from sklearn.base import is_classifier from .base import _get_response from .. import average_precision_score from .. import precision_recall_curve from .._base import _check_pos_label_consistency from .._classification import check_consistent_length from ...utils import check_matplotlib_support class PrecisionRecallDisplay: """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.PredictionRecallDisplay`. 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 : str or int, default=None The class considered as the positive class. If None, the class will not be shown in the legend. .. versionadded:: 0.24 Attributes ---------- line_ : matplotlib Artist Precision recall curve. 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`) 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, ): self.estimator_name = estimator_name self.precision = precision self.recall = recall self.average_precision = average_precision self.pos_label = pos_label def plot(self, ax=None, *, name=None, **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. **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`) 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. """ check_matplotlib_support("PrecisionRecallDisplay.plot") name = self.estimator_name if name is None else name line_kwargs = {"drawstyle": "steps-post"} if self.average_precision is not None and name is not None: line_kwargs["label"] = f"{name} (AP = {self.average_precision:0.2f})" elif self.average_precision is not None: line_kwargs["label"] = f"AP = {self.average_precision:0.2f}" elif name is not None: line_kwargs["label"] = name line_kwargs.update(**kwargs) import matplotlib.pyplot as plt if ax is None: fig, ax = plt.subplots() (self.line_,) = 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 ax.set(xlabel=xlabel, ylabel=ylabel) if "label" in line_kwargs: ax.legend(loc="lower left") self.ax_ = ax self.figure_ = ax.figure return self @classmethod def from_estimator( cls, estimator, X, y, *, sample_weight=None, pos_label=None, response_method="auto", name=None, ax=None, **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 : str or int, 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. 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. **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`) 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() """ method_name = f"{cls.__name__}.from_estimator" check_matplotlib_support(method_name) if not is_classifier(estimator): raise ValueError(f"{method_name} only supports classifiers") y_pred, pos_label = _get_response( X, estimator, response_method, pos_label=pos_label, ) name = name if name is not None else estimator.__class__.__name__ return cls.from_predictions( y, y_pred, sample_weight=sample_weight, name=name, pos_label=pos_label, ax=ax, **kwargs, ) @classmethod def from_predictions( cls, y_true, y_pred, *, sample_weight=None, pos_label=None, name=None, ax=None, **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 : str or int, 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. **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`) 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() """ check_matplotlib_support(f"{cls.__name__}.from_predictions") check_consistent_length(y_true, y_pred, sample_weight) pos_label = _check_pos_label_consistency(pos_label, y_true) precision, recall, _ = precision_recall_curve( y_true, y_pred, pos_label=pos_label, sample_weight=sample_weight ) average_precision = average_precision_score( y_true, y_pred, pos_label=pos_label, sample_weight=sample_weight ) name = name if name is not None else "Classifier" viz = PrecisionRecallDisplay( precision=precision, recall=recall, average_precision=average_precision, estimator_name=name, pos_label=pos_label, ) return viz.plot(ax=ax, name=name, **kwargs)