_nd-\ddlmZddlmZddlmZddlmZddlmZGdd Z d S) ) _get_response)auc) roc_curve)_check_pos_label_consistency)check_matplotlib_supportczeZdZdZdddddZd dddZeddddddd d Zedddddd d ZdS)RocCurveDisplayaROC Curve visualization. It is recommend to use :func:`~sklearn.metrics.RocCurveDisplay.from_estimator` or :func:`~sklearn.metrics.RocCurveDisplay.from_predictions` to create a :class:`~sklearn.metrics.RocCurveDisplay`. 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. 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 = 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() <...> >>> plt.show() N)roc_aucestimator_name pos_labelcL||_||_||_||_||_dSN)r fprtprr r)selfrrr r rs ?lib/python3.11/site-packages/sklearn/metrics/_plot/roc_curve.py__init__zRocCurveDisplay.__init__Ns*, ")namec td||jn|}i}|j||d|jdd|d<n|jd|jd|d<n|||d<|jdi|ddlm}||\}}|j|j|j fi|\|_ |j d |j dnd }d |z}d |z} | || d|vr| d||_|j|_|S)aPlot 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 `estimator_name` if not `None`, otherwise no labeling is shown. **kwargs : dict Keyword arguments to be passed to matplotlib's `plot`. Returns ------- display : :class:`~sklearn.metrics.plot.RocCurveDisplay` Object that stores computed values. zRocCurveDisplay.plotNz (AUC = z0.2f)labelzAUC = z (Positive label: zFalse Positive RatezTrue Positive Rate)xlabelylabelz lower right)loc)r r r updatematplotlib.pyplotpyplotsubplotsplotrrline_rsetlegendax_figurefigure_) raxrkwargs line_kwargspltfiginfo_pos_labelrrs rr%zRocCurveDisplay.plotUsr. !!7888&*lt"" < #(8&*#H#HDL#H#H#H#HK \ %#?DL#?#?#?K  #'K  $$V$$$'''''' :llnnGC$(BBkBB 6:n6P 2 2 2 2 2VX '7%6 fV,,, k ! ! II-I ( ( (y  rTauto) sample_weightdrop_intermediateresponse_methodrrr,c t|jd| |jjn|}t||||\} }|jd|| |||| |d| S)a Create a ROC Curve display from an estimator. 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 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 : 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. 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. **kwargs : dict Keyword arguments to be passed to matplotlib's `plot`. Returns ------- display : :class:`~sklearn.metrics.plot.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() z.from_estimatorN)r5r)y_truey_predr3r4rr,rr )r __name__ __class__rfrom_predictions) cls estimatorXyr3r4r5rrr,r-r8s rfrom_estimatorzRocCurveDisplay.from_estimatorsn !CL!A!A!ABBB/3|y"++) +     $s#  '/      r)r3r4rrr,c t|jdt|||||\} } } t| | } |dn|}t ||}t | | | ||} | jd||d|S)u' Plot ROC curve given the true and predicted values. Read more in the :ref:`User Guide `. .. versionadded:: 1.0 Parameters ---------- y_true : array-like of shape (n_samples,) True labels. 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). 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. pos_label : str or int, default=None The label of the positive class. 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 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. **kwargs : dict Additional keywords arguments passed to matplotlib `plot` function. 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_pred = clf.decision_function(X_test) >>> RocCurveDisplay.from_predictions( ... y_test, y_pred) <...> >>> plt.show() z.from_predictions)rr3r4N Classifier)rrr r r)r,rr )r r9rrrr r%)r<r7r8r3r4rrr,r-rr_r vizs rr;z RocCurveDisplay.from_predictionssd !CL!C!C!CDDD  '/    S!c3--#|||0FCC gdi   sx32D33F333rr) r9 __module__ __qualname____doc__rr% classmethodr@r;r rrr r sAAF-1QU#####8D88888t  j j j j [j X   c4c4c4c4[c4c4c4rr N) baserrrr_baserutilsr r r rrrLs000000------V4V4V4V4V4V4V4V4V4V4r