# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause from collections import Counter from ...utils._plotting import ( _BinaryClassifierCurveDisplayMixin, _despine, _validate_style_kwargs, ) from .._ranking import average_precision_score, precision_recall_curve class PrecisionRecallDisplay(_BinaryClassifierCurveDisplayMixin): """Precision Recall visualization. It is recommend 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. Read more in the :ref:`User 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() """ def __init__( self, precision, recall, *, average_precision=None, estimator_name=None, pos_label=None, prevalence_pos_label=None, ): self.estimator_name = estimator_name self.precision = precision self.recall = recall self.average_precision = average_precision self.pos_label = pos_label self.prevalence_pos_label = prevalence_pos_label def plot( self, ax=None, *, name=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs, ): """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. """ self.ax_, self.figure_, name = self._validate_plot_params(ax=ax, name=name) default_line_kwargs = {"drawstyle": "steps-post"} if self.average_precision is not None and name is not None: default_line_kwargs["label"] = ( f"{name} (AP = {self.average_precision:0.2f})" ) elif self.average_precision is not None: default_line_kwargs["label"] = f"AP = {self.average_precision:0.2f}" elif name is not None: default_line_kwargs["label"] = name line_kwargs = _validate_style_kwargs(default_line_kwargs, kwargs) (self.line_,) = self.ax_.plot(self.recall, self.precision, **line_kwargs) 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_line_kw = { "label": f"Chance level (AP = {self.prevalence_pos_label:0.2f})", "color": "k", "linestyle": "--", } if chance_level_kw is None: chance_level_kw = {} chance_level_line_kw = _validate_style_kwargs( default_chance_level_line_kw, chance_level_kw ) (self.chance_level_,) = self.ax_.plot( (0, 1), (self.prevalence_pos_label, self.prevalence_pos_label), **chance_level_line_kw, ) else: self.chance_level_ = None if despine: _despine(self.ax_) if "label" in line_kwargs or plot_chance_level: self.ax_.legend(loc="lower left") return self @classmethod def from_estimator( cls, estimator, X, y, *, sample_weight=None, pos_label=None, drop_intermediate=False, response_method="auto", name=None, ax=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs, ): """Plot precision-recall curve given an estimator and some data. 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. 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. 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. 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() """ y_pred, 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_pred, sample_weight=sample_weight, name=name, pos_label=pos_label, drop_intermediate=drop_intermediate, ax=ax, plot_chance_level=plot_chance_level, chance_level_kw=chance_level_kw, despine=despine, **kwargs, ) @classmethod def from_predictions( cls, y_true, y_pred, *, sample_weight=None, pos_label=None, drop_intermediate=False, name=None, ax=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs, ): """Plot precision-recall curve given binary class predictions. 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. pos_label : int, float, bool or str, default=None The class considered as the positive class when computing the precision and recall metrics. 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 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() """ pos_label, name = cls._validate_from_predictions_params( y_true, y_pred, sample_weight=sample_weight, pos_label=pos_label, name=name ) precision, recall, _ = precision_recall_curve( y_true, y_pred, pos_label=pos_label, sample_weight=sample_weight, drop_intermediate=drop_intermediate, ) average_precision = average_precision_score( y_true, y_pred, pos_label=pos_label, sample_weight=sample_weight ) class_count = Counter(y_true) prevalence_pos_label = class_count[pos_label] / sum(class_count.values()) viz = cls( precision=precision, recall=recall, average_precision=average_precision, estimator_name=name, pos_label=pos_label, prevalence_pos_label=prevalence_pos_label, ) return viz.plot( ax=ax, name=name, plot_chance_level=plot_chance_level, chance_level_kw=chance_level_kw, despine=despine, **kwargs, )