o \iK@sDddlmZddlmZmZmZddlmZmZGdddeZ dS) )Counter)"_BinaryClassifierCurveDisplayMixin_despine_validate_style_kwargs)average_precision_scoreprecision_recall_curvec @s~eZdZdZdddddddZ dddddddd Zeddd ddddddd d d ZedddddddddddZdS)PrecisionRecallDisplayaPrecision Recall visualization. It is recommended to use :func:`~sklearn.metrics.PrecisionRecallDisplay.from_estimator` or :func:`~sklearn.metrics.PrecisionRecallDisplay.from_predictions` to create a :class:`~sklearn.metrics.PrecisionRecallDisplay`. 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 ---------- precision : ndarray Precision values. recall : ndarray Recall values. average_precision : float, default=None Average precision. If None, the average precision is not shown. estimator_name : str, default=None Name of estimator. If None, then the estimator name is not shown. pos_label : int, float, bool or str, default=None The class considered as the positive class. If None, the class will not be shown in the legend. .. versionadded:: 0.24 prevalence_pos_label : float, default=None The prevalence of the positive label. It is used for plotting the chance level line. If None, the chance level line will not be plotted even if `plot_chance_level` is set to True when plotting. .. versionadded:: 1.3 Attributes ---------- line_ : matplotlib Artist Precision recall curve. 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 precision recall curve. figure_ : matplotlib Figure Figure containing the curve. See Also -------- precision_recall_curve : Compute precision-recall pairs for different probability thresholds. PrecisionRecallDisplay.from_estimator : Plot Precision Recall Curve given a binary classifier. PrecisionRecallDisplay.from_predictions : Plot Precision Recall Curve using predictions from a binary classifier. Notes ----- The average precision (cf. :func:`~sklearn.metrics.average_precision_score`) in scikit-learn is computed without any interpolation. To be consistent with this metric, the precision-recall curve is plotted without any interpolation as well (step-wise style). You can change this style by passing the keyword argument `drawstyle="default"` in :meth:`plot`, :meth:`from_estimator`, or :meth:`from_predictions`. However, the curve will not be strictly consistent with the reported average precision. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import (precision_recall_curve, ... PrecisionRecallDisplay) >>> 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) >>> clf.fit(X_train, y_train) SVC(random_state=0) >>> predictions = clf.predict(X_test) >>> precision, recall, _ = precision_recall_curve(y_test, predictions) >>> disp = PrecisionRecallDisplay(precision=precision, recall=recall) >>> disp.plot() <...> >>> plt.show() N)average_precisionestimator_name pos_labelprevalence_pos_labelcCs(||_||_||_||_||_||_dSN)r precisionrecallr r r)selfrrr r r rr/var/www/www-root/data/www/176.119.141.140/sports-predictor/venv/lib/python3.10/site-packages/sklearn/metrics/_plot/precision_recall_curve.py__init__rs   zPrecisionRecallDisplay.__init__F)nameplot_chance_levelchance_level_kwdespinecKs~|j||d\|_|_}ddi}|jdur&|dur&|d|jdd|d<n|jdur5d |jd|d<n|dur=||d<t||}|jj|j|jfi|\|_|j dur]d |j dnd } d | } d | } |jj | d| ddd|r|j dur}t dd|j ddddd} |duri}t| |} |jjd|j |j ffi| \|_ nd|_ |rt|jd|vs|r|jjdd|S)a?Plot 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 precision recall curve for labeling. If `None`, use `estimator_name` if not `None`, otherwise no labeling is shown. plot_chance_level : bool, default=False Whether to plot the chance level. The chance level is the prevalence of the positive label computed from the data passed during :meth:`from_estimator` or :meth:`from_predictions` call. .. 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`. Returns ------- display : :class:`~sklearn.metrics.PrecisionRecallDisplay` Object that stores computed values. Notes ----- The average precision (cf. :func:`~sklearn.metrics.average_precision_score`) in scikit-learn is computed without any interpolation. To be consistent with this metric, the precision-recall curve is plotted without any interpolation as well (step-wise style). You can change this style by passing the keyword argument `drawstyle="default"`. However, the curve will not be strictly consistent with the reported average precision. )axr drawstylez steps-postNz (AP = z0.2f)labelzAP = z (Positive label: Recall Precision)g{Gzg)\(?equal)xlabelxlimylabelylimaspectaYou must provide prevalence_pos_label when constructing the PrecisionRecallDisplay object in order to plot the chance level line. Alternatively, you may use PrecisionRecallDisplay.from_estimator or PrecisionRecallDisplay.from_predictions to automatically set prevalence_pos_labelzChance level (AP = kz--)rcolor linestyle)rz lower left)loc)_validate_plot_paramsax_figure_r rplotrrline_r setr ValueError chance_level_rlegend)rrrrrrkwargsdefault_line_kwargs line_kwargsinfo_pos_labelr"r$default_chance_level_line_kwchance_level_line_kwrrrr/s`=        zPrecisionRecallDisplay.plotauto) sample_weightdrop_intermediateresponse_methodr rrrrrc  KsB|j||||||d\}}}|j||f||||| | | | d| S)aPlot precision-recall curve given an estimator and some 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 `. 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=False Whether to drop some suboptimal thresholds which would not appear on a plotted precision-recall curve. This is useful in order to create lighter precision-recall curves. .. versionadded:: 1.3 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 precision and recall metrics. By default, `estimators.classes_[1]` is considered as the positive class. name : str, default=None Name for labeling curve. If `None`, no name is used. ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. plot_chance_level : bool, default=False Whether to plot the chance level. The chance level is the prevalence of the positive label computed from the data passed during :meth:`from_estimator` or :meth:`from_predictions` call. .. 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`. Returns ------- display : :class:`~sklearn.metrics.PrecisionRecallDisplay` See Also -------- PrecisionRecallDisplay.from_predictions : Plot precision-recall curve using estimated probabilities or output of decision function. Notes ----- The average precision (cf. :func:`~sklearn.metrics.average_precision_score`) in scikit-learn is computed without any interpolation. To be consistent with this metric, the precision-recall curve is plotted without any interpolation as well (step-wise style). You can change this style by passing the keyword argument `drawstyle="default"`. However, the curve will not be strictly consistent with the reported average precision. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import PrecisionRecallDisplay >>> from sklearn.model_selection import train_test_split >>> from sklearn.linear_model import LogisticRegression >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> clf = LogisticRegression() >>> clf.fit(X_train, y_train) LogisticRegression() >>> PrecisionRecallDisplay.from_estimator( ... clf, X_test, y_test) <...> >>> plt.show() )r>r r)r<rr r=rrrr)!_validate_and_get_response_valuesfrom_predictions)cls estimatorXyr<r=r>r rrrrrr5y_predrrrfrom_estimators.|   z%PrecisionRecallDisplay.from_estimator)r<r=r rrrrrcKs|j|||||d\}}t|||||d\} } }t||||d}t|}||t|}|| | ||||d}|jd|||| | d| S)aZPlot precision-recall curve given binary class predictions. 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 ---------- y_true : array-like of shape (n_samples,) True binary labels. y_pred : array-like of shape (n_samples,) Estimated probabilities or output of decision function. sample_weight : array-like of shape (n_samples,), default=None Sample weights. drop_intermediate : bool, default=False Whether to drop some suboptimal thresholds which would not appear on a plotted precision-recall curve. This is useful in order to create lighter precision-recall curves. .. versionadded:: 1.3 pos_label : int, float, bool or str, default=None The class considered as the positive class when computing the precision and recall metrics. name : str, default=None Name for labeling curve. 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. plot_chance_level : bool, default=False Whether to plot the chance level. The chance level is the prevalence of the positive label computed from the data passed during :meth:`from_estimator` or :meth:`from_predictions` call. .. 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`. Returns ------- display : :class:`~sklearn.metrics.PrecisionRecallDisplay` See Also -------- PrecisionRecallDisplay.from_estimator : Plot precision-recall curve using an estimator. Notes ----- The average precision (cf. :func:`~sklearn.metrics.average_precision_score`) in scikit-learn is computed without any interpolation. To be consistent with this metric, the precision-recall curve is plotted without any interpolation as well (step-wise style). You can change this style by passing the keyword argument `drawstyle="default"`. However, the curve will not be strictly consistent with the reported average precision. Examples -------- >>> import matplotlib.pyplot as plt >>> from sklearn.datasets import make_classification >>> from sklearn.metrics import PrecisionRecallDisplay >>> from sklearn.model_selection import train_test_split >>> from sklearn.linear_model import LogisticRegression >>> X, y = make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> clf = LogisticRegression() >>> clf.fit(X_train, y_train) LogisticRegression() >>> y_pred = clf.predict_proba(X_test)[:, 1] >>> PrecisionRecallDisplay.from_predictions( ... y_test, y_pred) <...> >>> plt.show() )r<r r)r r<r=)r r<)rrr r r r)rrrrrNr)!_validate_from_predictions_paramsr rrsumvaluesr/)rAy_truerEr<r=r rrrrrr5rr_r class_countrvizrrrr@s@p    z'PrecisionRecallDisplay.from_predictionsr) __name__ __module__ __qualname____doc__rr/ classmethodrFr@rrrrr sJh  r N) collectionsrutils._plottingrrr_rankingrr r rrrrs