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sklearn/metrics/_plot/roc_curve.py
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François Paugam
MNT Clean-up deprecations for 1.9 in RocCurveDisplay (#33823)
22 апр 2026, 13:50
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22 апр 2026, 13:50
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# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause from sklearn.metrics._ranking import auc, roc_curve from sklearn.utils import _safe_indexing from sklearn.utils._plotting import ( _BinaryClassifierCurveDisplayMixin, _check_param_lengths, _convert_to_list_leaving_none, _despine, _validate_style_kwargs, ) from sklearn.utils._response import _get_response_values_binary class RocCurveDisplay(_BinaryClassifierCurveDisplayMixin): """ROC Curve visualization. It is recommended to use :func:`~sklearn.metrics.RocCurveDisplay.from_estimator` or :func:`~sklearn.metrics.RocCurveDisplay.from_predictions` or :func:`~sklearn.metrics.RocCurveDisplay.from_cv_results` to create a :class:`~sklearn.metrics.RocCurveDisplay`. All parameters are stored as attributes. For general information regarding `scikit-learn` visualization tools, see the :ref:`Visualization Guide <visualizations>`. For guidance on interpreting these plots, refer to the :ref:`Model Evaluation Guide <roc_metrics>`. Parameters ---------- fpr : ndarray or list of ndarrays False positive rates. Each ndarray should contain values for a single curve. If plotting multiple curves, list should be of same length as `tpr`. .. versionchanged:: 1.7 Now accepts a list for plotting multiple curves. tpr : ndarray or list of ndarrays True positive rates. Each ndarray should contain values for a single curve. If plotting multiple curves, list should be of same length as `fpr`. .. versionchanged:: 1.7 Now accepts a list for plotting multiple curves. roc_auc : float or list of floats, default=None Area under ROC curve, used for labeling each curve in the legend. If plotting multiple curves, should be a list of the same length as `fpr` and `tpr`. If `None`, ROC AUC scores are not shown in the legend. .. versionchanged:: 1.7 Now accepts a list for plotting multiple curves. name : str or list of str, default=None Name for labeling legend entries. The number of legend entries is determined by the `curve_kwargs` passed to `plot`, and is not affected by `name`. If a string is provided, it will be used to either label the single legend entry or if there are multiple legend entries, label each individual curve with the same name. If a list is provided, it will be used to label each curve individually. Passing a list will raise an error if `curve_kwargs` is not a list to avoid labeling individual curves that have the same appearance. If `None`, no name is shown in the legend. .. versionchanged:: 1.7 `estimator_name` was deprecated in favor of `name` and now accepts a list for plotting multiple curves. pos_label : int, float, bool or str, default=None The class considered the positive class when ROC AUC metrics computed. If not `None`, this value is displayed in the x- and y-axes labels. .. versionadded:: 0.24 Attributes ---------- line_ : matplotlib Artist or list of matplotlib Artists ROC Curves. .. versionchanged:: 1.7 This attribute can now be a list of Artists, for when multiple curves are plotted. 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. RocCurveDisplay.from_cv_results : Plot multi-fold ROC curves given cross-validation results. 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_true = np.array([0, 0, 1, 1]) >>> y_score = np.array([0.1, 0.4, 0.35, 0.8]) >>> fpr, tpr, thresholds = metrics.roc_curve(y_true, y_score) >>> roc_auc = metrics.auc(fpr, tpr) >>> display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, ... name='example estimator') >>> display.plot() <...> >>> plt.show() """ def __init__( self, *, fpr, tpr, roc_auc=None, name=None, pos_label=None, ): self.fpr = fpr self.tpr = tpr self.roc_auc = roc_auc self.name = name self.pos_label = pos_label def _validate_plot_params(self, *, ax, name): self.ax_, self.figure_, name = super()._validate_plot_params(ax=ax, name=name) fpr = _convert_to_list_leaving_none(self.fpr) tpr = _convert_to_list_leaving_none(self.tpr) roc_auc = _convert_to_list_leaving_none(self.roc_auc) name = _convert_to_list_leaving_none(name) optional = {"self.roc_auc": roc_auc} if isinstance(name, list) and len(name) != 1: optional.update({"'name' (or self.name)": name}) _check_param_lengths( required={"self.fpr": fpr, "self.tpr": tpr}, optional=optional, class_name="RocCurveDisplay", ) return fpr, tpr, roc_auc, name def plot( self, ax=None, *, name=None, curve_kwargs=None, plot_chance_level=False, chance_level_kw=None, despine=False, ): """Plot visualization. Parameters ---------- ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. name : str or list of str, default=None Name for labeling legend entries. The number of legend entries is determined by the `curve_kwargs` passed to `plot`, and is not affected by `name`. If a string is provided, it will be used to either label the single legend entry or if there are multiple legend entries, label each individual curve with the same name. If a list is provided, it will be used to label each curve individually. Passing a list will raise an error if `curve_kwargs` is not a list to avoid labeling individual curves that have the same appearance. If `None`, set to `name` provided at `RocCurveDisplay` initialization. If still `None`, no name is shown in the legend. .. versionadded:: 1.7 curve_kwargs : dict or list of dict, default=None Keywords arguments to be passed to matplotlib's `plot` function to draw individual ROC curves. For single curve plotting, should be a dictionary. For multi-curve plotting, if a list is provided the parameters are applied to the ROC curves of each CV fold sequentially and a legend entry is added for each curve. If a single dictionary is provided, the same parameters are applied to all ROC curves and a single legend entry for all curves is added, labeled with the mean ROC AUC score. .. versionadded:: 1.7 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 despine : bool, default=False Whether to remove the top and right spines from the plot. .. versionadded:: 1.6 Returns ------- display : :class:`~sklearn.metrics.RocCurveDisplay` Object that stores computed values. """ fpr, tpr, roc_auc, name = self._validate_plot_params(ax=ax, name=name) n_curves = len(fpr) roc_auc, legend_metric = self._get_legend_metric( curve_kwargs, n_curves, roc_auc ) curve_kwargs = self._validate_curve_kwargs( n_curves, name, legend_metric, "AUC", curve_kwargs=curve_kwargs, default_multi_curve_kwargs={ "alpha": 0.5, "linestyle": "--", "color": "blue", }, ) default_chance_level_line_kw = { "label": "Chance level (AUC = 0.5)", "color": "k", "linestyle": "--", } if chance_level_kw is None: chance_level_kw = {} chance_level_kw = _validate_style_kwargs( default_chance_level_line_kw, chance_level_kw ) self.line_ = [] for fpr, tpr, line_kw in zip(fpr, tpr, curve_kwargs): self.line_.extend(self.ax_.plot(fpr, tpr, **line_kw)) # Return single artist if only one curve is plotted if len(self.line_) == 1: self.line_ = self.line_[0] 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_kw) else: self.chance_level_ = None if despine: _despine(self.ax_) if curve_kwargs[0].get("label") is not None or ( plot_chance_level and chance_level_kw.get("label") is not None ): 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, curve_kwargs=None, plot_chance_level=False, chance_level_kw=None, despine=False, ): """Create a ROC Curve display from an estimator. For general information regarding `scikit-learn` visualization tools, see the :ref:`Visualization Guide <visualizations>`. For guidance on interpreting these plots, refer to the :ref:`Model Evaluation Guide <roc_metrics>`. 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 thresholds where the resulting point is collinear with its neighbors in ROC space. This has no effect on the ROC AUC or visual shape of the curve, but reduces the number of plotted points. 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. 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. curve_kwargs : dict, default=None Keywords arguments to be passed to matplotlib's `plot` function. .. versionadded:: 1.7 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 despine : bool, default=False Whether to remove the top and right spines from the plot. .. versionadded:: 1.6 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. RocCurveDisplay.from_cv_results : Plot multi-fold ROC curves given cross-validation results. 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_score, 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_score=y_score, sample_weight=sample_weight, drop_intermediate=drop_intermediate, pos_label=pos_label, name=name, ax=ax, curve_kwargs=curve_kwargs, plot_chance_level=plot_chance_level, chance_level_kw=chance_level_kw, despine=despine, ) @classmethod def from_predictions( cls, y_true, y_score=None, *, sample_weight=None, drop_intermediate=True, pos_label=None, name=None, ax=None, curve_kwargs=None, plot_chance_level=False, chance_level_kw=None, despine=False, ): """Plot ROC curve given the true and predicted values. For general information regarding `scikit-learn` visualization tools, see the :ref:`Visualization Guide <visualizations>`. For guidance on interpreting these plots, refer to the :ref:`Model Evaluation Guide <roc_metrics>`. .. versionadded:: 1.0 Parameters ---------- y_true : array-like of shape (n_samples,) True labels. y_score : array-like of shape (n_samples,) Target scores, can either be probability estimates of the positive class or non-thresholded decision values (as returned by :term:`decision_function` on some classifiers). .. versionadded:: 1.7 `y_pred` has been renamed to `y_score`. sample_weight : array-like of shape (n_samples,), default=None Sample weights. drop_intermediate : bool, default=True Whether to drop thresholds where the resulting point is collinear with its neighbors in ROC space. This has no effect on the ROC AUC or visual shape of the curve, but reduces the number of plotted points. pos_label : int, float, bool or str, default=None The label of the positive class when computing the ROC AUC. 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 legend 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. curve_kwargs : dict, default=None Keywords arguments to be passed to matplotlib's `plot` function. .. versionadded:: 1.7 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 despine : bool, default=False Whether to remove the top and right spines from the plot. .. versionadded:: 1.6 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. RocCurveDisplay.from_cv_results : Plot multi-fold ROC curves given cross-validation results. 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_score = clf.decision_function(X_test) >>> RocCurveDisplay.from_predictions(y_test, y_score) <...> >>> plt.show() """ pos_label_validated, name = cls._validate_from_predictions_params( y_true, y_score, sample_weight=sample_weight, pos_label=pos_label, name=name ) fpr, tpr, _ = roc_curve( y_true, y_score, 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, name=name, pos_label=pos_label_validated, ) return viz.plot( ax=ax, curve_kwargs=curve_kwargs, plot_chance_level=plot_chance_level, chance_level_kw=chance_level_kw, despine=despine, ) @classmethod def from_cv_results( cls, cv_results, X, y, *, sample_weight=None, drop_intermediate=True, response_method="auto", pos_label=None, ax=None, name=None, curve_kwargs=None, plot_chance_level=False, chance_level_kwargs=None, despine=False, ): """Create a multi-fold ROC curve display given cross-validation results. .. versionadded:: 1.7 Parameters ---------- cv_results : dict Dictionary as returned by :func:`~sklearn.model_selection.cross_validate` using `return_estimator=True` and `return_indices=True` (i.e., dictionary should contain the keys "estimator" and "indices"). 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, `estimator.classes_[1]` (using `estimator` from `cv_results`) is considered as the positive class. ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. name : str or list of str, default=None Name for labeling legend entries. The number of legend entries is determined by the `curve_kwargs` passed to `plot`, and is not affected by `name`. If a string is provided, it will be used to either label the single legend entry or if there are multiple legend entries, label each individual curve with the same name. If a list is provided, it will be used to label each curve individually. Passing a list will raise an error if `curve_kwargs` is not a list to avoid labeling individual curves that have the same appearance. If `None`, no name is shown in the legend. curve_kwargs : dict or list of dict, default=None Keywords arguments to be passed to matplotlib's `plot` function to draw individual ROC curves. If a list is provided the parameters are applied to the ROC curves of each CV fold sequentially and a legend entry is added for each curve. If a single dictionary is provided, the same parameters are applied to all ROC curves and a single legend entry for all curves is added, labeled with the mean ROC AUC score. plot_chance_level : bool, default=False Whether to plot the chance level. chance_level_kwargs : dict, default=None Keyword arguments to be passed to matplotlib's `plot` for rendering the chance level line. despine : bool, default=False Whether to remove the top and right spines from the plot. Returns ------- display : :class:`~sklearn.metrics.RocCurveDisplay` The multi-fold ROC curve display. 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 : 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 cross_validate >>> from sklearn.svm import SVC >>> X, y = make_classification(random_state=0) >>> clf = SVC(random_state=0) >>> cv_results = cross_validate( ... clf, X, y, cv=3, return_estimator=True, return_indices=True) >>> RocCurveDisplay.from_cv_results(cv_results, X, y) <...> >>> plt.show() """ cls._validate_from_cv_results_params( cv_results, X, y, sample_weight=sample_weight, ) fpr_folds, tpr_folds, auc_folds = [], [], [] for estimator, test_indices in zip( cv_results["estimator"], cv_results["indices"]["test"] ): y_true = _safe_indexing(y, test_indices) y_score, pos_label_ = _get_response_values_binary( estimator, _safe_indexing(X, test_indices), response_method=response_method, pos_label=pos_label, ) sample_weight_fold = ( None if sample_weight is None else _safe_indexing(sample_weight, test_indices) ) fpr, tpr, _ = roc_curve( y_true, y_score, pos_label=pos_label_, sample_weight=sample_weight_fold, drop_intermediate=drop_intermediate, ) roc_auc = auc(fpr, tpr) fpr_folds.append(fpr) tpr_folds.append(tpr) auc_folds.append(roc_auc) viz = cls( fpr=fpr_folds, tpr=tpr_folds, roc_auc=auc_folds, name=name, pos_label=pos_label_, ) return viz.plot( ax=ax, curve_kwargs=curve_kwargs, plot_chance_level=plot_chance_level, chance_level_kw=chance_level_kwargs, despine=despine, )