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ai-content-maker/.venv/Lib/site-packages/sklearn/calibration.py

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"""Calibration of predicted probabilities."""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Mathieu Blondel <mathieu@mblondel.org>
#
# License: BSD 3 clause
import warnings
from inspect import signature
from math import log
from numbers import Integral, Real
import numpy as np
from scipy.optimize import minimize
from scipy.special import expit
from sklearn.utils import Bunch
from ._loss import HalfBinomialLoss
from .base import (
BaseEstimator,
ClassifierMixin,
MetaEstimatorMixin,
RegressorMixin,
_fit_context,
clone,
)
from .isotonic import IsotonicRegression
from .model_selection import check_cv, cross_val_predict
from .preprocessing import LabelEncoder, label_binarize
from .svm import LinearSVC
from .utils import (
_safe_indexing,
column_or_1d,
indexable,
)
from .utils._param_validation import (
HasMethods,
Interval,
StrOptions,
validate_params,
)
from .utils._plotting import _BinaryClassifierCurveDisplayMixin
from .utils._response import _get_response_values, _process_predict_proba
from .utils.metadata_routing import (
MetadataRouter,
MethodMapping,
_routing_enabled,
process_routing,
)
from .utils.multiclass import check_classification_targets
from .utils.parallel import Parallel, delayed
from .utils.validation import (
_check_method_params,
_check_pos_label_consistency,
_check_response_method,
_check_sample_weight,
_num_samples,
check_consistent_length,
check_is_fitted,
)
class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
"""Probability calibration with isotonic regression or logistic regression.
This class uses cross-validation to both estimate the parameters of a
classifier and subsequently calibrate a classifier. With default
`ensemble=True`, for each cv split it
fits a copy of the base estimator to the training subset, and calibrates it
using the testing subset. For prediction, predicted probabilities are
averaged across these individual calibrated classifiers. When
`ensemble=False`, cross-validation is used to obtain unbiased predictions,
via :func:`~sklearn.model_selection.cross_val_predict`, which are then
used for calibration. For prediction, the base estimator, trained using all
the data, is used. This is the prediction method implemented when
`probabilities=True` for :class:`~sklearn.svm.SVC` and :class:`~sklearn.svm.NuSVC`
estimators (see :ref:`User Guide <scores_probabilities>` for details).
Already fitted classifiers can be calibrated via the parameter
`cv="prefit"`. In this case, no cross-validation is used and all provided
data is used for calibration. The user has to take care manually that data
for model fitting and calibration are disjoint.
The calibration is based on the :term:`decision_function` method of the
`estimator` if it exists, else on :term:`predict_proba`.
Read more in the :ref:`User Guide <calibration>`.
Parameters
----------
estimator : estimator instance, default=None
The classifier whose output need to be calibrated to provide more
accurate `predict_proba` outputs. The default classifier is
a :class:`~sklearn.svm.LinearSVC`.
.. versionadded:: 1.2
method : {'sigmoid', 'isotonic'}, default='sigmoid'
The method to use for calibration. Can be 'sigmoid' which
corresponds to Platt's method (i.e. a logistic regression model) or
'isotonic' which is a non-parametric approach. It is not advised to
use isotonic calibration with too few calibration samples
``(<<1000)`` since it tends to overfit.
cv : int, cross-validation generator, iterable or "prefit", \
default=None
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 5-fold cross-validation,
- integer, to specify the number of folds.
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if ``y`` is binary or multiclass,
:class:`~sklearn.model_selection.StratifiedKFold` is used. If ``y`` is
neither binary nor multiclass, :class:`~sklearn.model_selection.KFold`
is used.
Refer to the :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
If "prefit" is passed, it is assumed that `estimator` has been
fitted already and all data is used for calibration.
.. versionchanged:: 0.22
``cv`` default value if None changed from 3-fold to 5-fold.
n_jobs : int, default=None
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors.
Base estimator clones are fitted in parallel across cross-validation
iterations. Therefore parallelism happens only when `cv != "prefit"`.
See :term:`Glossary <n_jobs>` for more details.
.. versionadded:: 0.24
ensemble : bool, default=True
Determines how the calibrator is fitted when `cv` is not `'prefit'`.
Ignored if `cv='prefit'`.
If `True`, the `estimator` is fitted using training data, and
calibrated using testing data, for each `cv` fold. The final estimator
is an ensemble of `n_cv` fitted classifier and calibrator pairs, where
`n_cv` is the number of cross-validation folds. The output is the
average predicted probabilities of all pairs.
If `False`, `cv` is used to compute unbiased predictions, via
:func:`~sklearn.model_selection.cross_val_predict`, which are then
used for calibration. At prediction time, the classifier used is the
`estimator` trained on all the data.
Note that this method is also internally implemented in
:mod:`sklearn.svm` estimators with the `probabilities=True` parameter.
.. versionadded:: 0.24
Attributes
----------
classes_ : ndarray of shape (n_classes,)
The class labels.
n_features_in_ : int
Number of features seen during :term:`fit`. Only defined if the
underlying estimator exposes such an attribute when fit.
.. versionadded:: 0.24
feature_names_in_ : ndarray of shape (`n_features_in_`,)
Names of features seen during :term:`fit`. Only defined if the
underlying estimator exposes such an attribute when fit.
.. versionadded:: 1.0
calibrated_classifiers_ : list (len() equal to cv or 1 if `cv="prefit"` \
or `ensemble=False`)
The list of classifier and calibrator pairs.
- When `cv="prefit"`, the fitted `estimator` and fitted
calibrator.
- When `cv` is not "prefit" and `ensemble=True`, `n_cv` fitted
`estimator` and calibrator pairs. `n_cv` is the number of
cross-validation folds.
- When `cv` is not "prefit" and `ensemble=False`, the `estimator`,
fitted on all the data, and fitted calibrator.
.. versionchanged:: 0.24
Single calibrated classifier case when `ensemble=False`.
See Also
--------
calibration_curve : Compute true and predicted probabilities
for a calibration curve.
References
----------
.. [1] Obtaining calibrated probability estimates from decision trees
and naive Bayesian classifiers, B. Zadrozny & C. Elkan, ICML 2001
.. [2] Transforming Classifier Scores into Accurate Multiclass
Probability Estimates, B. Zadrozny & C. Elkan, (KDD 2002)
.. [3] Probabilistic Outputs for Support Vector Machines and Comparisons to
Regularized Likelihood Methods, J. Platt, (1999)
.. [4] Predicting Good Probabilities with Supervised Learning,
A. Niculescu-Mizil & R. Caruana, ICML 2005
Examples
--------
>>> from sklearn.datasets import make_classification
>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.calibration import CalibratedClassifierCV
>>> X, y = make_classification(n_samples=100, n_features=2,
... n_redundant=0, random_state=42)
>>> base_clf = GaussianNB()
>>> calibrated_clf = CalibratedClassifierCV(base_clf, cv=3)
>>> calibrated_clf.fit(X, y)
CalibratedClassifierCV(...)
>>> len(calibrated_clf.calibrated_classifiers_)
3
>>> calibrated_clf.predict_proba(X)[:5, :]
array([[0.110..., 0.889...],
[0.072..., 0.927...],
[0.928..., 0.071...],
[0.928..., 0.071...],
[0.071..., 0.928...]])
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_classification(n_samples=100, n_features=2,
... n_redundant=0, random_state=42)
>>> X_train, X_calib, y_train, y_calib = train_test_split(
... X, y, random_state=42
... )
>>> base_clf = GaussianNB()
>>> base_clf.fit(X_train, y_train)
GaussianNB()
>>> calibrated_clf = CalibratedClassifierCV(base_clf, cv="prefit")
>>> calibrated_clf.fit(X_calib, y_calib)
CalibratedClassifierCV(...)
>>> len(calibrated_clf.calibrated_classifiers_)
1
>>> calibrated_clf.predict_proba([[-0.5, 0.5]])
array([[0.936..., 0.063...]])
"""
_parameter_constraints: dict = {
"estimator": [
HasMethods(["fit", "predict_proba"]),
HasMethods(["fit", "decision_function"]),
None,
],
"method": [StrOptions({"isotonic", "sigmoid"})],
"cv": ["cv_object", StrOptions({"prefit"})],
"n_jobs": [Integral, None],
"ensemble": ["boolean"],
}
def __init__(
self,
estimator=None,
*,
method="sigmoid",
cv=None,
n_jobs=None,
ensemble=True,
):
self.estimator = estimator
self.method = method
self.cv = cv
self.n_jobs = n_jobs
self.ensemble = ensemble
def _get_estimator(self):
"""Resolve which estimator to return (default is LinearSVC)"""
if self.estimator is None:
# we want all classifiers that don't expose a random_state
# to be deterministic (and we don't want to expose this one).
estimator = LinearSVC(random_state=0, dual="auto")
if _routing_enabled():
estimator.set_fit_request(sample_weight=True)
else:
estimator = self.estimator
return estimator
@_fit_context(
# CalibratedClassifierCV.estimator is not validated yet
prefer_skip_nested_validation=False
)
def fit(self, X, y, sample_weight=None, **fit_params):
"""Fit the calibrated model.
Parameters
----------
X : array-like of shape (n_samples, n_features)
Training data.
y : array-like of shape (n_samples,)
Target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
**fit_params : dict
Parameters to pass to the `fit` method of the underlying
classifier.
Returns
-------
self : object
Returns an instance of self.
"""
check_classification_targets(y)
X, y = indexable(X, y)
if sample_weight is not None:
sample_weight = _check_sample_weight(sample_weight, X)
estimator = self._get_estimator()
self.calibrated_classifiers_ = []
if self.cv == "prefit":
# `classes_` should be consistent with that of estimator
check_is_fitted(self.estimator, attributes=["classes_"])
self.classes_ = self.estimator.classes_
predictions, _ = _get_response_values(
estimator,
X,
response_method=["decision_function", "predict_proba"],
)
if predictions.ndim == 1:
# Reshape binary output from `(n_samples,)` to `(n_samples, 1)`
predictions = predictions.reshape(-1, 1)
calibrated_classifier = _fit_calibrator(
estimator,
predictions,
y,
self.classes_,
self.method,
sample_weight,
)
self.calibrated_classifiers_.append(calibrated_classifier)
else:
# Set `classes_` using all `y`
label_encoder_ = LabelEncoder().fit(y)
self.classes_ = label_encoder_.classes_
if _routing_enabled():
routed_params = process_routing(
self,
"fit",
sample_weight=sample_weight,
**fit_params,
)
else:
# sample_weight checks
fit_parameters = signature(estimator.fit).parameters
supports_sw = "sample_weight" in fit_parameters
if sample_weight is not None and not supports_sw:
estimator_name = type(estimator).__name__
warnings.warn(
f"Since {estimator_name} does not appear to accept"
" sample_weight, sample weights will only be used for the"
" calibration itself. This can be caused by a limitation of"
" the current scikit-learn API. See the following issue for"
" more details:"
" https://github.com/scikit-learn/scikit-learn/issues/21134."
" Be warned that the result of the calibration is likely to be"
" incorrect."
)
routed_params = Bunch()
routed_params.splitter = Bunch(split={}) # no routing for splitter
routed_params.estimator = Bunch(fit=fit_params)
if sample_weight is not None and supports_sw:
routed_params.estimator.fit["sample_weight"] = sample_weight
# Check that each cross-validation fold can have at least one
# example per class
if isinstance(self.cv, int):
n_folds = self.cv
elif hasattr(self.cv, "n_splits"):
n_folds = self.cv.n_splits
else:
n_folds = None
if n_folds and np.any(
[np.sum(y == class_) < n_folds for class_ in self.classes_]
):
raise ValueError(
f"Requesting {n_folds}-fold "
"cross-validation but provided less than "
f"{n_folds} examples for at least one class."
)
cv = check_cv(self.cv, y, classifier=True)
if self.ensemble:
parallel = Parallel(n_jobs=self.n_jobs)
self.calibrated_classifiers_ = parallel(
delayed(_fit_classifier_calibrator_pair)(
clone(estimator),
X,
y,
train=train,
test=test,
method=self.method,
classes=self.classes_,
sample_weight=sample_weight,
fit_params=routed_params.estimator.fit,
)
for train, test in cv.split(X, y, **routed_params.splitter.split)
)
else:
this_estimator = clone(estimator)
method_name = _check_response_method(
this_estimator,
["decision_function", "predict_proba"],
).__name__
predictions = cross_val_predict(
estimator=this_estimator,
X=X,
y=y,
cv=cv,
method=method_name,
n_jobs=self.n_jobs,
params=routed_params.estimator.fit,
)
if len(self.classes_) == 2:
# Ensure shape (n_samples, 1) in the binary case
if method_name == "predict_proba":
# Select the probability column of the postive class
predictions = _process_predict_proba(
y_pred=predictions,
target_type="binary",
classes=self.classes_,
pos_label=self.classes_[1],
)
predictions = predictions.reshape(-1, 1)
this_estimator.fit(X, y, **routed_params.estimator.fit)
# Note: Here we don't pass on fit_params because the supported
# calibrators don't support fit_params anyway
calibrated_classifier = _fit_calibrator(
this_estimator,
predictions,
y,
self.classes_,
self.method,
sample_weight,
)
self.calibrated_classifiers_.append(calibrated_classifier)
first_clf = self.calibrated_classifiers_[0].estimator
if hasattr(first_clf, "n_features_in_"):
self.n_features_in_ = first_clf.n_features_in_
if hasattr(first_clf, "feature_names_in_"):
self.feature_names_in_ = first_clf.feature_names_in_
return self
def predict_proba(self, X):
"""Calibrated probabilities of classification.
This function returns calibrated probabilities of classification
according to each class on an array of test vectors X.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The samples, as accepted by `estimator.predict_proba`.
Returns
-------
C : ndarray of shape (n_samples, n_classes)
The predicted probas.
"""
check_is_fitted(self)
# Compute the arithmetic mean of the predictions of the calibrated
# classifiers
mean_proba = np.zeros((_num_samples(X), len(self.classes_)))
for calibrated_classifier in self.calibrated_classifiers_:
proba = calibrated_classifier.predict_proba(X)
mean_proba += proba
mean_proba /= len(self.calibrated_classifiers_)
return mean_proba
def predict(self, X):
"""Predict the target of new samples.
The predicted class is the class that has the highest probability,
and can thus be different from the prediction of the uncalibrated classifier.
Parameters
----------
X : array-like of shape (n_samples, n_features)
The samples, as accepted by `estimator.predict`.
Returns
-------
C : ndarray of shape (n_samples,)
The predicted class.
"""
check_is_fitted(self)
return self.classes_[np.argmax(self.predict_proba(X), axis=1)]
def get_metadata_routing(self):
"""Get metadata routing of this object.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
Returns
-------
routing : MetadataRouter
A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating
routing information.
"""
router = (
MetadataRouter(owner=self.__class__.__name__)
.add_self_request(self)
.add(
estimator=self._get_estimator(),
method_mapping=MethodMapping().add(callee="fit", caller="fit"),
)
.add(
splitter=self.cv,
method_mapping=MethodMapping().add(callee="split", caller="fit"),
)
)
return router
def _more_tags(self):
return {
"_xfail_checks": {
"check_sample_weights_invariance": (
"Due to the cross-validation and sample ordering, removing a sample"
" is not strictly equal to putting is weight to zero. Specific unit"
" tests are added for CalibratedClassifierCV specifically."
),
}
}
def _fit_classifier_calibrator_pair(
estimator,
X,
y,
train,
test,
method,
classes,
sample_weight=None,
fit_params=None,
):
"""Fit a classifier/calibration pair on a given train/test split.
Fit the classifier on the train set, compute its predictions on the test
set and use the predictions as input to fit the calibrator along with the
test labels.
Parameters
----------
estimator : estimator instance
Cloned base estimator.
X : array-like, shape (n_samples, n_features)
Sample data.
y : array-like, shape (n_samples,)
Targets.
train : ndarray, shape (n_train_indices,)
Indices of the training subset.
test : ndarray, shape (n_test_indices,)
Indices of the testing subset.
method : {'sigmoid', 'isotonic'}
Method to use for calibration.
classes : ndarray, shape (n_classes,)
The target classes.
sample_weight : array-like, default=None
Sample weights for `X`.
fit_params : dict, default=None
Parameters to pass to the `fit` method of the underlying
classifier.
Returns
-------
calibrated_classifier : _CalibratedClassifier instance
"""
fit_params_train = _check_method_params(X, params=fit_params, indices=train)
X_train, y_train = _safe_indexing(X, train), _safe_indexing(y, train)
X_test, y_test = _safe_indexing(X, test), _safe_indexing(y, test)
estimator.fit(X_train, y_train, **fit_params_train)
predictions, _ = _get_response_values(
estimator,
X_test,
response_method=["decision_function", "predict_proba"],
)
if predictions.ndim == 1:
# Reshape binary output from `(n_samples,)` to `(n_samples, 1)`
predictions = predictions.reshape(-1, 1)
sw_test = None if sample_weight is None else _safe_indexing(sample_weight, test)
calibrated_classifier = _fit_calibrator(
estimator, predictions, y_test, classes, method, sample_weight=sw_test
)
return calibrated_classifier
def _fit_calibrator(clf, predictions, y, classes, method, sample_weight=None):
"""Fit calibrator(s) and return a `_CalibratedClassifier`
instance.
`n_classes` (i.e. `len(clf.classes_)`) calibrators are fitted.
However, if `n_classes` equals 2, one calibrator is fitted.
Parameters
----------
clf : estimator instance
Fitted classifier.
predictions : array-like, shape (n_samples, n_classes) or (n_samples, 1) \
when binary.
Raw predictions returned by the un-calibrated base classifier.
y : array-like, shape (n_samples,)
The targets.
classes : ndarray, shape (n_classes,)
All the prediction classes.
method : {'sigmoid', 'isotonic'}
The method to use for calibration.
sample_weight : ndarray, shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
Returns
-------
pipeline : _CalibratedClassifier instance
"""
Y = label_binarize(y, classes=classes)
label_encoder = LabelEncoder().fit(classes)
pos_class_indices = label_encoder.transform(clf.classes_)
calibrators = []
for class_idx, this_pred in zip(pos_class_indices, predictions.T):
if method == "isotonic":
calibrator = IsotonicRegression(out_of_bounds="clip")
else: # "sigmoid"
calibrator = _SigmoidCalibration()
calibrator.fit(this_pred, Y[:, class_idx], sample_weight)
calibrators.append(calibrator)
pipeline = _CalibratedClassifier(clf, calibrators, method=method, classes=classes)
return pipeline
class _CalibratedClassifier:
"""Pipeline-like chaining a fitted classifier and its fitted calibrators.
Parameters
----------
estimator : estimator instance
Fitted classifier.
calibrators : list of fitted estimator instances
List of fitted calibrators (either 'IsotonicRegression' or
'_SigmoidCalibration'). The number of calibrators equals the number of
classes. However, if there are 2 classes, the list contains only one
fitted calibrator.
classes : array-like of shape (n_classes,)
All the prediction classes.
method : {'sigmoid', 'isotonic'}, default='sigmoid'
The method to use for calibration. Can be 'sigmoid' which
corresponds to Platt's method or 'isotonic' which is a
non-parametric approach based on isotonic regression.
"""
def __init__(self, estimator, calibrators, *, classes, method="sigmoid"):
self.estimator = estimator
self.calibrators = calibrators
self.classes = classes
self.method = method
def predict_proba(self, X):
"""Calculate calibrated probabilities.
Calculates classification calibrated probabilities
for each class, in a one-vs-all manner, for `X`.
Parameters
----------
X : ndarray of shape (n_samples, n_features)
The sample data.
Returns
-------
proba : array, shape (n_samples, n_classes)
The predicted probabilities. Can be exact zeros.
"""
predictions, _ = _get_response_values(
self.estimator,
X,
response_method=["decision_function", "predict_proba"],
)
if predictions.ndim == 1:
# Reshape binary output from `(n_samples,)` to `(n_samples, 1)`
predictions = predictions.reshape(-1, 1)
n_classes = len(self.classes)
label_encoder = LabelEncoder().fit(self.classes)
pos_class_indices = label_encoder.transform(self.estimator.classes_)
proba = np.zeros((_num_samples(X), n_classes))
for class_idx, this_pred, calibrator in zip(
pos_class_indices, predictions.T, self.calibrators
):
if n_classes == 2:
# When binary, `predictions` consists only of predictions for
# clf.classes_[1] but `pos_class_indices` = 0
class_idx += 1
proba[:, class_idx] = calibrator.predict(this_pred)
# Normalize the probabilities
if n_classes == 2:
proba[:, 0] = 1.0 - proba[:, 1]
else:
denominator = np.sum(proba, axis=1)[:, np.newaxis]
# In the edge case where for each class calibrator returns a null
# probability for a given sample, use the uniform distribution
# instead.
uniform_proba = np.full_like(proba, 1 / n_classes)
proba = np.divide(
proba, denominator, out=uniform_proba, where=denominator != 0
)
# Deal with cases where the predicted probability minimally exceeds 1.0
proba[(1.0 < proba) & (proba <= 1.0 + 1e-5)] = 1.0
return proba
# The max_abs_prediction_threshold was approximated using
# logit(np.finfo(np.float64).eps) which is about -36
def _sigmoid_calibration(
predictions, y, sample_weight=None, max_abs_prediction_threshold=30
):
"""Probability Calibration with sigmoid method (Platt 2000)
Parameters
----------
predictions : ndarray of shape (n_samples,)
The decision function or predict proba for the samples.
y : ndarray of shape (n_samples,)
The targets.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
Returns
-------
a : float
The slope.
b : float
The intercept.
References
----------
Platt, "Probabilistic Outputs for Support Vector Machines"
"""
predictions = column_or_1d(predictions)
y = column_or_1d(y)
F = predictions # F follows Platt's notations
scale_constant = 1.0
max_prediction = np.max(np.abs(F))
# If the predictions have large values we scale them in order to bring
# them within a suitable range. This has no effect on the final
# (prediction) result because linear models like Logisitic Regression
# without a penalty are invariant to multiplying the features by a
# constant.
if max_prediction >= max_abs_prediction_threshold:
scale_constant = max_prediction
# We rescale the features in a copy: inplace rescaling could confuse
# the caller and make the code harder to reason about.
F = F / scale_constant
# Bayesian priors (see Platt end of section 2.2):
# It corresponds to the number of samples, taking into account the
# `sample_weight`.
mask_negative_samples = y <= 0
if sample_weight is not None:
prior0 = (sample_weight[mask_negative_samples]).sum()
prior1 = (sample_weight[~mask_negative_samples]).sum()
else:
prior0 = float(np.sum(mask_negative_samples))
prior1 = y.shape[0] - prior0
T = np.zeros_like(y, dtype=predictions.dtype)
T[y > 0] = (prior1 + 1.0) / (prior1 + 2.0)
T[y <= 0] = 1.0 / (prior0 + 2.0)
bin_loss = HalfBinomialLoss()
def loss_grad(AB):
# .astype below is needed to ensure y_true and raw_prediction have the
# same dtype. With result = np.float64(0) * np.array([1, 2], dtype=np.float32)
# - in Numpy 2, result.dtype is float64
# - in Numpy<2, result.dtype is float32
raw_prediction = -(AB[0] * F + AB[1]).astype(dtype=predictions.dtype)
l, g = bin_loss.loss_gradient(
y_true=T,
raw_prediction=raw_prediction,
sample_weight=sample_weight,
)
loss = l.sum()
# TODO: Remove casting to np.float64 when minimum supported SciPy is 1.11.2
# With SciPy >= 1.11.2, the LBFGS implementation will cast to float64
# https://github.com/scipy/scipy/pull/18825.
# Here we cast to float64 to support SciPy < 1.11.2
grad = np.asarray([-g @ F, -g.sum()], dtype=np.float64)
return loss, grad
AB0 = np.array([0.0, log((prior0 + 1.0) / (prior1 + 1.0))])
opt_result = minimize(
loss_grad,
AB0,
method="L-BFGS-B",
jac=True,
options={
"gtol": 1e-6,
"ftol": 64 * np.finfo(float).eps,
},
)
AB_ = opt_result.x
# The tuned multiplicative parameter is converted back to the original
# input feature scale. The offset parameter does not need rescaling since
# we did not rescale the outcome variable.
return AB_[0] / scale_constant, AB_[1]
class _SigmoidCalibration(RegressorMixin, BaseEstimator):
"""Sigmoid regression model.
Attributes
----------
a_ : float
The slope.
b_ : float
The intercept.
"""
def fit(self, X, y, sample_weight=None):
"""Fit the model using X, y as training data.
Parameters
----------
X : array-like of shape (n_samples,)
Training data.
y : array-like of shape (n_samples,)
Training target.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted.
Returns
-------
self : object
Returns an instance of self.
"""
X = column_or_1d(X)
y = column_or_1d(y)
X, y = indexable(X, y)
self.a_, self.b_ = _sigmoid_calibration(X, y, sample_weight)
return self
def predict(self, T):
"""Predict new data by linear interpolation.
Parameters
----------
T : array-like of shape (n_samples,)
Data to predict from.
Returns
-------
T_ : ndarray of shape (n_samples,)
The predicted data.
"""
T = column_or_1d(T)
return expit(-(self.a_ * T + self.b_))
@validate_params(
{
"y_true": ["array-like"],
"y_prob": ["array-like"],
"pos_label": [Real, str, "boolean", None],
"n_bins": [Interval(Integral, 1, None, closed="left")],
"strategy": [StrOptions({"uniform", "quantile"})],
},
prefer_skip_nested_validation=True,
)
def calibration_curve(
y_true,
y_prob,
*,
pos_label=None,
n_bins=5,
strategy="uniform",
):
"""Compute true and predicted probabilities for a calibration curve.
The method assumes the inputs come from a binary classifier, and
discretize the [0, 1] interval into bins.
Calibration curves may also be referred to as reliability diagrams.
Read more in the :ref:`User Guide <calibration>`.
Parameters
----------
y_true : array-like of shape (n_samples,)
True targets.
y_prob : array-like of shape (n_samples,)
Probabilities of the positive class.
pos_label : int, float, bool or str, default=None
The label of the positive class.
.. versionadded:: 1.1
n_bins : int, default=5
Number of bins to discretize the [0, 1] interval. A bigger number
requires more data. Bins with no samples (i.e. without
corresponding values in `y_prob`) will not be returned, thus the
returned arrays may have less than `n_bins` values.
strategy : {'uniform', 'quantile'}, default='uniform'
Strategy used to define the widths of the bins.
uniform
The bins have identical widths.
quantile
The bins have the same number of samples and depend on `y_prob`.
Returns
-------
prob_true : ndarray of shape (n_bins,) or smaller
The proportion of samples whose class is the positive class, in each
bin (fraction of positives).
prob_pred : ndarray of shape (n_bins,) or smaller
The mean predicted probability in each bin.
References
----------
Alexandru Niculescu-Mizil and Rich Caruana (2005) Predicting Good
Probabilities With Supervised Learning, in Proceedings of the 22nd
International Conference on Machine Learning (ICML).
See section 4 (Qualitative Analysis of Predictions).
Examples
--------
>>> import numpy as np
>>> from sklearn.calibration import calibration_curve
>>> y_true = np.array([0, 0, 0, 0, 1, 1, 1, 1, 1])
>>> y_pred = np.array([0.1, 0.2, 0.3, 0.4, 0.65, 0.7, 0.8, 0.9, 1.])
>>> prob_true, prob_pred = calibration_curve(y_true, y_pred, n_bins=3)
>>> prob_true
array([0. , 0.5, 1. ])
>>> prob_pred
array([0.2 , 0.525, 0.85 ])
"""
y_true = column_or_1d(y_true)
y_prob = column_or_1d(y_prob)
check_consistent_length(y_true, y_prob)
pos_label = _check_pos_label_consistency(pos_label, y_true)
if y_prob.min() < 0 or y_prob.max() > 1:
raise ValueError("y_prob has values outside [0, 1].")
labels = np.unique(y_true)
if len(labels) > 2:
raise ValueError(
f"Only binary classification is supported. Provided labels {labels}."
)
y_true = y_true == pos_label
if strategy == "quantile": # Determine bin edges by distribution of data
quantiles = np.linspace(0, 1, n_bins + 1)
bins = np.percentile(y_prob, quantiles * 100)
elif strategy == "uniform":
bins = np.linspace(0.0, 1.0, n_bins + 1)
else:
raise ValueError(
"Invalid entry to 'strategy' input. Strategy "
"must be either 'quantile' or 'uniform'."
)
binids = np.searchsorted(bins[1:-1], y_prob)
bin_sums = np.bincount(binids, weights=y_prob, minlength=len(bins))
bin_true = np.bincount(binids, weights=y_true, minlength=len(bins))
bin_total = np.bincount(binids, minlength=len(bins))
nonzero = bin_total != 0
prob_true = bin_true[nonzero] / bin_total[nonzero]
prob_pred = bin_sums[nonzero] / bin_total[nonzero]
return prob_true, prob_pred
class CalibrationDisplay(_BinaryClassifierCurveDisplayMixin):
"""Calibration curve (also known as reliability diagram) visualization.
It is recommended to use
:func:`~sklearn.calibration.CalibrationDisplay.from_estimator` or
:func:`~sklearn.calibration.CalibrationDisplay.from_predictions`
to create a `CalibrationDisplay`. All parameters are stored as attributes.
Read more about calibration in the :ref:`User Guide <calibration>` and
more about the scikit-learn visualization API in :ref:`visualizations`.
.. versionadded:: 1.0
Parameters
----------
prob_true : ndarray of shape (n_bins,)
The proportion of samples whose class is the positive class (fraction
of positives), in each bin.
prob_pred : ndarray of shape (n_bins,)
The mean predicted probability in each bin.
y_prob : ndarray of shape (n_samples,)
Probability estimates for the positive class, for each sample.
estimator_name : str, default=None
Name of estimator. If None, the estimator name is not shown.
pos_label : int, float, bool or str, default=None
The positive class when computing the calibration curve.
By default, `pos_label` is set to `estimators.classes_[1]` when using
`from_estimator` and set to 1 when using `from_predictions`.
.. versionadded:: 1.1
Attributes
----------
line_ : matplotlib Artist
Calibration curve.
ax_ : matplotlib Axes
Axes with calibration curve.
figure_ : matplotlib Figure
Figure containing the curve.
See Also
--------
calibration_curve : Compute true and predicted probabilities for a
calibration curve.
CalibrationDisplay.from_predictions : Plot calibration curve using true
and predicted labels.
CalibrationDisplay.from_estimator : Plot calibration curve using an
estimator and data.
Examples
--------
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.calibration import calibration_curve, CalibrationDisplay
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, random_state=0)
>>> clf = LogisticRegression(random_state=0)
>>> clf.fit(X_train, y_train)
LogisticRegression(random_state=0)
>>> y_prob = clf.predict_proba(X_test)[:, 1]
>>> prob_true, prob_pred = calibration_curve(y_test, y_prob, n_bins=10)
>>> disp = CalibrationDisplay(prob_true, prob_pred, y_prob)
>>> disp.plot()
<...>
"""
def __init__(
self, prob_true, prob_pred, y_prob, *, estimator_name=None, pos_label=None
):
self.prob_true = prob_true
self.prob_pred = prob_pred
self.y_prob = y_prob
self.estimator_name = estimator_name
self.pos_label = pos_label
def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
"""Plot visualization.
Extra keyword arguments will be passed to
:func:`matplotlib.pyplot.plot`.
Parameters
----------
ax : Matplotlib Axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
name : str, default=None
Name for labeling curve. If `None`, use `estimator_name` if
not `None`, otherwise no labeling is shown.
ref_line : bool, default=True
If `True`, plots a reference line representing a perfectly
calibrated classifier.
**kwargs : dict
Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
Returns
-------
display : :class:`~sklearn.calibration.CalibrationDisplay`
Object that stores computed values.
"""
self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=name)
info_pos_label = (
f"(Positive class: {self.pos_label})" if self.pos_label is not None else ""
)
line_kwargs = {"marker": "s", "linestyle": "-"}
if name is not None:
line_kwargs["label"] = name
line_kwargs.update(**kwargs)
ref_line_label = "Perfectly calibrated"
existing_ref_line = ref_line_label in self.ax_.get_legend_handles_labels()[1]
if ref_line and not existing_ref_line:
self.ax_.plot([0, 1], [0, 1], "k:", label=ref_line_label)
self.line_ = self.ax_.plot(self.prob_pred, self.prob_true, **line_kwargs)[0]
# We always have to show the legend for at least the reference line
self.ax_.legend(loc="lower right")
xlabel = f"Mean predicted probability {info_pos_label}"
ylabel = f"Fraction of positives {info_pos_label}"
self.ax_.set(xlabel=xlabel, ylabel=ylabel)
return self
@classmethod
def from_estimator(
cls,
estimator,
X,
y,
*,
n_bins=5,
strategy="uniform",
pos_label=None,
name=None,
ref_line=True,
ax=None,
**kwargs,
):
"""Plot calibration curve using a binary classifier and data.
A calibration curve, also known as a reliability diagram, uses inputs
from a binary classifier and plots the average predicted probability
for each bin against the fraction of positive classes, on the
y-axis.
Extra keyword arguments will be passed to
:func:`matplotlib.pyplot.plot`.
Read more about calibration in the :ref:`User Guide <calibration>` and
more about the scikit-learn visualization API in :ref:`visualizations`.
.. versionadded:: 1.0
Parameters
----------
estimator : estimator instance
Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
in which the last estimator is a classifier. The classifier must
have a :term:`predict_proba` method.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input values.
y : array-like of shape (n_samples,)
Binary target values.
n_bins : int, default=5
Number of bins to discretize the [0, 1] interval into when
calculating the calibration curve. A bigger number requires more
data.
strategy : {'uniform', 'quantile'}, default='uniform'
Strategy used to define the widths of the bins.
- `'uniform'`: The bins have identical widths.
- `'quantile'`: The bins have the same number of samples and depend
on predicted probabilities.
pos_label : int, float, bool or str, default=None
The positive class when computing the calibration curve.
By default, `estimators.classes_[1]` is considered as the
positive class.
.. versionadded:: 1.1
name : str, default=None
Name for labeling curve. If `None`, the name of the estimator is
used.
ref_line : bool, default=True
If `True`, plots a reference line representing a perfectly
calibrated classifier.
ax : matplotlib axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
**kwargs : dict
Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
Returns
-------
display : :class:`~sklearn.calibration.CalibrationDisplay`.
Object that stores computed values.
See Also
--------
CalibrationDisplay.from_predictions : Plot calibration curve using true
and predicted labels.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.calibration import CalibrationDisplay
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, random_state=0)
>>> clf = LogisticRegression(random_state=0)
>>> clf.fit(X_train, y_train)
LogisticRegression(random_state=0)
>>> disp = CalibrationDisplay.from_estimator(clf, X_test, y_test)
>>> plt.show()
"""
y_prob, pos_label, name = cls._validate_and_get_response_values(
estimator,
X,
y,
response_method="predict_proba",
pos_label=pos_label,
name=name,
)
return cls.from_predictions(
y,
y_prob,
n_bins=n_bins,
strategy=strategy,
pos_label=pos_label,
name=name,
ref_line=ref_line,
ax=ax,
**kwargs,
)
@classmethod
def from_predictions(
cls,
y_true,
y_prob,
*,
n_bins=5,
strategy="uniform",
pos_label=None,
name=None,
ref_line=True,
ax=None,
**kwargs,
):
"""Plot calibration curve using true labels and predicted probabilities.
Calibration curve, also known as reliability diagram, uses inputs
from a binary classifier and plots the average predicted probability
for each bin against the fraction of positive classes, on the
y-axis.
Extra keyword arguments will be passed to
:func:`matplotlib.pyplot.plot`.
Read more about calibration in the :ref:`User Guide <calibration>` and
more about the scikit-learn visualization API in :ref:`visualizations`.
.. versionadded:: 1.0
Parameters
----------
y_true : array-like of shape (n_samples,)
True labels.
y_prob : array-like of shape (n_samples,)
The predicted probabilities of the positive class.
n_bins : int, default=5
Number of bins to discretize the [0, 1] interval into when
calculating the calibration curve. A bigger number requires more
data.
strategy : {'uniform', 'quantile'}, default='uniform'
Strategy used to define the widths of the bins.
- `'uniform'`: The bins have identical widths.
- `'quantile'`: The bins have the same number of samples and depend
on predicted probabilities.
pos_label : int, float, bool or str, default=None
The positive class when computing the calibration curve.
By default `pos_label` is set to 1.
.. versionadded:: 1.1
name : str, default=None
Name for labeling curve.
ref_line : bool, default=True
If `True`, plots a reference line representing a perfectly
calibrated classifier.
ax : matplotlib axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
**kwargs : dict
Keyword arguments to be passed to :func:`matplotlib.pyplot.plot`.
Returns
-------
display : :class:`~sklearn.calibration.CalibrationDisplay`.
Object that stores computed values.
See Also
--------
CalibrationDisplay.from_estimator : Plot calibration curve using an
estimator and data.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.calibration import CalibrationDisplay
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, random_state=0)
>>> clf = LogisticRegression(random_state=0)
>>> clf.fit(X_train, y_train)
LogisticRegression(random_state=0)
>>> y_prob = clf.predict_proba(X_test)[:, 1]
>>> disp = CalibrationDisplay.from_predictions(y_test, y_prob)
>>> plt.show()
"""
pos_label_validated, name = cls._validate_from_predictions_params(
y_true, y_prob, sample_weight=None, pos_label=pos_label, name=name
)
prob_true, prob_pred = calibration_curve(
y_true, y_prob, n_bins=n_bins, strategy=strategy, pos_label=pos_label
)
disp = cls(
prob_true=prob_true,
prob_pred=prob_pred,
y_prob=y_prob,
estimator_name=name,
pos_label=pos_label_validated,
)
return disp.plot(ax=ax, ref_line=ref_line, **kwargs)