# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import numpy as np from sklearn.metrics._ranking import average_precision_score, precision_recall_curve from sklearn.utils import _safe_indexing, check_array from sklearn.utils._plotting import ( _BinaryClassifierCurveDisplayMixin, _check_param_lengths, _convert_to_list_leaving_none, _deprecate_estimator_name, _deprecate_y_pred_parameter, _despine, _validate_style_kwargs, ) from sklearn.utils._response import _get_response_values_binary class PrecisionRecallDisplay(_BinaryClassifierCurveDisplayMixin): """Precision 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() """ def __init__( self, precision, recall, *, average_precision=None, name=None, pos_label=None, prevalence_pos_label=None, estimator_name="deprecated", ): self.precision = precision self.recall = recall self.average_precision = average_precision self.name = _deprecate_estimator_name(estimator_name, name, "1.8") self.pos_label = pos_label self.prevalence_pos_label = prevalence_pos_label def _validate_plot_params(self, *, ax, name): self.ax_, self.figure_, name = super()._validate_plot_params(ax=ax, name=name) precision = _convert_to_list_leaving_none(self.precision) recall = _convert_to_list_leaving_none(self.recall) average_precision = _convert_to_list_leaving_none(self.average_precision) prevalence_pos_label = _convert_to_list_leaving_none(self.prevalence_pos_label) name = _convert_to_list_leaving_none(name) optional = { "self.average_precision": average_precision, "self.prevalence_pos_label": prevalence_pos_label, } if isinstance(name, list) and len(name) != 1: optional.update({"'name' (or self.name)": name}) _check_param_lengths( required={"self.precision": precision, "self.recall": recall}, optional=optional, class_name="PrecisionRecallDisplay", ) return precision, recall, average_precision, name, prevalence_pos_label def plot( self, ax=None, *, name=None, curve_kwargs=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs, ): """Plot visualization. Parameters ---------- ax : Matplotlib Axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. 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`, set to `name` provided at `PrecisionRecallDisplay` initialization. If still `None`, no name is shown in the legend. .. versionchanged:: 1.9 Now accepts a list for plotting multiple curves. curve_kwargs : dict or list of dict, default=None Keywords arguments to be passed to matplotlib's `plot` function to draw individual precision-recall curves. For single curve plotting, this should be a dictionary. For multi-curve plotting, if a list is provided, the parameters are applied to each precision-recall curve sequentially and a legend entry is added for each curve. If a single dictionary is provided, the same parameters are applied to all curves and a single legend entry for all curves is added, labeled with the mean average precision. .. 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` 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). To enable interpolation, pass `curve_kwargs={"drawstyle": "default"}`. However, the curve will not be strictly consistent with the reported average precision. """ precision, recall, average_precision, name, prevalence_pos_label = ( self._validate_plot_params(ax=ax, name=name) ) n_curves = len(precision) average_precision, legend_metric = self._get_legend_metric( curve_kwargs, n_curves, average_precision ) curve_kwargs = self._validate_curve_kwargs( n_curves, name, legend_metric, "AP", curve_kwargs=curve_kwargs, default_curve_kwargs={"drawstyle": "steps-post"}, default_multi_curve_kwargs={ "alpha": 0.5, "linestyle": "--", "color": "blue", }, removed_version="1.11", **kwargs, ) self.line_ = [] for recall_val, precision_val, curve_kwarg in zip( recall, precision, curve_kwargs ): self.line_.extend(self.ax_.plot(recall_val, precision_val, **curve_kwarg)) # Return single artist if only one curve is plotted if len(self.line_) == 1: self.line_ = self.line_[0] info_pos_label = ( f" (Positive label: {self.pos_label})" if self.pos_label is not None else "" ) xlabel = "Recall" + info_pos_label ylabel = "Precision" + info_pos_label self.ax_.set( xlabel=xlabel, xlim=(-0.01, 1.01), ylabel=ylabel, ylim=(-0.01, 1.01), aspect="equal", ) if plot_chance_level: if self.prevalence_pos_label is None: raise ValueError( "You 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_label" ) default_chance_level_kwargs = { "color": "k", "linestyle": "--", } if n_curves > 1: default_chance_level_kwargs["alpha"] = 0.3 if chance_level_kw is None: chance_level_kw = {} chance_level_kw = _validate_style_kwargs( default_chance_level_kwargs, chance_level_kw ) self.chance_level_ = [] for prevalence in prevalence_pos_label: self.chance_level_.extend( self.ax_.plot( (0, 1), (prevalence, prevalence), **chance_level_kw, ) ) if "label" not in chance_level_kw: label = ( f"Chance level (AP = {prevalence_pos_label[0]:0.2f})" if n_curves == 1 else f"Chance level (AP = {np.mean(prevalence_pos_label):0.2f} " f"+/- {np.std(prevalence_pos_label):0.2f})" ) # Only label first curve with mean AP, to get single legend entry self.chance_level_[0].set_label(label) if n_curves == 1: # Return single artist if only one curve is plotted self.chance_level_ = self.chance_level_[0] else: self.chance_level_ = None if despine: _despine(self.ax_) # Note: if 'label' present in one `line_kwargs`, it should be present in all if curve_kwargs[0].get("label") is not None or ( plot_chance_level and chance_level_kw.get("label") is not None ): self.ax_.legend(loc="lower left") return self @classmethod def from_estimator( cls, estimator, X, y, *, sample_weight=None, drop_intermediate=False, response_method="auto", pos_label=None, name=None, ax=None, curve_kwargs=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs, ): """Plot 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() """ y_score, pos_label, name = cls._validate_and_get_response_values( estimator, X, y, response_method=response_method, pos_label=pos_label, name=name, ) return cls.from_predictions( y, y_score, sample_weight=sample_weight, drop_intermediate=drop_intermediate, pos_label=pos_label, name=name, ax=ax, curve_kwargs=curve_kwargs, plot_chance_level=plot_chance_level, chance_level_kw=chance_level_kw, despine=despine, **kwargs, ) @classmethod def from_predictions( cls, y_true, y_score=None, *, sample_weight=None, drop_intermediate=False, pos_label=None, name=None, ax=None, curve_kwargs=None, plot_chance_level=False, chance_level_kw=None, despine=False, y_pred="deprecated", **kwargs, ): """Plot 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() """ y_true = check_array(y_true, ensure_2d=False, dtype=None) y_score = _deprecate_y_pred_parameter(y_score, y_pred, "1.8") pos_label, name = cls._validate_from_predictions_params( y_true, y_score, sample_weight=sample_weight, pos_label=pos_label, name=name ) precision, recall, _ = precision_recall_curve( y_true, y_score, pos_label=pos_label, sample_weight=sample_weight, drop_intermediate=drop_intermediate, ) average_precision = average_precision_score( y_true, y_score, pos_label=pos_label, sample_weight=sample_weight ) prevalence_pos_label = (y_true == pos_label).sum() / len(y_true) viz = cls( precision=precision, recall=recall, average_precision=average_precision, name=name, pos_label=pos_label, prevalence_pos_label=prevalence_pos_label, ) return viz.plot( ax=ax, name=name, curve_kwargs=curve_kwargs, plot_chance_level=plot_chance_level, chance_level_kw=chance_level_kw, despine=despine, **kwargs, ) @classmethod def from_cv_results( cls, cv_results, X, y, *, sample_weight=None, drop_intermediate=True, response_method="auto", pos_label=None, name=None, ax=None, curve_kwargs=None, plot_chance_level=False, chance_level_kwargs=None, despine=False, ): """Plot 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() """ cls._validate_from_cv_results_params( cv_results, X, y, sample_weight=sample_weight ) precision_folds, recall_folds = [], [] ap_folds, prevalence_pos_label_folds = [], [] for estimator, test_indices in zip( cv_results["estimator"], cv_results["indices"]["test"] ): y_true = _safe_indexing(y, test_indices) y_pred, pos_label_ = _get_response_values_binary( estimator, _safe_indexing(X, test_indices), response_method=response_method, pos_label=pos_label, ) sample_weight_fold = ( None if sample_weight is None else _safe_indexing(sample_weight, test_indices) ) precision, recall, _ = precision_recall_curve( y_true, y_pred, pos_label=pos_label_, sample_weight=sample_weight_fold, drop_intermediate=drop_intermediate, ) # `average_precision_score` is only metric where default `pos_label=1`, # thus `pos_label` cannot be None and we use `pos_label_` from # `_get_response_values_binary` average_precision = average_precision_score( y_true, y_pred, pos_label=pos_label_, sample_weight=sample_weight_fold ) prevalence_pos_label = ( np.count_nonzero(y_true == pos_label_) / y_true.shape[0] ) precision_folds.append(precision) recall_folds.append(recall) ap_folds.append(average_precision) prevalence_pos_label_folds.append(prevalence_pos_label) viz = cls( precision=precision_folds, recall=recall_folds, average_precision=ap_folds, name=name, pos_label=pos_label_, prevalence_pos_label=prevalence_pos_label_folds, ) return viz.plot( ax=ax, curve_kwargs=curve_kwargs, plot_chance_level=plot_chance_level, chance_level_kw=chance_level_kwargs, despine=despine, )