8^jrddlZddlmZmZddlmZmZddlm Z m Z m Z m Z m Z mZmZddlmZGdde Zy)N)average_precision_scoreprecision_recall_curve)_safe_indexing check_array)"_BinaryClassifierCurveDisplayMixin_check_param_lengths_convert_to_list_leaving_none_deprecate_estimator_name_deprecate_y_pred_parameter_despine_validate_style_kwargs)_get_response_values_binaryc eZdZdZdddddddZfdZ dddddddd Zeddd dddddddd d Ze dddddddddddd dZ eddd dddddddd dZ xZ S)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 or list of ndarrays Precision values. Each ndarray should contain values for a single curve. If plotting multiple curves, list should be of same length as `recall`. .. versionchanged:: 1.9 Now accepts a list for plotting multiple curves. recall : ndarray or list of ndarrays Recall values. Each ndarray should contain values for a single curve. If plotting multiple curves, list should be of same length as `precision`. .. versionchanged:: 1.9 Now accepts a list for plotting multiple curves. average_precision : float or list of floats, default=None Average precision, used for labeling each curve in the legend. If plotting multiple curves, should be a list of the same length as `precision` and `recall`. If `None`, average precision values are not shown in the legend. .. versionchanged:: 1.9 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`. 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 a list is provided, it will be used to label each curve individually. Passing a list will raise an error if `curve_kwargs` is not a list to avoid labeling individual curves that have the same appearance. If `None`, no name is shown in the legend. .. versionchanged:: 1.8 `estimator_name` was deprecated in favor of `name`. .. versionchanged:: 1.9 `name` can now take a list of str for multiple curves. pos_label : int, float, bool or str, default=None The class considered the positive class when precision and recall metrics computed. If not `None`, this value is displayed in the x- and y-axes labels. .. versionadded:: 0.24 prevalence_pos_label : float or list of floats, default=None The prevalence of the positive label. It is used for plotting the chance level lines. If None, no chance level line will be plotted even if `plot_chance_level` is set to True when plotting. .. versionadded:: 1.3 .. versionchanged:: 1.9 May now be list of floats for when multiple curves plotted. estimator_name : str, default=None Name of estimator. If None, the estimator name is not shown. .. deprecated:: 1.8 `estimator_name` is deprecated and will be removed in 1.10. Use `name` instead. Attributes ---------- line_ : matplotlib Artist or list of Artists Precision recall curve(s). .. versionchanged:: 1.9 This attribute can now be a list of Artists, for when multiple curves are plotted. chance_level_ : matplotlib Artist or list of Artists or None Chance level line(s). It is `None` if the chance level is not plotted. .. versionadded:: 1.3 .. versionchanged:: 1.9 This attribute can now be a list of Artists, for when multiple curves are plotted. 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). To enable interpolation, pass `curve_kwargs={"drawstyle": "default"}` to 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 deprecated)average_precisionname pos_labelprevalence_pos_labelestimator_namecn||_||_||_t||d|_||_||_y)N1.8) precisionrecallrr rrr)selfrrrrrrrs w/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/sklearn/metrics/_plot/precision_recall_curve.py__init__zPrecisionRecallDisplay.__init__s9# !2-ndEJ "$8!ct|||\|_|_}t |j }t |j }t |j}t |j}t |}||d}t|tr!t|dk7r|jd|it||d|d|||||fS)Naxr)zself.average_precisionzself.prevalence_pos_labelz'name' (or self.name))zself.precisionz self.recallr)requiredoptional class_name)super_validate_plot_paramsax_figure_r rrrr isinstancelistlenupdater) rr!rrrrrr$ __class__s rr'z,PrecisionRecallDisplay._validate_plot_paramss',w'DQU'D'V$$,1$..A .t{{;9$:P:PQ+/+B+B ($5, (=3t22      &"-|!<!( #   69 I|7  W 2J { JJ  mdhhmmJ UU V W tzz?a ADJ7;nn6P  0 2VX N*~-     ((0 @!+ '!|7:+G4&"$4+_O"$D 2  ""))!DHHMM#Z0* o- 1}**>q*A$)GqI.rww7K/LT.RS66"67=Q@""1%//61}%)%7%7%:"!%D   TXX  ?  w ' 3 /"5"5g">"J HHOO O - rauto) sample_weightdrop_intermediateresponse_methodrrr!r/r0r1r2c t|j||||||\}}}|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. curve_kwargs : dict, default=None Keywords arguments to be passed to matplotlib's `plot` function. .. versionadded:: 1.9 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`. .. deprecated:: 1.9 kwargs is deprecated and will be removed in 1.11. Pass matplotlib arguments to `curve_kwargs` as a dictionary instead. 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). To enable interpolation, pass `curve_kwargs={"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() )rerr) rcrdrrr!r/r0r1r2)!_validate_and_get_response_valuesfrom_predictions)cls estimatorXyrcrdrerrr!r/r0r1r2rYy_scores rfrom_estimatorz%PrecisionRecallDisplay.from_estimators}L$'#H#H  + $I$  D$s##   (/%/+    r) rcrdrrr!r/r0r1r2y_predc <t|dd}t|| d}|j|||||\}}t|||||\}}}t ||||}||k(j t |z }|||||||}|jd |||| | | d | S) aPlot 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_score : array-like of shape (n_samples,) Estimated probabilities or output of decision function. .. 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=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. 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 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. curve_kwargs : dict, default=None Keywords arguments to be passed to matplotlib's `plot` function. .. versionadded:: 1.9 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 y_pred : array-like of shape (n_samples,) Estimated probabilities or output of decision function. .. deprecated:: 1.8 `y_pred` is deprecated and will be removed in 1.10. Use `y_score` instead. **kwargs : dict Keyword arguments to be passed to matplotlib's `plot`. .. deprecated:: 1.9 kwargs is deprecated and will be removed in 1.11. Pass matplotlib arguments to `curve_kwargs` as a dictionary instead. 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). To enable interpolation, pass `curve_kwargs={"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_score = clf.predict_proba(X_test)[:, 1] >>> PrecisionRecallDisplay.from_predictions( ... y_test, y_score) <...> >>> plt.show() FN) ensure_2ddtyper)rcrrrrcrdrrcrrrrrr)r!rr/r0r1r2)rr !_validate_from_predictions_paramsrrsumr,rO)riy_truermrcrdrrr!r/r0r1r2rorYrr_rrvizs rrhz'PrecisionRecallDisplay.from_predictions3sPVuDA-gvuE?? G=ITX@  4 6  '/   614 Gy  !') 388:S[H/!5  sxx %/+    rT) rcrdrerrr!r/r0chance_level_kwargsr2c L|j||||gg}}gg}}t|d|ddD]\}}t||}t|t||||\}}|dn t||}t |||||\}}}t ||||}t j||k(|jd z }|j||j||j||j||||||| }|j| | | | | S) adPlot multi-fold precision-recall curves given cross-validation results. .. versionadded:: 1.9 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 precision-recall curve. This is useful in order to create lighter precision-recall 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 precision and recall metrics. By default, `estimators.classes_[1]` is considered as the positive class. 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`. 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 a list is provided, it will be used to label each curve individually. Passing a list will raise an error if `curve_kwargs` is not a list to avoid labeling individual curves that have the same appearance. If `None`, no name is shown in the legend. ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. curve_kwargs : dict or list of dict, default=None Dictionary with keywords passed to the matplotlib's `plot` function to draw the individual precision-recall curves. If a list is provided, the parameters are applied to the precision-recall curves of each CV fold sequentially. If a single dictionary is provided, the same parameters are applied to all precision-recall curves. plot_chance_level : bool, default=False Whether to plot the chance level lines. chance_level_kwargs : dict, default=None Keyword arguments to be passed to matplotlib's `plot` for rendering the chance level lines. despine : bool, default=False Whether to remove the top and right spines from the plot. Returns ------- display : :class:`~sklearn.metrics.PrecisionRecallDisplay` See Also -------- PrecisionRecallDisplay.from_predictions : Plot precision-recall curve using estimated probabilities or output of decision function. PrecisionRecallDisplay.from_estimator : Plot precision-recall curve using an estimator. precision_recall_curve : Compute precision-recall pairs for different probability thresholds. average_precision_score : Compute average precision (AP) from prediction scores. 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). To enable interpolation, pass `curve_kwargs={"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 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) >>> PrecisionRecallDisplay.from_cv_results(cv_results, X, y) <...> >>> plt.show() )rcrjindicestest)rerNrsrtrru)r!r/r0r1r2) _validate_from_cv_results_paramsrMrrrrrS count_nonzeroshapeappendrO)ri cv_resultsrkrlrcrdrerrr!r/r0r|r2precision_folds recall_foldsap_foldsprevalence_pos_label_foldsrj test_indicesryro pos_label_sample_weight_foldrrrzrrr{s rfrom_cv_resultsz&PrecisionRecallDisplay.from_cv_resultssD ,, 1M - )+B/12,'* { #Z %:6%B( # D #I|$A|4F!<q,/ /# " FJ!(#M<@  $:$0"3 $ Ivq!8*DV!   :!56aH !  " "9 -    ' OO- . & - -.B CG# DJ%& !;  xx%//   r)N) __name__ __module__ __qualname____doc__rr'rO classmethodrnrhr __classcell__)r.s@rrrsQp !#9$P0 CCJ  [ [ zk   k k Z   { { rr)numpyrSsklearn.metrics._rankingrr sklearn.utilsrrsklearn.utils._plottingrrr r r r r sklearn.utils._responserrrvrrrs4T5@I ?I r