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roc_curve.py
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roc_curve.py
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from .base import _get_response
from .. import auc
from .. import roc_curve
from ...utils import check_matplotlib_support
from ...utils.validation import _deprecate_positional_args
class RocCurveDisplay:
"""ROC Curve visualization.
It is recommend to use :func:`~sklearn.metrics.plot_roc_curve` to create a
visualizer. 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 : str or int, 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.
ax_ : matplotlib Axes
Axes with ROC Curve.
figure_ : matplotlib Figure
Figure containing the curve.
See Also
--------
roc_curve : Compute Receiver operating characteristic (ROC) curve.
plot_roc_curve : Plot Receiver operating characteristic (ROC) curve.
roc_auc_score : Compute the area under the ROC curve.
Examples
--------
>>> import matplotlib.pyplot as plt # doctest: +SKIP
>>> 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() # doctest: +SKIP
>>> plt.show() # doctest: +SKIP
"""
@_deprecate_positional_args
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
@_deprecate_positional_args
def plot(self, ax=None, *, name=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 the name of the
estimator.
Returns
-------
display : :class:`~sklearn.metrics.plot.RocCurveDisplay`
Object that stores computed values.
"""
check_matplotlib_support('RocCurveDisplay.plot')
name = self.estimator_name if name is None else 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)
import matplotlib.pyplot as plt
if ax is None:
fig, ax = plt.subplots()
self.line_, = 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
ax.set(xlabel=xlabel, ylabel=ylabel)
if "label" in line_kwargs:
ax.legend(loc="lower right")
self.ax_ = ax
self.figure_ = ax.figure
return self
@_deprecate_positional_args
def plot_roc_curve(estimator, X, y, *, sample_weight=None,
drop_intermediate=True, response_method="auto",
name=None, ax=None, pos_label=None, **kwargs):
"""Plot Receiver operating characteristic (ROC) curve.
Extra keyword arguments will be passed to matplotlib's `plot`.
Read more in the :ref:`User Guide <visualizations>`.
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 : boolean, 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.
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.
pos_label : str or int, 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
Returns
-------
display : :class:`~sklearn.metrics.RocCurveDisplay`
Object that stores computed values.
See Also
--------
roc_curve : Compute Receiver operating characteristic (ROC) curve.
RocCurveDisplay : ROC Curve visualization.
roc_auc_score : Compute the area under the ROC curve.
Examples
--------
>>> import matplotlib.pyplot as plt # doctest: +SKIP
>>> from sklearn import datasets, metrics, model_selection, svm
>>> X, y = datasets.make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = model_selection.train_test_split(
... X, y, random_state=0)
>>> clf = svm.SVC(random_state=0)
>>> clf.fit(X_train, y_train)
SVC(random_state=0)
>>> metrics.plot_roc_curve(clf, X_test, y_test) # doctest: +SKIP
>>> plt.show() # doctest: +SKIP
"""
check_matplotlib_support('plot_roc_curve')
y_pred, pos_label = _get_response(
X, estimator, response_method, pos_label=pos_label)
fpr, tpr, _ = roc_curve(y, y_pred, pos_label=pos_label,
sample_weight=sample_weight,
drop_intermediate=drop_intermediate)
roc_auc = auc(fpr, tpr)
name = estimator.__class__.__name__ if name is None else name
viz = RocCurveDisplay(
fpr=fpr,
tpr=tpr,
roc_auc=roc_auc,
estimator_name=name,
pos_label=pos_label
)
return viz.plot(ax=ax, name=name, **kwargs)