203 lines
6.6 KiB
Python
203 lines
6.6 KiB
Python
from .. import auc
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from .. import roc_curve
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from .base import _check_classifer_response_method
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from ...utils import check_matplotlib_support
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from ...base import is_classifier
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from ...utils.validation import _deprecate_positional_args
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class RocCurveDisplay:
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"""ROC Curve visualization.
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It is recommend to use :func:`~sklearn.metrics.plot_roc_curve` to create a
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visualizer. All parameters are stored as attributes.
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Read more in the :ref:`User Guide <visualizations>`.
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Parameters
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----------
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fpr : ndarray
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False positive rate.
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tpr : ndarray
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True positive rate.
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roc_auc : float, default=None
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Area under ROC curve. If None, the roc_auc score is not shown.
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estimator_name : str, default=None
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Name of estimator. If None, the estimator name is not shown.
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Attributes
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----------
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line_ : matplotlib Artist
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ROC Curve.
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ax_ : matplotlib Axes
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Axes with ROC Curve.
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figure_ : matplotlib Figure
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Figure containing the curve.
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Examples
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--------
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>>> import matplotlib.pyplot as plt # doctest: +SKIP
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>>> import numpy as np
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>>> from sklearn import metrics
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>>> y = np.array([0, 0, 1, 1])
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>>> pred = np.array([0.1, 0.4, 0.35, 0.8])
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>>> fpr, tpr, thresholds = metrics.roc_curve(y, pred)
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>>> roc_auc = metrics.auc(fpr, tpr)
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>>> display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,\
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estimator_name='example estimator')
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>>> display.plot() # doctest: +SKIP
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>>> plt.show() # doctest: +SKIP
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"""
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def __init__(self, *, fpr, tpr, roc_auc=None, estimator_name=None):
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self.fpr = fpr
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self.tpr = tpr
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self.roc_auc = roc_auc
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self.estimator_name = estimator_name
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@_deprecate_positional_args
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def plot(self, ax=None, *, name=None, **kwargs):
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"""Plot visualization
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Extra keyword arguments will be passed to matplotlib's ``plot``.
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Parameters
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----------
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ax : matplotlib axes, default=None
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Axes object to plot on. If `None`, a new figure and axes is
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created.
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name : str, default=None
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Name of ROC Curve for labeling. If `None`, use the name of the
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estimator.
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Returns
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-------
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display : :class:`~sklearn.metrics.plot.RocCurveDisplay`
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Object that stores computed values.
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"""
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check_matplotlib_support('RocCurveDisplay.plot')
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import matplotlib.pyplot as plt
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if ax is None:
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fig, ax = plt.subplots()
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name = self.estimator_name if name is None else name
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line_kwargs = {}
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if self.roc_auc is not None and name is not None:
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line_kwargs["label"] = f"{name} (AUC = {self.roc_auc:0.2f})"
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elif self.roc_auc is not None:
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line_kwargs["label"] = f"AUC = {self.roc_auc:0.2f}"
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elif name is not None:
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line_kwargs["label"] = name
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line_kwargs.update(**kwargs)
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self.line_ = ax.plot(self.fpr, self.tpr, **line_kwargs)[0]
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ax.set_xlabel("False Positive Rate")
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ax.set_ylabel("True Positive Rate")
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if "label" in line_kwargs:
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ax.legend(loc='lower right')
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self.ax_ = ax
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self.figure_ = ax.figure
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return self
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@_deprecate_positional_args
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def plot_roc_curve(estimator, X, y, *, sample_weight=None,
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drop_intermediate=True, response_method="auto",
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name=None, ax=None, **kwargs):
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"""Plot Receiver operating characteristic (ROC) curve.
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Extra keyword arguments will be passed to matplotlib's `plot`.
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Read more in the :ref:`User Guide <visualizations>`.
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Parameters
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----------
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estimator : estimator instance
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Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
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in which the last estimator is a classifier.
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X : {array-like, sparse matrix} of shape (n_samples, n_features)
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Input values.
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y : array-like of shape (n_samples,)
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Target values.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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drop_intermediate : boolean, default=True
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Whether to drop some suboptimal thresholds which would not appear
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on a plotted ROC curve. This is useful in order to create lighter
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ROC curves.
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response_method : {'predict_proba', 'decision_function', 'auto'} \
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default='auto'
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Specifies whether to use :term:`predict_proba` or
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:term:`decision_function` as the target response. If set to 'auto',
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:term:`predict_proba` is tried first and if it does not exist
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:term:`decision_function` is tried next.
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name : str, default=None
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Name of ROC Curve for labeling. If `None`, use the name of the
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estimator.
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ax : matplotlib axes, default=None
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Axes object to plot on. If `None`, a new figure and axes is created.
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Returns
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-------
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display : :class:`~sklearn.metrics.RocCurveDisplay`
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Object that stores computed values.
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Examples
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--------
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>>> import matplotlib.pyplot as plt # doctest: +SKIP
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>>> from sklearn import datasets, metrics, model_selection, svm
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>>> X, y = datasets.make_classification(random_state=0)
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>>> X_train, X_test, y_train, y_test = model_selection.train_test_split(\
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X, y, random_state=0)
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>>> clf = svm.SVC(random_state=0)
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>>> clf.fit(X_train, y_train)
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SVC(random_state=0)
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>>> metrics.plot_roc_curve(clf, X_test, y_test) # doctest: +SKIP
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>>> plt.show() # doctest: +SKIP
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"""
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check_matplotlib_support('plot_roc_curve')
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classification_error = (
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"{} should be a binary classifier".format(estimator.__class__.__name__)
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)
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if not is_classifier(estimator):
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raise ValueError(classification_error)
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prediction_method = _check_classifer_response_method(estimator,
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response_method)
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y_pred = prediction_method(X)
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if y_pred.ndim != 1:
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if y_pred.shape[1] != 2:
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raise ValueError(classification_error)
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else:
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y_pred = y_pred[:, 1]
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pos_label = estimator.classes_[1]
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fpr, tpr, _ = roc_curve(y, y_pred, pos_label=pos_label,
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sample_weight=sample_weight,
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drop_intermediate=drop_intermediate)
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roc_auc = auc(fpr, tpr)
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name = estimator.__class__.__name__ if name is None else name
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viz = RocCurveDisplay(
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fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name=name
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)
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return viz.plot(ax=ax, name=name, **kwargs)
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