U _{j*@s4ddlZddlmZddlmZGdddeZdS)N)"_BinaryClassifierCurveDisplayMixin) det_curvec@sbeZdZdZdddddZedddddddd Zeddddd d d Zddd ddZdS)DetCurveDisplayaDET curve visualization. It is recommend to use :func:`~sklearn.metrics.DetCurveDisplay.from_estimator` or :func:`~sklearn.metrics.DetCurveDisplay.from_predictions` to create a visualizer. All parameters are stored as attributes. Read more in the :ref:`User Guide `. .. versionadded:: 0.24 Parameters ---------- fpr : ndarray False positive rate. fnr : ndarray False negative rate. estimator_name : str, default=None Name of estimator. If None, the estimator name is not shown. pos_label : int, float, bool or str, default=None The label of the positive class. Attributes ---------- line_ : matplotlib Artist DET Curve. ax_ : matplotlib Axes Axes with DET Curve. figure_ : matplotlib Figure Figure containing the curve. See Also -------- det_curve : Compute error rates for different probability thresholds. DetCurveDisplay.from_estimator : Plot DET curve given an estimator and some data. DetCurveDisplay.from_predictions : Plot DET curve given the true and predicted labels. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import det_curve, DetCurveDisplay >>> from sklearn.model_selection import train_test_split >>> from sklearn.svm import SVC >>> X, y = make_classification(n_samples=1000, random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, test_size=0.4, random_state=0) >>> clf = SVC(random_state=0).fit(X_train, y_train) >>> y_pred = clf.decision_function(X_test) >>> fpr, fnr, _ = det_curve(y_test, y_pred) >>> display = DetCurveDisplay( ... fpr=fpr, fnr=fnr, estimator_name="SVC" ... ) >>> display.plot() <...> >>> plt.show() N)estimator_name pos_labelcCs||_||_||_||_dS)Nfprfnrrr)selfr r rrr ^/var/www/html/bwave/python/venv/lib/python3.8/site-packages/sklearn/metrics/_plot/det_curve.py__init__HszDetCurveDisplay.__init__auto) sample_weightresponse_methodrnameaxc Ks:|j||||||d\} }}|jf|| ||||d| S)ai Plot DET curve given an estimator and data. Read more in the :ref:`User Guide `. .. versionadded:: 1.0 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. response_method : {'predict_proba', 'decision_function', 'auto'} default='auto' Specifies whether to use :term:`predict_proba` or :term:`decision_function` as the predicted 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 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 DET 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 Additional keywords arguments passed to matplotlib `plot` function. Returns ------- display : :class:`~sklearn.metrics.DetCurveDisplay` Object that stores computed values. See Also -------- det_curve : Compute error rates for different probability thresholds. DetCurveDisplay.from_predictions : Plot DET curve given the true and predicted labels. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import DetCurveDisplay >>> from sklearn.model_selection import train_test_split >>> from sklearn.svm import SVC >>> X, y = make_classification(n_samples=1000, random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, test_size=0.4, random_state=0) >>> clf = SVC(random_state=0).fit(X_train, y_train) >>> DetCurveDisplay.from_estimator( ... clf, X_test, y_test) <...> >>> plt.show() )rrr)y_truey_predrrrr)Z!_validate_and_get_response_valuesfrom_predictions) clsZ estimatorXyrrrrrkwargsrr r rfrom_estimatorNs$U zDetCurveDisplay.from_estimator)rrrrc KsT|j|||||d\}}t||||d\} } } t| | ||d} | jf||d|S)a+ Plot the DET curve given the true and predicted labels. 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. pos_label : int, float, bool or str, 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 DET 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.DetCurveDisplay` Object that stores computed values. See Also -------- det_curve : Compute error rates for different probability thresholds. DetCurveDisplay.from_estimator : Plot DET curve given an estimator and some data. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import DetCurveDisplay >>> from sklearn.model_selection import train_test_split >>> from sklearn.svm import SVC >>> X, y = make_classification(n_samples=1000, random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, test_size=0.4, random_state=0) >>> clf = SVC(random_state=0).fit(X_train, y_train) >>> y_pred = clf.decision_function(X_test) >>> DetCurveDisplay.from_predictions( ... y_test, y_pred) <...> >>> plt.show() )rrr)rrr rr)Z!_validate_from_predictions_paramsrrplot) rrrrrrrrZpos_label_validatedr r _Zvizr r rrs(K  z DetCurveDisplay.from_predictions)rc Ks:|j||d\|_|_}|dkr$ind|i}|jf||jjtjj|j tjj|j f|\|_ |j dk r~d|j dnd}d|}d|}|jj ||d d|kr|jjd d d d dddddddg }tjj|} dd|D} |j| |j| |jdd|j| |j| |jdd|S)apPlot visualization. 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 DET curve for labeling. If `None`, use `estimator_name` if it is not `None`, otherwise no labeling is shown. **kwargs : dict Additional keywords arguments passed to matplotlib `plot` function. Returns ------- display : :class:`~sklearn.metrics.DetCurveDisplay` Object that stores computed values. rNlabelz (Positive label: )zFalse Positive RatezFalse Negative Rate)xlabelylabelz lower right)locgMbP?g{Gz?g?g?g?g?gffffff?gGz?g+?cSs,g|]$}d|rd|nd|qS)dz{:.0%}z{:.1%}) is_integerformat).0sr r r Asz(DetCurveDisplay.plot..r)Z_validate_plot_paramsZax_Zfigure_updaterspstatsZnormZppfr r Zline_rsetZlegendZ set_xticksZset_xticklabelsZset_xlimZ set_yticksZset_yticklabelsZset_ylim) r rrrZ line_kwargsZinfo_pos_labelr#r$ZticksZtick_locationsZ tick_labelsr r rrs8     zDetCurveDisplay.plot)N) __name__ __module__ __qualname____doc__r classmethodrrrr r r rrs @g^r)Zscipyr.Zutils._plottingrZ_rankingrrr r r rs