examples/frozen/plot_frozen_examples.py - main - githubmirror/scikit-learn

examples/frozen/plot_frozen_examples.py
98 строк4 KB
Christine P. Chai
DOC: Correct a typo: this examples -> this example (#31458)
01 июн 2025, 06:58
01 июн 2025, 06:584560abc
"""
===================================
Examples of Using `FrozenEstimator`
===================================

This example showcases some use cases of :class:`~sklearn.frozen.FrozenEstimator`.

:class:`~sklearn.frozen.FrozenEstimator` is a utility class that allows to freeze a
fitted estimator. This is useful, for instance, when we want to pass a fitted estimator
to a meta-estimator, such as :class:`~sklearn.model_selection.FixedThresholdClassifier`
without letting the meta-estimator refit the estimator.
"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# Setting a decision threshold for a pre-fitted classifier
# --------------------------------------------------------
# Fitted classifiers in scikit-learn use an arbitrary decision threshold to decide
# which class the given sample belongs to. The decision threshold is either `0.0` on the
# value returned by :term:`decision_function`, or `0.5` on the probability returned by
# :term:`predict_proba`.
#
# However, one might want to set a custom decision threshold. We can do this by
# using :class:`~sklearn.model_selection.FixedThresholdClassifier` and wrapping the
# classifier with :class:`~sklearn.frozen.FrozenEstimator`.
from sklearn.datasets import make_classification
from sklearn.frozen import FrozenEstimator
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import FixedThresholdClassifier, train_test_split

X, y = make_classification(n_samples=1000, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
classifier = LogisticRegression().fit(X_train, y_train)

print(
    "Probability estimates for three data points:\n"
    f"{classifier.predict_proba(X_test[-3:]).round(3)}"
)
print(
    "Predicted class for the same three data points:\n"
    f"{classifier.predict(X_test[-3:])}"
)

# %%
# Now imagine you'd want to set a different decision threshold on the probability
# estimates. We can do this by wrapping the classifier with
# :class:`~sklearn.frozen.FrozenEstimator` and passing it to
# :class:`~sklearn.model_selection.FixedThresholdClassifier`.

threshold_classifier = FixedThresholdClassifier(
    estimator=FrozenEstimator(classifier), threshold=0.9
)

# %%
# Note that in the above piece of code, calling `fit` on
# :class:`~sklearn.model_selection.FixedThresholdClassifier` does not refit the
# underlying classifier.
#
# Now, let's see how the predictions changed with respect to the probability
# threshold.
print(
    "Probability estimates for three data points with FixedThresholdClassifier:\n"
    f"{threshold_classifier.predict_proba(X_test[-3:]).round(3)}"
)
print(
    "Predicted class for the same three data points with FixedThresholdClassifier:\n"
    f"{threshold_classifier.predict(X_test[-3:])}"
)

# %%
# We see that the probability estimates stay the same, but since a different decision
# threshold is used, the predicted classes are different.
#
# Please refer to
# :ref:`sphx_glr_auto_examples_model_selection_plot_cost_sensitive_learning.py`
# to learn about cost-sensitive learning and decision threshold tuning.

# %%
# Calibration of a pre-fitted classifier
# --------------------------------------
# You can use :class:`~sklearn.frozen.FrozenEstimator` to calibrate a pre-fitted
# classifier using :class:`~sklearn.calibration.CalibratedClassifierCV`.
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import brier_score_loss

calibrated_classifier = CalibratedClassifierCV(
    estimator=FrozenEstimator(classifier)
).fit(X_train, y_train)

prob_pos_clf = classifier.predict_proba(X_test)[:, 1]
clf_score = brier_score_loss(y_test, prob_pos_clf)
print(f"No calibration: {clf_score:.3f}")

prob_pos_calibrated = calibrated_classifier.predict_proba(X_test)[:, 1]
calibrated_score = brier_score_loss(y_test, prob_pos_calibrated)
print(f"With calibration: {calibrated_score:.3f}")