-iorSSKrSSKrSSKJr SSKJrJrJrJ r J r J r SSK J r SSKJrJr "SS \5rg) N)_safe_indexing)"_BinaryClassifierCurveDisplayMixin_check_param_lengths_convert_to_list_leaving_none_deprecate_estimator_name_despine_validate_style_kwargs)_get_response_values_binary)auc roc_curvec ^\rSrSrSrSSSSS.SjrU4SjrSSSSSSS .S jjr\SS S SSSSSSSS . Sj5r \SSS SSSSSSSSS. Sjj5r \SS S SSSSSSSS. Sj5r Sr U=r $)RocCurveDisplayaaROC 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 `. For guidance on interpreting these plots, refer to the :ref:`Model Evaluation Guide `. 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`. To label each curve, provide a list of strings. To avoid labeling individual curves that have the same appearance, this cannot be used in conjunction with `curve_kwargs` being a dictionary or None. 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 still `None`, no name is shown in the legend. .. versionadded:: 1.7 pos_label : int, float, bool or str, 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 estimator_name : str, default=None Name of estimator. If None, the estimator name is not shown. .. deprecated:: 1.7 `estimator_name` is deprecated and will be removed in 1.9. Use `name` instead. 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. 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() N deprecated)roc_aucname pos_labelestimator_namecVXlX lX0l[XdS5UlXPlg)Nz1.7)fprtprrrrr)selfrrrrrrs R/var/www/html/venv/lib/python3.13/site-packages/sklearn/metrics/_plot/roc_curve.py__init__RocCurveDisplay.__init__s& -nEJ "cf>[TU]XS9uUlUln[ UR 5n[ UR 5n[ UR5n[ U5nSU0n[U[5(a"[U5S:waURSU05 [X4S.USS9 X4XR4$)Naxrz self.roc_aucz'name' (or self.name))zself.fprzself.tprr)requiredoptional class_name) super_validate_plot_paramsax_figure_rrrr isinstancelistlenupdater)rr!rrrrr$ __class__s rr'%RocCurveDisplay._validate_plot_paramss',w'D'D'V$$,+DHH5+DHH5/ =,T2"G, dD ! !c$i1n OO4d; <"%7( &&rF)r curve_kwargsplot_chance_levelchance_level_kwdespinec URXS9upp[U5n [U[5(dAU S:a;U (a.[R "U 5[R "U 5S.n OSSS.n OU bU OS/U -n SU 0n UR"U UU S4SU0UD6nSS S S .n Uc0n[X5n/Ul [XU5H<upnURRURR"X40UD65 M> [UR5S:XaURS Ul URbS URS3OSnSU-nSU-nURRUSUSSS9 U(a$URR"S0UD6uUlOSUlU(a[#UR5 US R%S5cU(a+UR%S5bURR'SS9 U$)aPlot 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 `curve_kwargs`, and is not affected by `name`. To label each curve, provide a list of strings. To avoid labeling individual curves that have the same appearance, this cannot be used in conjunction with `curve_kwargs` being a dictionary or None. 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 `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 **kwargs : dict Keyword arguments to be passed to matplotlib's `plot`. .. deprecated:: 1.7 kwargs is deprecated and will be removed in 1.9. Pass matplotlib arguments to `curve_kwargs` as a dictionary instead. Returns ------- display : :class:`~sklearn.metrics.RocCurveDisplay` Object that stores computed values. r r")meanstdNmetricAUCr0zChance level (AUC = 0.5)kz--)labelcolor linestylerz (Positive label: )zFalse Positive RatezTrue Positive Rate)g{Gzg)\(?equal)xlabelxlimylabelylimaspectr:z lower right)loc)rr"rF)r'r,r*r+npr5r6_validate_curve_kwargsr line_zipextendr(plotrset chance_level_r getlegend)rr!rr0r1r2r3kwargsrrrn_curves legend_metricdefault_chance_level_line_kwline_kwinfo_pos_labelr@rBs rrLRocCurveDisplay.plotsP#'"<"<"<"N's8,--(Q,)+)9"&&/ R )-d ; !(!4g4&8:KG%w/M22      &    0( $  " O0 (  !$S|!< Cg JJ  dhhmmC@@ A"= tzz?a ADJ7;nn6P  0 2VX '7%6     $(HHMM$TO$T !T !%D   TXX  ?  w ' 3 /"5"5g">"J HHOO O . rTauto) sample_weightdrop_intermediateresponse_methodrrr!r0r1r2r3c  lURUUUUUUS9upnUR"SUUUUUUU U U U U S. UD6$)a Create a ROC Curve display from an estimator. For general information regarding `scikit-learn` visualization tools, see the :ref:`Visualization Guide `. For guidance on interpreting these plots, refer to the :ref:`Model Evaluation 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 : 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 **kwargs : dict Keyword arguments to be passed to matplotlib's `plot`. .. deprecated:: 1.7 kwargs is deprecated and will be removed in 1.9. Pass matplotlib arguments to `curve_kwargs` as a dictionary instead. 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. 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() )r[rr) y_truey_scorerYrZrrr!r0r1r2r3)!_validate_and_get_response_valuesfrom_predictions)cls estimatorXyrYrZr[rrr!r0r1r2r3rQr^s rfrom_estimatorRocCurveDisplay.from_estimator3swt$'#H#H  + $I$  D##  '/%/+    r) rYrZrrr!r0r1r2r3y_predc hUb&[U [5(aU S:Xd [S5e[U [5(aU S:Xd[R"S[ 5 U nUR XX5US9up[UUUUUS9unnn[UU5nU"UUUUUS9nUR"SUUU U U S.U D6$) u Plot ROC curve given the true and predicted values. For general information regarding `scikit-learn` visualization tools, see the :ref:`Visualization Guide `. For guidance on interpreting these plots, refer to the :ref:`Model Evaluation Guide `. .. 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, confidence values, or non-thresholded measure of decisions (as returned by “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 y_pred : array-like of shape (n_samples,) Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers). .. deprecated:: 1.7 `y_pred` is deprecated and will be removed in 1.9. Use `y_score` instead. **kwargs : dict Additional keywords arguments passed to matplotlib `plot` function. .. deprecated:: 1.7 kwargs is deprecated and will be removed in 1.9. Pass matplotlib arguments to `curve_kwargs` as a dictionary instead. 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. 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() rz`y_pred` and `y_score` cannot be both specified. Please use `y_score` only as `y_pred` is deprecated in 1.7 and will be removed in 1.9.zUy_pred is deprecated in 1.7 and will be removed in 1.9. Please use `y_score` instead.)rYrrrrYrZrrrrrr!r0r1r2r3r_) r*str ValueErrorwarningswarn FutureWarning!_validate_from_predictions_paramsrr rL)rbr]r^rYrZrrr!r0r1r2r3rhrQpos_label_validatedrr_rvizs rra RocCurveDisplay.from_predictionss@   vs # #,(>U 63''Fl,B MM4  G$'$I$I =TX%J% !  '/  S!c3-)  xx %/+     r) rYrZr[rr!rr0r1chance_level_kwargsr3c URUUUUUS9n///nnn[USUSS5Hunn[UU5n[U[UU5UUS9unnUcSO [UU5n[ UUUUUS9unnn[ UU5nUR U5 UR U5 UR U5 M U"UUUU US9nURUU U U U S 9$) aCreate 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, `estimators.classes_[1]` 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 `curve_kwargs`, and is not affected by `name`. To label each curve, provide a list of strings. To avoid labeling individual curves that have the same appearance, this cannot be used in conjunction with `curve_kwargs` being a dictionary or None. 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 `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 : ROC Curve visualization 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() )rYrrcindicestest)r[rNrjrkrl) _validate_from_cv_results_paramsrJrr rr appendrL)rb cv_resultsrdrerYrZr[rr!rr0r1rwr3 pos_label_ fpr_folds tpr_folds auc_foldsrc test_indicesr]rhrtsample_weight_foldrrrrus rfrom_cv_resultsRocCurveDisplay.from_cv_resultstsEj99  ' : +-b"i9 '* { #Z %:6%B( #I|$A|4F3q,/ /$ IFA!(#M<@  $$0"3 KCa#smG   S !   S !   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