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

Code before:
#   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 an `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/>`_.
#
# In scikit-learn, one hope with 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. Be aware that switching the default joblib
# backend and testing that everything works well with free-threaded is a
# long-term ongoing effort.

# %%
# Temperature scaling in `CalibratedClassifierCV`
# -----------------------------------------------
# Probability calibration of classifiers with temperature scaling is available in
# :class:`calibration.CalibratedClassifierCV` by setting `method="temperature"`.
# This method is particularly well suited for multiclass problems because it provides
# (better) calibrated probabilities with a single free parameter, in contrast
# to using a "One-vs-Rest" scheme that adds more parameters for each class.

from sklearn.calibration import CalibratedClassifierCV
from sklearn.datasets import make_classification
from sklearn.naive_bayes import GaussianNB

X, y = make_classification(n_classes=3, n_informative=8, random_state=42)
clf = GaussianNB().fit(X, y)
sig = CalibratedClassifierCV(clf, method="sigmoid", ensemble=False).fit(X, y)
ts = CalibratedClassifierCV(clf, method="temperature", ensemble=False).fit(X, y)

# %%
# The following example shows that temperature scaling can produce better-calibrated
# probabilities than sigmoid calibration in multi-class classification problem
# (3 classes).


Review comment (suggestion):
```suggestion
# backend and testing that everything works well with free-threaded Python is a
```

Code after:
#       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/>`_.
#
# In scikit-learn, one hope with 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. Be aware that switching the default joblib
# backend and testing that everything works well with free-threaded Python is an
# ongoing long-term effort.

# %%
# Temperature scaling in `CalibratedClassifierCV`
# -----------------------------------------------
# Probability calibration of classifiers with temperature scaling is available in
# :class:`calibration.CalibratedClassifierCV` by setting `method="temperature"`.
# This method is particularly well suited for multiclass problems because it provides
# (better) calibrated probabilities with a single free parameter. This is in
# contrast to all the other available calibrations methods
# which use a "One-vs-Rest" scheme that adds more parameters for each class.

from sklearn.calibration import CalibratedClassifierCV
from sklearn.datasets import make_classification
from sklearn.naive_bayes import GaussianNB

X, y = make_classification(n_classes=3, n_informative=8, random_state=42)
clf = GaussianNB().fit(X, y)
sig = CalibratedClassifierCV(clf, method="sigmoid", ensemble=False).fit(X, y)
ts = CalibratedClassifierCV(clf, method="temperature", ensemble=False).fit(X, y)

# %%
# The following example shows that temperature scaling can produce better calibrated
# probabilities than sigmoid calibration in multi-class classification problem
# with 3 classes.

import matplotlib.pyplot as plt

from sklearn.calibration import CalibrationDisplay