ai-content-maker/.venv/Lib/site-packages/sklearn/metrics/_plot/roc_curve.py

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2024-05-03 04:18:51 +03:00
from ...utils._plotting import _BinaryClassifierCurveDisplayMixin
from .._ranking import auc, roc_curve
class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin):
"""ROC Curve visualization.
It is recommend to use
:func:`~sklearn.metrics.RocCurveDisplay.from_estimator` or
:func:`~sklearn.metrics.RocCurveDisplay.from_predictions` to create
a :class:`~sklearn.metrics.RocCurveDisplay`. All parameters are
stored as attributes.
Read more in the :ref:`User Guide <visualizations>`.
Parameters
----------
fpr : ndarray
False positive rate.
tpr : ndarray
True positive rate.
roc_auc : float, default=None
Area under ROC curve. If None, the roc_auc score is not shown.
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 class considered as the positive class when computing the roc auc
metrics. By default, `estimators.classes_[1]` is considered
as the positive class.
.. versionadded:: 0.24
Attributes
----------
line_ : matplotlib Artist
ROC Curve.
chance_level_ : matplotlib Artist or None
The chance level line. It is `None` if the chance level is not plotted.
.. versionadded:: 1.3
ax_ : matplotlib Axes
Axes with ROC Curve.
figure_ : matplotlib Figure
Figure containing the curve.
See Also
--------
roc_curve : Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_estimator : Plot Receiver Operating Characteristic
(ROC) curve given an estimator and some data.
RocCurveDisplay.from_predictions : Plot Receiver Operating Characteristic
(ROC) curve given the true and predicted values.
roc_auc_score : Compute the area under the ROC curve.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> from sklearn import metrics
>>> y = np.array([0, 0, 1, 1])
>>> pred = np.array([0.1, 0.4, 0.35, 0.8])
>>> fpr, tpr, thresholds = metrics.roc_curve(y, pred)
>>> roc_auc = metrics.auc(fpr, tpr)
>>> display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,
... estimator_name='example estimator')
>>> display.plot()
<...>
>>> plt.show()
"""
def __init__(self, *, fpr, tpr, roc_auc=None, estimator_name=None, pos_label=None):
self.estimator_name = estimator_name
self.fpr = fpr
self.tpr = tpr
self.roc_auc = roc_auc
self.pos_label = pos_label
def plot(
self,
ax=None,
*,
name=None,
plot_chance_level=False,
chance_level_kw=None,
**kwargs,
):
"""Plot visualization.
Extra keyword arguments will be passed to matplotlib's ``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 of ROC Curve for labeling. If `None`, use `estimator_name` if
not `None`, otherwise no labeling is shown.
plot_chance_level : bool, default=False
Whether to plot the chance level.
.. versionadded:: 1.3
chance_level_kw : dict, default=None
Keyword arguments to be passed to matplotlib's `plot` for rendering
the chance level line.
.. versionadded:: 1.3
**kwargs : dict
Keyword arguments to be passed to matplotlib's `plot`.
Returns
-------
display : :class:`~sklearn.metrics.RocCurveDisplay`
Object that stores computed values.
"""
self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=name)
line_kwargs = {}
if self.roc_auc is not None and name is not None:
line_kwargs["label"] = f"{name} (AUC = {self.roc_auc:0.2f})"
elif self.roc_auc is not None:
line_kwargs["label"] = f"AUC = {self.roc_auc:0.2f}"
elif name is not None:
line_kwargs["label"] = name
line_kwargs.update(**kwargs)
chance_level_line_kw = {
"label": "Chance level (AUC = 0.5)",
"color": "k",
"linestyle": "--",
}
if chance_level_kw is not None:
chance_level_line_kw.update(**chance_level_kw)
(self.line_,) = self.ax_.plot(self.fpr, self.tpr, **line_kwargs)
info_pos_label = (
f" (Positive label: {self.pos_label})" if self.pos_label is not None else ""
)
xlabel = "False Positive Rate" + info_pos_label
ylabel = "True Positive Rate" + info_pos_label
self.ax_.set(
xlabel=xlabel,
xlim=(-0.01, 1.01),
ylabel=ylabel,
ylim=(-0.01, 1.01),
aspect="equal",
)
if plot_chance_level:
(self.chance_level_,) = self.ax_.plot(
(0, 1), (0, 1), **chance_level_line_kw
)
else:
self.chance_level_ = None
if "label" in line_kwargs or "label" in chance_level_line_kw:
self.ax_.legend(loc="lower right")
return self
@classmethod
def from_estimator(
cls,
estimator,
X,
y,
*,
sample_weight=None,
drop_intermediate=True,
response_method="auto",
pos_label=None,
name=None,
ax=None,
plot_chance_level=False,
chance_level_kw=None,
**kwargs,
):
"""Create a ROC Curve display from an estimator.
Parameters
----------
estimator : estimator instance
Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
in which the last estimator is a classifier.
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input values.
y : array-like of shape (n_samples,)
Target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
drop_intermediate : bool, default=True
Whether to drop some suboptimal thresholds which would not appear
on a plotted ROC curve. This is useful in order to create lighter
ROC curves.
response_method : {'predict_proba', 'decision_function', 'auto'} \
default='auto'
Specifies whether to use :term:`predict_proba` or
:term:`decision_function` as the target response. If set to 'auto',
:term:`predict_proba` is tried first and if it does not exist
:term:`decision_function` is tried next.
pos_label : int, float, bool or str, default=None
The class considered as the positive class when computing the roc auc
metrics. By default, `estimators.classes_[1]` is considered
as the positive class.
name : str, default=None
Name of ROC Curve for labeling. If `None`, use the name of the
estimator.
ax : matplotlib axes, default=None
Axes object to plot on. If `None`, a new figure and axes is created.
plot_chance_level : bool, default=False
Whether to plot the chance level.
.. versionadded:: 1.3
chance_level_kw : dict, default=None
Keyword arguments to be passed to matplotlib's `plot` for rendering
the chance level line.
.. versionadded:: 1.3
**kwargs : dict
Keyword arguments to be passed to matplotlib's `plot`.
Returns
-------
display : :class:`~sklearn.metrics.RocCurveDisplay`
The ROC Curve display.
See Also
--------
roc_curve : Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_predictions : ROC Curve visualization given the
probabilities of scores of a classifier.
roc_auc_score : Compute the area under the ROC curve.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import RocCurveDisplay
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.svm import SVC
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, random_state=0)
>>> clf = SVC(random_state=0).fit(X_train, y_train)
>>> RocCurveDisplay.from_estimator(
... clf, X_test, y_test)
<...>
>>> plt.show()
"""
y_pred, pos_label, name = cls._validate_and_get_response_values(
estimator,
X,
y,
response_method=response_method,
pos_label=pos_label,
name=name,
)
return cls.from_predictions(
y_true=y,
y_pred=y_pred,
sample_weight=sample_weight,
drop_intermediate=drop_intermediate,
name=name,
ax=ax,
pos_label=pos_label,
plot_chance_level=plot_chance_level,
chance_level_kw=chance_level_kw,
**kwargs,
)
@classmethod
def from_predictions(
cls,
y_true,
y_pred,
*,
sample_weight=None,
drop_intermediate=True,
pos_label=None,
name=None,
ax=None,
plot_chance_level=False,
chance_level_kw=None,
**kwargs,
):
"""Plot ROC curve given the true and predicted values.
Read more in the :ref:`User Guide <visualizations>`.
.. versionadded:: 1.0
Parameters
----------
y_true : array-like of shape (n_samples,)
True labels.
y_pred : array-like of shape (n_samples,)
Target scores, can either be probability estimates of the positive
class, confidence values, or non-thresholded measure of decisions
(as returned by decision_function on some classifiers).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
drop_intermediate : bool, default=True
Whether to drop some suboptimal thresholds which would not appear
on a plotted ROC curve. This is useful in order to create lighter
ROC curves.
pos_label : int, float, bool or str, default=None
The label of the positive class. When `pos_label=None`, if `y_true`
is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an
error will be raised.
name : str, default=None
Name of ROC curve for labeling. If `None`, name will be set to
`"Classifier"`.
ax : matplotlib axes, default=None
Axes object to plot on. If `None`, a new figure and axes is
created.
plot_chance_level : bool, default=False
Whether to plot the chance level.
.. versionadded:: 1.3
chance_level_kw : dict, default=None
Keyword arguments to be passed to matplotlib's `plot` for rendering
the chance level line.
.. versionadded:: 1.3
**kwargs : dict
Additional keywords arguments passed to matplotlib `plot` function.
Returns
-------
display : :class:`~sklearn.metrics.RocCurveDisplay`
Object that stores computed values.
See Also
--------
roc_curve : Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay.from_estimator : ROC Curve visualization given an
estimator and some data.
roc_auc_score : Compute the area under the ROC curve.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import RocCurveDisplay
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.svm import SVC
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, random_state=0)
>>> clf = SVC(random_state=0).fit(X_train, y_train)
>>> y_pred = clf.decision_function(X_test)
>>> RocCurveDisplay.from_predictions(
... y_test, y_pred)
<...>
>>> plt.show()
"""
pos_label_validated, name = cls._validate_from_predictions_params(
y_true, y_pred, sample_weight=sample_weight, pos_label=pos_label, name=name
)
fpr, tpr, _ = roc_curve(
y_true,
y_pred,
pos_label=pos_label,
sample_weight=sample_weight,
drop_intermediate=drop_intermediate,
)
roc_auc = auc(fpr, tpr)
viz = cls(
fpr=fpr,
tpr=tpr,
roc_auc=roc_auc,
estimator_name=name,
pos_label=pos_label_validated,
)
return viz.plot(
ax=ax,
name=name,
plot_chance_level=plot_chance_level,
chance_level_kw=chance_level_kw,
**kwargs,
)