit-learn/scikit-learn/issues/new/choose>`_!
#
# 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).
#
# 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/>`_.