import scipy as sp from .base import _get_response from .. import det_curve from ...utils import check_matplotlib_support class DetCurveDisplay: """DET curve visualization. It is recommend to use :func:`~sklearn.metrics.plot_det_curve` to create a visualizer. All parameters are stored as attributes. Read more in the :ref:`User Guide `. .. versionadded:: 0.24 Parameters ---------- fpr : ndarray False positive rate. fnr : ndarray False negative rate. estimator_name : str, default=None Name of estimator. If None, the estimator name is not shown. pos_label : str or int, default=None The label of the positive class. Attributes ---------- line_ : matplotlib Artist DET Curve. ax_ : matplotlib Axes Axes with DET Curve. figure_ : matplotlib Figure Figure containing the curve. See Also -------- det_curve : Compute error rates for different probability thresholds. plot_det_curve : Plot detection error tradeoff (DET) curve. Examples -------- >>> import matplotlib.pyplot as plt # doctest: +SKIP >>> import numpy as np >>> from sklearn import metrics >>> y = np.array([0, 0, 1, 1]) >>> pred = np.array([0.1, 0.4, 0.35, 0.8]) >>> fpr, fnr, thresholds = metrics.det_curve(y, pred) >>> display = metrics.DetCurveDisplay( ... fpr=fpr, fnr=fnr, estimator_name='example estimator' ... ) >>> display.plot() # doctest: +SKIP >>> plt.show() # doctest: +SKIP """ def __init__(self, *, fpr, fnr, estimator_name=None, pos_label=None): self.fpr = fpr self.fnr = fnr self.estimator_name = estimator_name self.pos_label = pos_label def plot(self, ax=None, *, name=None, **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, default=None Name of DET curve for labeling. If `None`, use the name of the estimator. Returns ------- display : :class:`~sklearn.metrics.plot.DetCurveDisplay` Object that stores computed values. """ check_matplotlib_support('DetCurveDisplay.plot') name = self.estimator_name if name is None else name line_kwargs = {} if name is None else {"label": name} line_kwargs.update(**kwargs) import matplotlib.pyplot as plt if ax is None: _, ax = plt.subplots() self.line_, = ax.plot( sp.stats.norm.ppf(self.fpr), sp.stats.norm.ppf(self.fnr), **line_kwargs, ) info_pos_label = (f" (Positive label: {self.pos_label})" if self.pos_label is not None else "") xlabel = "False Positive Rate" + info_pos_label ylabel = "False Negative Rate" + info_pos_label ax.set(xlabel=xlabel, ylabel=ylabel) if "label" in line_kwargs: ax.legend(loc="lower right") ticks = [0.001, 0.01, 0.05, 0.20, 0.5, 0.80, 0.95, 0.99, 0.999] tick_locations = sp.stats.norm.ppf(ticks) tick_labels = [ '{:.0%}'.format(s) if (100*s).is_integer() else '{:.1%}'.format(s) for s in ticks ] ax.set_xticks(tick_locations) ax.set_xticklabels(tick_labels) ax.set_xlim(-3, 3) ax.set_yticks(tick_locations) ax.set_yticklabels(tick_labels) ax.set_ylim(-3, 3) self.ax_ = ax self.figure_ = ax.figure return self def plot_det_curve( estimator, X, y, *, sample_weight=None, response_method="auto", name=None, ax=None, pos_label=None, **kwargs ): """Plot detection error tradeoff (DET) curve. Extra keyword arguments will be passed to matplotlib's `plot`. Read more in the :ref:`User Guide `. .. versionadded:: 0.24 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. response_method : {'predict_proba', 'decision_function', 'auto'} \ default='auto' Specifies whether to use :term:`predict_proba` or :term:`decision_function` as the predicted 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 of DET curve for labeling. If `None`, use the name of the estimator. ax : matplotlib axes, default=None Axes object to plot on. If `None`, a new figure and axes is created. pos_label : str or int, default=None The label of the positive class. 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. Returns ------- display : :class:`~sklearn.metrics.DetCurveDisplay` Object that stores computed values. See Also -------- det_curve : Compute error rates for different probability thresholds. DetCurveDisplay : DET curve visualization. plot_roc_curve : Plot Receiver operating characteristic (ROC) curve. Examples -------- >>> import matplotlib.pyplot as plt # doctest: +SKIP >>> from sklearn import datasets, metrics, model_selection, svm >>> X, y = datasets.make_classification(random_state=0) >>> X_train, X_test, y_train, y_test = model_selection.train_test_split( ... X, y, random_state=0) >>> clf = svm.SVC(random_state=0) >>> clf.fit(X_train, y_train) SVC(random_state=0) >>> metrics.plot_det_curve(clf, X_test, y_test) # doctest: +SKIP >>> plt.show() # doctest: +SKIP """ check_matplotlib_support('plot_det_curve') y_pred, pos_label = _get_response( X, estimator, response_method, pos_label=pos_label ) fpr, fnr, _ = det_curve( y, y_pred, pos_label=pos_label, sample_weight=sample_weight, ) name = estimator.__class__.__name__ if name is None else name viz = DetCurveDisplay( fpr=fpr, fnr=fnr, estimator_name=name, pos_label=pos_label ) return viz.plot(ax=ax, name=name, **kwargs)