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from .. import auc
from .. import roc_curve
from .base import _check_classifer_response_method
from ...utils import check_matplotlib_support
from ...base import is_classifier
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.
Attributes
----------
line_ : matplotlib Artist
ROC Curve.
ax_ : matplotlib Axes
Axes with ROC Curve.
figure_ : matplotlib Figure
Figure containing the 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
"""
def __init__(self, *, fpr, tpr, roc_auc=None, estimator_name=None):
self.fpr = fpr
self.tpr = tpr
self.roc_auc = roc_auc
self.estimator_name = estimator_name
@_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')
import matplotlib.pyplot as plt
if ax is None:
fig, ax = plt.subplots()
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)
self.line_ = ax.plot(self.fpr, self.tpr, **line_kwargs)[0]
ax.set_xlabel("False Positive Rate")
ax.set_ylabel("True Positive Rate")
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, **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.
Returns
-------
display : :class:`~sklearn.metrics.RocCurveDisplay`
Object that stores computed values.
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')
classification_error = (
"{} should be a binary classifier".format(estimator.__class__.__name__)
)
if not is_classifier(estimator):
raise ValueError(classification_error)
prediction_method = _check_classifer_response_method(estimator,
response_method)
y_pred = prediction_method(X)
if y_pred.ndim != 1:
if y_pred.shape[1] != 2:
raise ValueError(classification_error)
else:
y_pred = y_pred[:, 1]
pos_label = estimator.classes_[1]
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
)
return viz.plot(ax=ax, name=name, **kwargs)
|
