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 `. 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 `. 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)