rtj3NddlZddlZddlmZddlmZmZGddeZ dS)N) det_curve)"_BinaryClassifierCurveDisplayMixin_deprecate_y_pred_parameterc ~eZdZdZddddZeddddddddZe ddddddd d d Zddd d ZdS)DetCurveDisplayaDetection Error Tradeoff (DET) curve visualization. It is recommended 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. 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:: 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. If not `None`, this value is displayed in the x- and y-axes labels. 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_score = clf.decision_function(X_test) >>> fpr, fnr, _ = det_curve(y_test, y_score) >>> display = DetCurveDisplay( ... fpr=fpr, fnr=fnr, estimator_name="SVC" ... ) >>> display.plot() <...> >>> plt.show() N)estimator_name pos_labelc>||_||_||_||_dSNfprfnrrr )selfr rrr s ]/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/sklearn/metrics/_plot/det_curve.py__init__zDetCurveDisplay.__init__Ss#,"Tauto) sample_weightdrop_intermediateresponse_methodr nameaxc j|||||||\} }}|jd|| |||| |d| S)a& Plot DET curve given an estimator and data. 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 ---------- 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 true positives (tp) do not change from the previous or subsequent threshold. All points with the same tp value have the same `fnr` and thus same y coordinate. .. versionadded:: 1.7 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. By default, `estimators.classes_[1]` is considered as the positive class. 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() )rr r)y_truey_scorerrrrr )!_validate_and_get_response_valuesfrom_predictions) cls estimatorXyrrrr rrkwargsrs rfrom_estimatorzDetCurveDisplay.from_estimatorYs{~$'#H#H  + $I$ $  D$s#  '/      r deprecated)rrr rry_predc t||d}||||||\} }t|||||\} } } || | || }|jd||d| S)a Plot the DET curve given the true and predicted labels. 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 or non-thresholded decision values (as returned by :term:`decision_function` on some classifiers). .. versionadded:: 1.8 `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 true positives (tp) do not change from the previous or subsequent threshold. All points with the same tp value have the same `fnr` and thus same y coordinate. .. versionadded:: 1.7 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. y_pred : 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). .. deprecated:: 1.8 `y_pred` is deprecated and will be removed in 1.10. Use `y_score` instead. **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_score = clf.decision_function(X_test) >>> DetCurveDisplay.from_predictions( ... y_test, y_score) <...> >>> plt.show() z1.8)rr r)r rrr rrr)r!_validate_from_predictions_paramsrplot)rrrrrr rrr&r#pos_label_validatedr r_vizs rrz DetCurveDisplay.from_predictionssF.gvuEE$'$I$I G=ITX%J% % !T  '/    S!c)    sx32D33F333r)rc t|||\|_|_}|ind|i}|jdi|t j|jjj}|j |d|z |_|j |d|z |_ |jj tj j|jtj j|j fi|\|_|j d|jdnd}d|z}d |z}|j|| d|vr|jd gd } tj j| } d| 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. 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